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1.  Assessing Optimal Target Populations for Influenza Vaccination Programmes: An Evidence Synthesis and Modelling Study 
PLoS Medicine  2013;10(10):e1001527.
Marc Baguelin and colleagues use virological, clinical, epidemiological, and behavioral data to estimate how policies for influenza vaccination programs may be optimized in England and Wales.
Please see later in the article for the Editors' Summary
Background
Influenza vaccine policies that maximise health benefit through efficient use of limited resources are needed. Generally, influenza vaccination programmes have targeted individuals 65 y and over and those at risk, according to World Health Organization recommendations. We developed methods to synthesise the multiplicity of surveillance datasets in order to evaluate how changing target populations in the seasonal vaccination programme would affect infection rate and mortality.
Methods and Findings
Using a contemporary evidence-synthesis approach, we use virological, clinical, epidemiological, and behavioural data to develop an age- and risk-stratified transmission model that reproduces the strain-specific behaviour of influenza over 14 seasons in England and Wales, having accounted for the vaccination uptake over this period. We estimate the reduction in infections and deaths achieved by the historical programme compared with no vaccination, and the reduction had different policies been in place over the period. We find that the current programme has averted 0.39 (95% credible interval 0.34–0.45) infections per dose of vaccine and 1.74 (1.16–3.02) deaths per 1,000 doses. Targeting transmitters by extending the current programme to 5–16-y-old children would increase the efficiency of the total programme, resulting in an overall reduction of 0.70 (0.52–0.81) infections per dose and 1.95 (1.28–3.39) deaths per 1,000 doses. In comparison, choosing the next group most at risk (50–64-y-olds) would prevent only 0.43 (0.35–0.52) infections per dose and 1.77 (1.15–3.14) deaths per 1,000 doses.
Conclusions
This study proposes a framework to integrate influenza surveillance data into transmission models. Application to data from England and Wales confirms the role of children as key infection spreaders. The most efficient use of vaccine to reduce overall influenza morbidity and mortality is thus to target children in addition to older adults.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every winter, millions of people catch influenza, a viral infection of the airways. Most infected individuals recover quickly, but seasonal influenza outbreaks (epidemics) kill about half a million people annually. In countries with advanced health systems, these deaths occur mainly among elderly people and among individuals with long-term illnesses such as asthma and heart disease that increase the risk of complications occurring after influenza virus infection. Epidemics of influenza occur because small but frequent changes in the influenza virus mean that an immune response produced one year through infection provides only partial protection against influenza the following year. Annual immunization with a vaccine that contains killed influenza viruses of the major circulating strains can greatly reduce a person's risk of catching influenza by preparing the immune system to respond quickly when challenged by a live influenza virus. Consequently, many countries run seasonal influenza vaccination programs that, in line with World Health Organization recommendations, target individuals 65 years old and older and people in high-risk groups.
Why Was This Study Done?
Is this approach the best use of available resources? Might, for example, vaccination of children—the main transmitters of influenza—provide more benefit to the whole population than vaccination of elderly people? Vaccination of children would not directly prevent as many influenza-related deaths as vaccination of elderly people, but it might indirectly prevent deaths in elderly adults by inducing herd immunity—vaccination of a large part of a population can protect unvaccinated members of the population by reducing the chances of an infection spreading. Policy makers need to know whether a change to an influenza vaccination program is likely to provide additional population benefits before altering the program. In this evidence synthesis and modeling study, the researchers combine (synthesize) longitudinal influenza surveillance datasets (data collected over time) from England and Wales, develop a mathematical model for influenza transmission based on these data using a Bayesian statistical approach, and use the model to evaluate the impact on influenza infections and deaths of changes to the seasonal influenza vaccination program in England and Wales.
What Did the Researchers Do and Find?
The researchers developed an influenza transmission model using clinical data on influenza-like illness consultations collected in a primary care surveillance scheme for each week of 14 influenza seasons in England and Wales, virological information on respiratory viruses detected in a subset of patients presenting with clinically suspected influenza, and data on vaccination coverage in the whole population (epidemiological data). They also incorporated data on social contacts (behavioral data) and on immunity to influenza viruses in the population (seroepidemiological data) into their model. To estimate the impact of potential changes to the current vaccination strategy in England and Wales, the researchers used their model, which replicated the patterns of disease observed in the surveillance data, to run simulated epidemics for each influenza season and for three strains of influenza virus under various vaccination scenarios. Compared to no vaccination, the current program (vaccination of people 65 years old and older and people in high-risk groups) averted 0.39 infections per dose of vaccine and 1.74 deaths per 1,000 doses. Notably, the model predicted that extension of the program to target 5–16-year-old children would increase the efficiency of the program and would avert 0.70 infections per dose and 1.95 deaths per 1,000 doses.
What Do These Findings Mean?
The finding that the transmission model developed by the researchers closely fit the available surveillance data suggests that the model should be able to predict what would have happened in England and Wales over the study period if an alternative vaccination regimen had been in place. The accuracy of such predictions may be limited, however, because the vaccination model is based on a series of simplifying assumptions. Importantly, given that influenza vaccination for children is being rolled out in England and Wales from September 2013, the model confirms that children are key spreaders of influenza and suggests that a vaccination program targeting children will reduce influenza infections and potentially influenza deaths in the whole population. More generally, the findings of this study support wider adoption of national vaccination strategies designed to block influenza transmission and to target those individuals most at risk from the complications of influenza infection.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371.journal.pmed.1001527.
The UK National Health Service Choices website provides information for patients about seasonal influenza and about vaccination; Public Health England (formerly the Health Protection Agency) provides information on influenza surveillance in the UK, including information about the primary care surveillance database used in this study
The World Health Organization provides information on seasonal influenza (in several languages)
The European Influenzanet is a system to monitor the activity of influenza-like illness with the aid of volunteers via the Internet
The US Centers for Disease Control and Prevention also provides information for patients and health professionals on all aspects of seasonal influenza, including information about vaccination and about the US influenza surveillance system; its website contains a short video about personal experiences of influenza
Flu.gov, a US government website, provides access to information on seasonal influenza and vaccination
MedlinePlus has links to further information about influenza and about immunization (in English and Spanish)
doi:10.1371/journal.pmed.1001527
PMCID: PMC3793005  PMID: 24115913
2.  Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data 
PLoS Medicine  2013;10(11):e1001552.
Cécile Viboud and colleagues describe epidemiological patterns of influenza incidence across China to support the design of a national vaccination program.
Please see later in the article for the Editors' Summary
Background
The complexity of influenza seasonal patterns in the inter-tropical zone impedes the establishment of effective routine immunization programs. China is a climatologically and economically diverse country, which has yet to establish a national influenza vaccination program. Here we characterize the diversity of influenza seasonality in China and make recommendations to guide future vaccination programs.
Methods and Findings
We compiled weekly reports of laboratory-confirmed influenza A and B infections from sentinel hospitals in cities representing 30 Chinese provinces, 2005–2011, and data on population demographics, mobility patterns, socio-economic, and climate factors. We applied linear regression models with harmonic terms to estimate influenza seasonal characteristics, including the amplitude of annual and semi-annual periodicities, their ratio, and peak timing. Hierarchical Bayesian modeling and hierarchical clustering were used to identify predictors of influenza seasonal characteristics and define epidemiologically-relevant regions. The annual periodicity of influenza A epidemics increased with latitude (mean amplitude of annual cycle standardized by mean incidence, 140% [95% CI 128%–151%] in the north versus 37% [95% CI 27%–47%] in the south, p<0.0001). Epidemics peaked in January–February in Northern China (latitude ≥33°N) and April–June in southernmost regions (latitude <27°N). Provinces at intermediate latitudes experienced dominant semi-annual influenza A periodicity with peaks in January–February and June–August (periodicity ratio >0.6 in provinces located within 27.4°N–31.3°N, slope of latitudinal gradient with latitude −0.016 [95% CI −0.025 to −0.008], p<0.001). In contrast, influenza B activity predominated in colder months throughout most of China. Climate factors were the strongest predictors of influenza seasonality, including minimum temperature, hours of sunshine, and maximum rainfall. Our main study limitations include a short surveillance period and sparse influenza sampling in some of the southern provinces.
Conclusions
Regional-specific influenza vaccination strategies would be optimal in China; in particular, annual campaigns should be initiated 4–6 months apart in Northern and Southern China. Influenza surveillance should be strengthened in mid-latitude provinces, given the complexity of seasonal patterns in this region. More broadly, our findings are consistent with the role of climatic factors on influenza transmission dynamics.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every year, millions of people worldwide catch influenza, a viral disease of the airways. Most infected individuals recover quickly but seasonal influenza outbreaks (epidemics) kill about half a million people annually. These epidemics occur because antigenic drift—frequent small changes in the viral proteins to which the immune system responds—means that an immune response produced one year provides only partial protection against influenza the next year. Annual vaccination with a mixture of killed influenza viruses of the major circulating strains boosts this natural immunity and greatly reduces the risk of catching influenza. Consequently, many countries run seasonal influenza vaccination programs. Because the immune response induced by vaccination decays within 4–8 months of vaccination and because of antigenic drift, it is important that these programs are initiated only a few weeks before the onset of local influenza activity. Thus, vaccination starts in early autumn in temperate zones (regions of the world that have a mild climate, part way between a tropical and a polar climate), because seasonal influenza outbreaks occur in the winter months when low humidity and low temperatures favor the transmission of the influenza virus.
Why Was This Study Done?
Unlike temperate regions, seasonal influenza patterns are very diverse in tropical countries, which lie between latitudes 23.5°N and 23.5°S, and in the subtropical countries slightly north and south of these latitudes. In some of these countries, there is year-round influenza activity, in others influenza epidemics occur annually or semi-annually (twice yearly). This complexity, which is perhaps driven by rainfall fluctuations, complicates the establishment of effective routine immunization programs in tropical and subtropical countries. Take China as an example. Before a national influenza vaccination program can be established in this large, climatologically diverse country, public-health experts need a clear picture of influenza seasonality across the country. Here, the researchers use spatio-temporal modeling of influenza surveillance data to characterize the seasonality of influenza A and B (the two types of influenza that usually cause epidemics) in China, to assess the role of putative drivers of seasonality, and to identify broad epidemiological regions (areas with specific patterns of disease) that could be used as a basis to optimize the timing of future Chinese vaccination programs.
What Did the Researchers Do and Find?
The researchers collected together the weekly reports of laboratory-confirmed influenza prepared by the Chinese national sentinel hospital-based surveillance network between 2005 and 2011, data on population size and density, mobility patterns, and socio-economic factors, and daily meteorological data for the cities participating in the surveillance network. They then used various statistical modeling approaches to estimate influenza seasonal characteristics, to assess predictors of influenza seasonal characteristics, and to identify epidemiologically relevant regions. These analyses indicate that, over the study period, northern provinces (latitudes greater than 33°N) experienced winter epidemics of influenza A in January–February, southern provinces (latitudes less than 27°N) experienced peak viral activity in the spring (April–June), and provinces at intermediate latitudes experienced semi-annual epidemic cycles with infection peaks in January–February and June–August. By contrast, influenza B activity predominated in the colder months throughout China. The researchers also report that minimum temperatures, hours of sunshine, and maximum rainfall were the strongest predictors of influenza seasonality.
What Do These Findings Mean?
These findings show that influenza seasonality in China varies between regions and between influenza virus types and suggest that, as in other settings, some of these variations might be associated with specific climatic factors. The accuracy of these findings is limited by the short surveillance period, by sparse surveillance data from some southern and mid-latitude provinces, and by some aspects of the modeling approach used in the study. Further surveillance studies need to be undertaken to confirm influenza seasonality patterns in China. Overall, these findings suggest that, to optimize routine influenza vaccination in China, it will be necessary to stagger the timing of vaccination over three broad geographical regions. More generally, given that there is growing interest in rolling out national influenza immunization programs in low- and middle-income countries, these findings highlight the importance of ensuring that vaccination strategies are optimized by taking into account local disease patterns.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/ 10.1371/journal.pmed.1001552.
This study is further discussed in a PLOS Medicine Perspective by Steven Riley
The UK National Health Service Choices website provides information for patients about seasonal influenza and about influenza vaccination
The World Health Organization provides information on seasonal influenza (in several languages) and on influenza surveillance and monitoring
The US Centers for Disease Control and Prevention also provides information for patients and health professionals on all aspects of seasonal influenza, including information about vaccination; its website contains a short video about personal experiences of influenza.
Flu.gov, a US government website, provides access to information on seasonal influenza and vaccination
Information about the Chinese National Influenza Center, which is part of the Chinese Center for Disease Control and Prevention: and which runs influenza surveillance in China, is available (in English and Chinese)
MedlinePlus has links to further information about influenza and about vaccination (in English and Spanish)
A recent PLOS Pathogens Research Article by James D. Tamerius et al. investigates environmental predictors of seasonal influenza epidemics across temperate and tropical climates
A study published in PLOS ONE by Wyller Alencar de Mello et al. indicates that Brazil, like China, requires staggered timing of vaccination from Northern to Southern states to account for different timings of influenza activity.
doi:10.1371/journal.pmed.1001552
PMCID: PMC3864611  PMID: 24348203
3.  Estimates of Pandemic Influenza Vaccine Effectiveness in Europe, 2009–2010: Results of Influenza Monitoring Vaccine Effectiveness in Europe (I-MOVE) Multicentre Case-Control Study 
PLoS Medicine  2011;8(1):e1000388.
Results from a European multicentre case-control study reported by Marta Valenciano and colleagues suggest good protection by the pandemic monovalent H1N1 vaccine against pH1N1 and no effect of the 2009–2010 seasonal influenza vaccine on H1N1.
Background
A multicentre case-control study based on sentinel practitioner surveillance networks from seven European countries was undertaken to estimate the effectiveness of 2009–2010 pandemic and seasonal influenza vaccines against medically attended influenza-like illness (ILI) laboratory-confirmed as pandemic influenza A (H1N1) (pH1N1).
Methods and Findings
Sentinel practitioners swabbed ILI patients using systematic sampling. We included in the study patients meeting the European ILI case definition with onset of symptoms >14 days after the start of national pandemic vaccination campaigns. We compared pH1N1 cases to influenza laboratory-negative controls. A valid vaccination corresponded to >14 days between receiving a dose of vaccine and symptom onset. We estimated pooled vaccine effectiveness (VE) as 1 minus the odds ratio with the study site as a fixed effect. Using logistic regression, we adjusted VE for potential confounding factors (age group, sex, month of onset, chronic diseases and related hospitalizations, smoking history, seasonal influenza vaccinations, practitioner visits in previous year). We conducted a complete case analysis excluding individuals with missing values and a multiple multivariate imputation to estimate missing values. The multivariate imputation (n = 2902) adjusted pandemic VE (PIVE) estimates were 71.9% (95% confidence interval [CI] 45.6–85.5) overall; 78.4% (95% CI 54.4–89.8) in patients <65 years; and 72.9% (95% CI 39.8–87.8) in individuals without chronic disease. The complete case (n = 1,502) adjusted PIVE were 66.0% (95% CI 23.9–84.8), 71.3% (95% CI 29.1–88.4), and 70.2% (95% CI 19.4–89.0), respectively. The adjusted PIVE was 66.0% (95% CI −69.9 to 93.2) if vaccinated 8–14 days before ILI onset. The adjusted 2009–2010 seasonal influenza VE was 9.9% (95% CI −65.2 to 50.9).
Conclusions
Our results suggest good protection of the pandemic monovalent vaccine against medically attended pH1N1 and no effect of the 2009–2010 seasonal influenza vaccine. However, the late availability of the pandemic vaccine and subsequent limited coverage with this vaccine hampered our ability to study vaccine benefits during the outbreak period. Future studies should include estimation of the effectiveness of the new trivalent vaccine in the upcoming 2010–2011 season, when vaccination will occur before the influenza season starts.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Following the World Health Organization's declaration of pandemic phase six in June 2009, manufacturers developed vaccines against pandemic influenza A 2009 (pH1N1). On the basis of the scientific opinion of the European Medicines Agency, the European Commission initially granted marketing authorization to three pandemic vaccines for use in European countries. During the autumn of 2009, most European countries included the 2009–2010 seasonal influenza vaccine and the pandemic vaccine in their influenza vaccination programs.
The Influenza Monitoring Vaccine Effectiveness in Europe network (established to monitor seasonal and pandemic influenza vaccine effectiveness) conducted seven case-control and three cohort studies in seven European countries in 2009–2010 to estimate the effectiveness of the pandemic and seasonal vaccines. Data from the seven pilot case-control studies were pooled to provide overall adjusted estimates of vaccine effectiveness.
Why Was This Study Done?
After seasonal and pandemic vaccines are made available to populations, it is necessary to estimate the effectiveness of the vaccines at the population level during every influenza season. Therefore, this study was conducted in European countries to estimate the pandemic influenza vaccine effectiveness and seasonal influenza vaccine effectiveness against people presenting to their doctor with influenza-like illness who were confirmed (by laboratory tests) to be infected with pH1N1.
What Did the Researchers Do and Find?
The researchers conducted a multicenter case-control study on the basis of practitioner surveillance networks from seven countries—France, Hungary, Ireland, Italy, Romania, Portugal, and Spain. Patients consulting a participating practitioner for influenza-like illness had a nasal or throat swab taken within 8 days of symptom onset. Cases were swabbed patients who tested positive for pH1N1. Patients presenting with influenza-like illness whose swab tested negative for any influenza virus were controls.
Individuals were considered vaccinated if they had received a dose of the vaccine more than 14 days before the date of onset of influenza-like illness and unvaccinated if they were not vaccinated at all, or if the vaccine was given less than 15 days before the onset of symptoms. The researchers analyzed pandemic influenza vaccination effectiveness in those vaccinated less than 8 days, those vaccinated between and including 8 and 14 days, and those vaccinated more than 14 days before onset of symptoms compared to those who had never been vaccinated.
The researchers used modeling (taking account of all potential confounding factors) to estimate adjusted vaccine effectiveness and stratified the adjusted pandemic influenza vaccine effectiveness and the adjusted seasonal influenza vaccine effectiveness in three age groups (<15, 15–64, and ≥65 years of age).
The adjusted results suggest that the 2009–2010 seasonal influenza vaccine did not protect against pH1N1 illness. However, one dose of the pandemic vaccines used in the participating countries conferred good protection (65.5%–100% according to various stratifications performed) against pH1N1 in people who attended their practitioner with influenza-like illness, especially in people aged <65 years and in those without any chronic disease. Furthermore, good pandemic influenza vaccine effectiveness was observed as early as 8 days after vaccination.
What Do These Findings Mean?
The results of this study provide early estimates of the pandemic influenza vaccine effectiveness suggesting that the monovalent pandemic vaccines have been effective. The findings also give an indication of the vaccine effectiveness for the Influenza A (H1N1) 2009 strain included in the 2010–2011 seasonal vaccines, although specific vaccine effectiveness studies will have to be conducted to verify if similar good effectiveness are observed with 2010–2011 trivalent vaccines. However, the results of this study should be interpreted with caution because of limitations in the pandemic context (late timing of the studies, low incidence, low vaccine coverage leading to imprecise estimates) and potential biases due the study design, confounding factors, and missing values. The researchers recommend that in future season studies, the sample size per country should be enlarged in order to allow for precise pooled and stratified analyses.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000388.
The World Health Organization has information on H1N1 vaccination
The US Centers for Disease Control and Prevention provides a fact sheet on the 2009 H1N1 influenza virus
The US Department of Health and Human services has a comprehensive website on flu
The European Centre for Disease Prevention and Control provides information on 2009 H1N1 pandemic
The European Centre for Disease Prevention and Control presents a summary of the 2009 H1N1 pandemic in Europe and elsewhere
doi:10.1371/journal.pmed.1000388
PMCID: PMC3019108  PMID: 21379316
4.  Integrating influenza antigenic dynamics with molecular evolution 
eLife  2014;3:e01914.
Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution.
DOI: http://dx.doi.org/10.7554/eLife.01914.001
eLife digest
Every year, seasonal influenza, commonly called flu, infects up to one in five people around the world, and causes up to half a million deaths. Even though the human immune system can detect and destroy the virus that causes influenza, people can catch flu many times throughout their lifetimes because the virus keeps evolving in an effort to avoid the immune system. This antigenic drift—so-called because the antigens displayed by the virus keep changing—also explains why influenza vaccines become less effective over time and need to be reformulated every year.
It is possible to determine which antigens are displayed by a new strain of the virus by observing how blood samples that respond to known strains respond to the new strain. This information about the “antigenic phenotype” of the virus can be plotted on an antigenic map in which strains with similar antigens cluster together. Gene sequencing has shown that there are four subtypes of the flu virus that commonly infect people; but the relationship between changes in antigenic phenotype and changes in gene sequences of the influenza virus is poorly understood.
Bedford et al. have now developed an approach to combine antigenic maps with genetic information about the four subtypes of the human flu virus. This revealed that the antigenic phenotype of H3N2—a subtype that is becoming increasingly common—evolved faster than the other three subtypes. Further, a correlation was observed between antigenic drift and the number of new influenza cases per year for each flu strain. This suggests that knowing which antigenic phenotypes are present at the start of flu season could help predict which strains of the virus will predominate later on.
The work of Bedford et al. provides a useful framework to study influenza, and could help to pinpoint which changes in viral genes cause the changes in antigens. This information could potentially speed up the development of new flu vaccines for each flu season.
DOI: http://dx.doi.org/10.7554/eLife.01914.002
doi:10.7554/eLife.01914
PMCID: PMC3909918  PMID: 24497547
influenza; evolution; antigenic cartography; phylogenetics; Bayesian inference; multidimensional scaling; viruses
5.  The Effect of Universal Influenza Immunization on Mortality and Health Care Use 
PLoS Medicine  2008;5(10):e211.
Background
In 2000, Ontario, Canada, initiated a universal influenza immunization program (UIIP) to provide free influenza vaccines for the entire population aged 6 mo or older. Influenza immunization increased more rapidly in younger age groups in Ontario compared to other Canadian provinces, which all maintained targeted immunization programs. We evaluated the effect of Ontario's UIIP on influenza-associated mortality, hospitalizations, emergency department (ED) use, and visits to doctors' offices.
Methods and Findings
Mortality and hospitalization data from 1997 to 2004 for all ten Canadian provinces were obtained from national datasets. Physician billing claims for visits to EDs and doctors' offices were obtained from provincial administrative datasets for four provinces with comprehensive data. Since outcomes coded as influenza are known to underestimate the true burden of influenza, we studied more broadly defined conditions. Hospitalizations, ED use, doctors' office visits for pneumonia and influenza, and all-cause mortality from 1997 to 2004 were modelled using Poisson regression, controlling for age, sex, province, influenza surveillance data, and temporal trends, and used to estimate the expected baseline outcome rates in the absence of influenza activity. The primary outcome was then defined as influenza-associated events, or the difference between the observed events and the expected baseline events. Changes in influenza-associated outcome rates before and after UIIP introduction in Ontario were compared to the corresponding changes in other provinces. After UIIP introduction, influenza-associated mortality decreased more in Ontario (relative rate [RR] = 0.26) than in other provinces (RR = 0.43) (ratio of RRs = 0.61, p = 0.002). Similar differences between Ontario and other provinces were observed for influenza-associated hospitalizations (RR = 0.25 versus 0.44, ratio of RRs = 0.58, p < 0.001), ED use (RR = 0.31 versus 0.69, ratio of RRs = 0.45, p < 0.001), and doctors' office visits (RR = 0.21 versus 0.52, ratio of RRs = 0.41, p < 0.001). Sensitivity analyses were carried out to assess consistency, specificity, and the presence of a dose-response relationship. Limitations of this study include the ecological study design, the nonspecific outcomes, difficulty in modeling baseline events, data quality and availability, and the inability to control for potentially important confounders.
Conclusions
Compared to targeted programs in other provinces, introduction of universal vaccination in Ontario in 2000 was associated with relative reductions in influenza-associated mortality and health care use. The results of this large-scale natural experiment suggest that universal vaccination may be an effective public health measure for reducing the annual burden of influenza.
Comparing influenza-related mortality and health care use between Ontario and other Canadian provinces, Jeffrey Kwong and colleagues find evidence that Ontario's universal vaccination program has reduced the burden of influenza.
Editors' Summary
Background.
Seasonal outbreaks (epidemics) of influenza—a viral disease of the nose, throat, and airways—affect millions of people and kill about 500,000 individuals every year. These epidemics occur because of “antigenic drift”: small but frequent changes in the viral proteins to which the human immune system responds mean that an immune response produced one year by exposure to an influenza virus provides only partial protection against influenza the next year. Immunization can boost this natural immunity and reduce a person's chances of catching influenza. That is, an injection of killed influenza viruses can be used to prime the immune system so that it responds quickly and efficiently when exposed to live virus. However, because of antigenic drift, for influenza immunization to be effective, it has to be repeated annually with a vaccine that contains the major circulating strains of the influenza virus.
Why Was This Study Done?
Public-health organizations recommend targeted vaccination programs, so that elderly people, infants, and chronically ill individuals—the people most likely to die from pneumonia and other complications of influenza—receive annual influenza vaccination. Some experts argue, however, that universal vaccination might provide populations with better protection from influenza, both directly by increasing the number of vaccinated people and indirectly through “herd immunity,” which occurs when a high proportion of the population is immune to an infectious disease, so that even unvaccinated people are unlikely to become infected (because infected people rarely come into contact with susceptible people). In this study, the researchers compare the effects of the world's first free universal influenza immunization program (UIIP), which started in 2000 in the Canadian province of Ontario, on influenza-associated deaths and health care use with the effects of targeted vaccine programs on the same outcomes elsewhere in Canada.
What Did the Researchers Do and Find?
Using national records, the researchers collected data on influenza vaccination, on all deaths, and on hospitalizations for pneumonia and influenza in all Canadian provinces between 1997 and 2004. They also collected data on emergency department and doctors' office visits for pneumonia and influenza for Ontario, Quebec, Alberta, and Manitoba. They then used a mathematical model to estimate the baseline rates for these outcomes in the absence of influenza activity, and from these calculated weekly rates for deaths and health care use specifically resulting from influenza. In 1996–1997, 18% of the population was vaccinated against influenza in Ontario whereas in the other provinces combined the vaccination rate was 13%. On average, since 2000—the year in which UIIP was introduced in Ontario—vaccination rates have risen to 38% and 24% in Ontario and the other provinces, respectively. Since the introduction of UIIP, the researchers report, influenza-associated deaths have decreased by 74% in Ontario but by only 57% in the other provinces combined. Influenza-associated use of health care facilities has also decreased more in Ontario than in the other provinces over the same period.
What Do These Findings Mean?
These findings are limited by some aspects of the study design. For example, they depend on the accuracy of the assumptions made when calculating events due specifically to influenza, and on the availability and accuracy of vaccination and clinical outcome data. In addition, it is possible that influenza-associated deaths and health care use may have decreased more in Ontario than in the other Canadian provinces because of some unrecognized health care changes specific to Ontario but unrelated to the introduction of universal influenza vaccination. Nevertheless, these findings indicate that, compared to the targeted vaccination programs in the other Canadian provinces, the Ontarian UIIP is associated with reductions in influenza-associated deaths and health care use, particularly in people younger than 65 years old. This effect is seen at a level of vaccination unlikely to produce herd immunity so might be more marked if the uptake of vaccination could be further increased. Thus, although it is possible that Canada is a special case, these findings suggest that universal influenza vaccination might be an effective way to reduce the global burden of influenza.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050211.
Read the related PLoSMedicine Perspective by Cécile Viboud and Mark Miller
A related PLoSMedicine Research Article by Carline van den Dool and colleagues is also available
The Ontario Ministry of Health provides information on its universal influenza immunization program (in English and French)
The World Health Organization provides information on influenza and on influenza vaccines (in several languages)
The US Centers for Disease Control and Prevention provide information for patients and professionals on all aspects of influenza (in English and Spanish)
MedlinePlus provides a list of links to other information about influenza (in English and Spanish)
The UK National Health Service provides information about the science of immunization, including a simple explanatory animation of immunity
doi:10.1371/journal.pmed.0050211
PMCID: PMC2573914  PMID: 18959473
6.  Protective efficacy of a broadly cross-reactive swine influenza DNA vaccine encoding M2e, cytotoxic T lymphocyte epitope and consensus H3 hemagglutinin 
Virology Journal  2012;9:127.
Background
Pigs have been implicated as mixing reservoir for the generation of new pandemic influenza strains, control of swine influenza has both veterinary and public health significance. Unlike human influenza vaccines, strains used for commercially available swine influenza vaccines are not regularly replaced, making the vaccines provide limited protection against antigenically diverse viruses. It is therefore necessary to develop broadly protective swine influenza vaccines that are efficacious to both homologous and heterologous virus infections. In this study, two forms of DNA vaccines were constructed, one was made by fusing M2e to consensus H3HA (MHa), which represents the majority of the HA sequences of H3N2 swine influenza viruses. Another was made by fusing M2e and a conserved CTL epitope (NP147-155) to consensus H3HA (MNHa). Their protective efficacies against homologous and heterologous challenges were tested.
Results
BALB/c mice were immunized twice by particle-mediated epidermal delivery (gene gun) with the two DNA vaccines. It was shown that the two vaccines elicited substantial antibody responses, and MNHa induced more significant T cell-mediated immune response than MHa did. Then two H3N2 strains representative of different evolutional and antigenic clusters were used to challenge the vaccine-immunized mice (homosubtypic challenge). Results indicated that both of the DNA vaccines prevented homosubtypic virus infections completely. The vaccines’ heterologous protective efficacies were further tested by challenging with a H1N1 swine influenza virus and a reassortant 2009 pandemic strain. It was found that MNHa reduced the lung viral titers significantly in both challenge groups, histopathological observation showed obvious reduction of lung pathogenesis as compared to MHa and control groups.
Conclusions
The combined utility of the consensus HA and the conserved M2e and CTL epitope can confer complete and partial protection against homologous and heterologous challenges, respectively, in mouse model. This may provide a basis for the development of universal swine influenza vaccines.
doi:10.1186/1743-422X-9-127
PMCID: PMC3447699  PMID: 22738661
Swine influenza; DNA vaccine; Consensus HA; M2e; CTL epitope
7.  The Effects of Influenza Vaccination of Health Care Workers in Nursing Homes: Insights from a Mathematical Model 
PLoS Medicine  2008;5(10):e200.
Background
Annual influenza vaccination of institutional health care workers (HCWs) is advised in most Western countries, but adherence to this recommendation is generally low. Although protective effects of this intervention for nursing home patients have been demonstrated in some clinical trials, the exact relationship between increased vaccine uptake among HCWs and protection of patients remains unknown owing to variations between study designs, settings, intensity of influenza seasons, and failure to control all effect modifiers. Therefore, we use a mathematical model to estimate the effects of HCW vaccination in different scenarios and to identify a herd immunity threshold in a nursing home department.
Methods and Findings
We use a stochastic individual-based model with discrete time intervals to simulate influenza virus transmission in a 30-bed long-term care nursing home department. We simulate different levels of HCW vaccine uptake and study the effect on influenza virus attack rates among patients for different institutional and seasonal scenarios. Our model reveals a robust linear relationship between the number of HCWs vaccinated and the expected number of influenza virus infections among patients. In a realistic scenario, approximately 60% of influenza virus infections among patients can be prevented when the HCW vaccination rate increases from 0 to 1. A threshold for herd immunity is not detected. Due to stochastic variations, the differences in patient attack rates between departments are high and large outbreaks can occur for every level of HCW vaccine uptake.
Conclusions
The absence of herd immunity in nursing homes implies that vaccination of every additional HCW protects an additional fraction of patients. Because of large stochastic variations, results of small-sized clinical trials on the effects of HCW vaccination should be interpreted with great care. Moreover, the large variations in attack rates should be taken into account when designing future studies.
Using a mathematical model to simulate influenza transmission in nursing homes, Carline van den Dool and colleagues find that each additional staff member vaccinated further reduces the risk to patients.
Editors' Summary
Background.
Every winter, millions of people catch influenza, a contagious viral disease of the nose, throat, and airways. Most people recover completely from influenza within a week or two but some develop life-threatening complications such as bacterial pneumonia. As a result, influenza outbreaks kill about half a million people—mainly infants, elderly people, and chronically ill individuals—each year. To minimize influenza-related deaths, the World Health Organization recommends that vulnerable people be vaccinated against influenza every autumn. Annual vaccination is necessary because flu viruses continually make small changes to the viral proteins (antigens) that the immune system recognizes. This means that an immune response produced one year provides only partial protection against influenza the next year. To provide maximum protection against influenza, each year's vaccine contains disabled versions of the major circulating strains of influenza viruses.
Why Was This Study Done?
Most Western countries also recommend annual flu vaccination for health care workers (HCWs) in hospitals and other institutions to reduce the transmission of influenza to vulnerable patients. However, many HCWs don't get a regular flu shot, so should efforts be made to increase their rate of vaccine uptake? To answer this question, public-health experts need to know more about the relationship between vaccine uptake among HCWs and patient protection. In particular, they need to know whether a high rate of vaccine uptake by HCWs will provide “herd immunity.” Herd immunity occurs because, when a sufficient fraction of a population is immune to a disease that passes from person to person, infected people rarely come into contact with susceptible people, which means that both vaccinated and unvaccinated people are protected from the disease. In this study, the researchers develop a mathematical model to investigate the relationship between vaccine uptake among HCWs and patient protection in a nursing home department.
What Did the Researchers Do and Find?
To predict influenza virus attack rates (the number of patient infections divided by the number of patients in a nursing home department during an influenza season) at different levels of HCW vaccine uptake, the researchers develop a stochastic transmission model to simulate epidemics on a computer. This model predicts that as the HCW vaccination rate increases from 0 (no HCWs vaccinated) to 1 (all the HCWs vaccinated), the expected average influenza virus attack rate decreases at a constant rate. In the researchers' baseline scenario—a nursing home department with 30 beds where patients come into contact with other patients, HCWs, and visitors—the model predicts that about 60% of the patients who would have been infected if no HCWs had been vaccinated are protected when all the HCWs are vaccinated, and that seven HCWs would have to be vaccinated to protect one patient. This last figure does not change with increasing vaccine uptake, which indicates that there is no level of HCW vaccination that completely stops the spread of influenza among the patients; that is, there is no herd immunity. Finally, the researchers show that large influenza outbreaks can happen by chance at every level of HCW vaccine uptake.
What Do These Findings Mean?
As with all mathematical models, the accuracy of these predictions may depend on the specific assumptions built into the model. Therefore the researchers verified that their findings hold for a wide range of plausible assumptions. These findings have two important practical implications. First, the direct relationship between HCW vaccination and patient protection and the lack of any herd immunity suggest that any increase in HCW vaccine uptake will be beneficial to patients in nursing homes. That is, increasing the HCW vaccination rate from 80% to 90% is likely to be as important as increasing it from 10% to 20%. Second, even 100% HCW vaccination cannot guarantee that influenza outbreaks will not occasionally occur in nursing homes. Because of the large variation in attack rates, the results of small clinical trials on the effects of HCW vaccination may be inaccurate and future studies will need to be very large if they are to provide reliable estimates of the amount of protection that HCW vaccination provides to vulnerable patients.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050200.
Read the related PLoSMedicine Perspective by Cécile Viboud and Mark Miller
A related PLoSMedicine Research Article by Jeffrey Kwong and colleagues is also available
The World Health Organization provides information on influenza and on influenza vaccines (in several languages)
The US Centers for Disease Control and Prevention provide information for patients and professionals on all aspects of influenza (in English and Spanish)
The UK Health Protection Agency also provides information on influenza
MedlinePlus provides a list of links to other information about influenza (in English and Spanish)
The UK National Health Service provides information about herd immunity, including a simple explanatory animation
The European Centre for Disease Prevention and Control provides an overview on the types of influenza
doi:10.1371/journal.pmed.0050200
PMCID: PMC2573905  PMID: 18959470
8.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions 
PLoS Medicine  2007;4(1):e13.
Background
The highly pathogenic H5N1 avian influenza virus, which is now widespread in Southeast Asia and which diffused recently in some areas of the Balkans region and Western Europe, has raised a public alert toward the potential occurrence of a new severe influenza pandemic. Here we study the worldwide spread of a pandemic and its possible containment at a global level taking into account all available information on air travel.
Methods and Findings
We studied a metapopulation stochastic epidemic model on a global scale that considers airline travel flow data among urban areas. We provided a temporal and spatial evolution of the pandemic with a sensitivity analysis of different levels of infectiousness of the virus and initial outbreak conditions (both geographical and seasonal). For each spreading scenario we provided the timeline and the geographical impact of the pandemic in 3,100 urban areas, located in 220 different countries. We compared the baseline cases with different containment strategies, including travel restrictions and the therapeutic use of antiviral (AV) drugs. We investigated the effect of the use of AV drugs in the event that therapeutic protocols can be carried out with maximal coverage for the populations in all countries. In view of the wide diversity of AV stockpiles in different regions of the world, we also studied scenarios in which only a limited number of countries are prepared (i.e., have considerable AV supplies). In particular, we compared different plans in which, on the one hand, only prepared and wealthy countries benefit from large AV resources, with, on the other hand, cooperative containment scenarios in which countries with large AV stockpiles make a small portion of their supplies available worldwide.
Conclusions
We show that the inclusion of air transportation is crucial in the assessment of the occurrence probability of global outbreaks. The large-scale therapeutic usage of AV drugs in all hit countries would be able to mitigate a pandemic effect with a reproductive rate as high as 1.9 during the first year; with AV supply use sufficient to treat approximately 2% to 6% of the population, in conjunction with efficient case detection and timely drug distribution. For highly contagious viruses (i.e., a reproductive rate as high as 2.3), even the unrealistic use of supplies corresponding to the treatment of approximately 20% of the population leaves 30%–50% of the population infected. In the case of limited AV supplies and pandemics with a reproductive rate as high as 1.9, we demonstrate that the more cooperative the strategy, the more effective are the containment results in all regions of the world, including those countries that made part of their resources available for global use.
A metapopulation stochastic epidemic model for influenza shows the need to include air transportation when assessing the occurrence probability of global outbreaks. The impact of the use of antiviral drugs is also measured.
Editors' Summary
Background.
Seasonal outbreaks (epidemics) of influenza—a viral infection of the nose, throat, and airways—affect millions of people and kill about 500,000 individuals every year. Regular epidemics occur because flu viruses frequently make small changes in the viral proteins (antigens) recognized by the human immune system. Consequently, a person's immune-system response that combats influenza one year provides incomplete protection the next year. Occasionally, a human influenza virus appears that contains large antigenic changes. People have little immunity to such viruses (which often originate in birds or animals), so they can start a global epidemic (pandemic) that kills millions of people. Experts fear that a human influenza pandemic could be triggered by the avian H5N1 influenza virus, which is present in bird flocks around the world. So far, fewer than 300 people have caught this virus but more than 150 people have died.
Why Was This Study Done?
Avian H5N1 influenza has not yet triggered a human pandemic, because it rarely passes between people. If it does acquire this ability, it would take 6–8 months to develop a vaccine to provide protection against this new, potentially pandemic virus. Public health officials therefore need other strategies to protect people during the first few months of a pandemic. These could include international travel restrictions and the use of antiviral drugs. However, to get the most benefit from these interventions, public-health officials need to understand how influenza pandemics spread, both over time and geographically. In this study, the researchers have used detailed information on air travel to model the global spread of an emerging influenza pandemic and its containment.
What Did the Researchers Do and Find?
The researchers incorporated data on worldwide air travel and census data from urban centers near airports into a mathematical model of the spread of an influenza pandemic. They then used this model to investigate how the spread and health effects of a pandemic flu virus depend on the season in which it emerges (influenza virus thrives best in winter), where it emerges, and how infectious it is. Their model predicts, for example, that a flu virus originating in Hanoi, Vietnam, with a reproductive number (R0) of 1.1 (a measure of how many people an infectious individual infects on average) poses a very mild global threat. However, epidemics initiated by a virus with an R0 of more than 1.5 would often infect half the population in more than 100 countries. Next, the researchers used their model to show that strict travel restrictions would have little effect on pandemic evolution. More encouragingly, their model predicts that antiviral drugs would mitigate pandemics of a virus with an R0 up to 1.9 if every country had an antiviral drug stockpile sufficient to treat 5% of its population; if the R0 was 2.3 or higher, the pandemic would not be contained even if 20% of the population could be treated. Finally, the researchers considered a realistic scenario in which only a few countries possess antiviral stockpiles. In these circumstances, compared with a “selfish” strategy in which countries only use their antiviral drugs within their borders, limited worldwide sharing of antiviral drugs would slow down the spread of a flu virus with an R0 of 1.9 by more than a year and would benefit both drug donors and recipients.
What Do These Findings Mean?
Like all mathematical models, this model for the global spread of an emerging pandemic influenza virus contains many assumptions (for example, about viral behavior) that might affect the accuracy of its predictions. The model also does not consider variations in travel frequency between individuals or viral spread in rural areas. Nevertheless, the model provides the most extensive global simulation of pandemic influenza spread to date. Reassuringly, it suggests that an emerging virus with a low R0 would not pose a major public-health threat, since its attack rate would be limited and would not peak for more than a year, by which time a vaccine could be developed. Most importantly, the model suggests that cooperative sharing of antiviral drugs, which could be organized by the World Health Organization, might be the best way to deal with an emerging influenza pandemic.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040013.
The US Centers for Disease Control and Prevention has information about influenza for patients and professionals, including key facts about avian influenza and antiviral drugs
The US National Institute of Allergy and Infectious Disease features information on seasonal, avian, and pandemic flu
The US Department of Health and Human Services provides information on pandemic flu and avian flu, including advice to travelers
World Health Organization has fact sheets on influenza and avian influenza, including advice to travelers and current pandemic flu threat
The UK Health Protection Agency has information on seasonal, avian, and pandemic influenza
The UK Department of Health has a feature article on bird flu and pandemic influenza
doi:10.1371/journal.pmed.0040013
PMCID: PMC1779816  PMID: 17253899
9.  Changing Selective Pressure during Antigenic Changes in Human Influenza H3 
PLoS Pathogens  2008;4(5):e1000058.
The rapid evolution of influenza viruses presents difficulties in maintaining the optimal efficiency of vaccines. Amino acid substitutions result in antigenic drift, a process whereby antisera raised in response to one virus have reduced effectiveness against future viruses. Interestingly, while amino acid substitutions occur at a relatively constant rate, the antigenic properties of H3 move in a discontinuous, step-wise manner. It is not clear why this punctuated evolution occurs, whether this represents simply the fact that some substitutions affect these properties more than others, or if this is indicative of a changing relationship between the virus and the host. In addition, the role of changing glycosylation of the haemagglutinin in these shifts in antigenic properties is unknown. We analysed the antigenic drift of HA1 from human influenza H3 using a model of sequence change that allows for variation in selective pressure at different locations in the sequence, as well as at different parts of the phylogenetic tree. We detect significant changes in selective pressure that occur preferentially during major changes in antigenic properties. Despite the large increase in glycosylation during the past 40 years, changes in glycosylation did not correlate either with changes in antigenic properties or with significantly more rapid changes in selective pressure. The locations that undergo changes in selective pressure are largely in places undergoing adaptive evolution, in antigenic locations, and in locations or near locations undergoing substitutions that characterise the change in antigenicity of the virus. Our results suggest that the relationship of the virus to the host changes with time, with the shifts in antigenic properties representing changes in this relationship. This suggests that the virus and host immune system are evolving different methods to counter each other. While we are able to characterise the rapid increase in glycosylation of the haemagglutinin during time in human influenza H3, an increase not present in influenza in birds, this increase seems unrelated to the observed changes in antigenic properties.
Author Summary
H3N2-type influenza is responsible for widespread disease and significant mortality. The virus evolves rapidly, changing its antigenic properties, allowing it to escape clearance by the immune response as well as complicating the maintenance of vaccine effectiveness. Part of this evolution has been the rapid increase in glycosylation, an increase not observed either in H9 evolution in birds or in H1 evolution in humans. It has been observed that the antigenic properties change in a punctuated, discontinuous manner. This could be either because some mutations are more significant than others, or it could mean that the antigenic changes correspond to adjustments in the antagonistic relationship between virus and host. By studying the sequence evolution of the H3 haemagglutinin, we can demonstrate that the selective pressure acting on the virus protein changes with time, and that these changes are especially rapid during changes in antigenic properties. This indicates that the antigenic changes correspond to modifications in the virus–host relationship. Surprisingly, neither the changes in selective pressure nor the changes in antigenic properties correspond to changes in glycosylation.
doi:10.1371/journal.ppat.1000058
PMCID: PMC2323114  PMID: 18451985
10.  Optimizing the Dose of Pre-Pandemic Influenza Vaccines to Reduce the Infection Attack Rate 
PLoS Medicine  2007;4(6):e218.
Background
The recent spread of avian influenza in wild birds and poultry may be a precursor to the emergence of a 1918-like human pandemic. Therefore, stockpiles of human pre-pandemic vaccine (targeted at avian strains) are being considered. For many countries, the principal constraint for these vaccine stockpiles will be the total mass of antigen maintained. We tested the hypothesis that lower individual doses (i.e., less than the recommended dose for maximum protection) may provide substantial extra community-level benefits because they would permit wider vaccine coverage for a given total size of antigen stockpile.
Methods and Findings
We used a mathematical model to predict infection attack rates under different policies. The model incorporated both an individual's response to vaccination at different doses and the process of person-to-person transmission of pandemic influenza. We found that substantial reductions in the attack rate are likely if vaccines are given to more people at lower doses. These results are applicable to all three vaccine candidates for which data are available. As a guide to the magnitude of the effect, we simulated epidemics based on historical studies of immunogenicity. For example, for one of the vaccines for which data are available, the attack rate would drop from 67.6% to 58.7% if 160 out of the total US population of 300 million were given an optimal dose rather than 20 out of 300 million given the maximally protective dose (as promulgated in the US National Pandemic Preparedness Plan). Our results are conservative with respect to a number of alternative assumptions about the precise nature of vaccine protection. We also considered a model variant that includes a single high-risk subgroup representing children. For smaller stockpile sizes that allow vaccine to be offered only to the high-risk group at the optimal dose, the predicted benefits of using the homogenous model formed a lower bound in the presence of a risk group, even when the high-risk group was twice as infective and twice as susceptible.
Conclusions
In addition to individual-level protection (i.e., vaccine efficacy), the population-level implications of pre-pandemic vaccine programs should be considered when deciding on stockpile size and dose. Our results suggest that a lower vaccine dose may be justified in order to increase population coverage, thereby reducing the infection attack rate overall.
Steven Riley and colleagues examine the potential benefits of "stretching" a limited supply of vaccine and suggest that substantial reductions in the attack rate are possible if vaccines are given to more people at lower doses.
Editors' Summary
Background.
Every winter, millions of people catch influenza, a viral infection of the nose, throat, and airways. Most recover quickly, but the disease can be deadly. In the US, seasonal influenza outbreaks (epidemics) cause 36,000 excess deaths annually. And now there are fears that an avian (bird) influenza virus might trigger a human influenza pandemic—a global epidemic that could kill millions. Seasonal epidemics occur because flu viruses continually make small changes to their hemagglutinin and neuraminidase molecules, the viral proteins (antigens) that the immune system recognizes. Because of this “antigenic drift,” an immune system response (which can be induced by catching flu or by vaccination with disabled circulating influenza strains) that combats flu one year may provide only partial protection the next year. “Antigenic shift” (large changes in flu antigens) can cause pandemics because communities have no immunity to the changed virus.
Why Was This Study Done?
Although avian influenza virus, which contains a hemagglutinin type that differs from currently circulating human flu viruses, has caused a few cases of human influenza, it has not started a human pandemic yet because it cannot move easily between people. If it acquires this property, which will probably involve further small antigenic changes, it could kill millions of people before scientists can develop an effective vaccine against it. To provide some interim protection, many countries are preparing stockpiles of “pre-pandemic” vaccines targeted against the avian virus. The US, for example, plans to store enough pre-pandemic vaccine to provide maximum protection to 20 million people (including key health workers) out of its population of 300 million. But, given a limited stockpile of pre-pandemic vaccine, might giving more people a lower dose of vaccine, which might reduce the number of people susceptible to infection and induce herd immunity by preventing efficient transmission of the flu virus, be a better way to limit the spread of pandemic influenza? In this study, the researchers have used mathematical modeling to investigate this question.
What Did the Researchers Do and Find?
To predict the infection rates associated with different vaccination policies, the researchers developed a mathematical model that incorporates data on human immune responses induced with three experimental vaccines against the avian virus and historical data on the person–person transmission of previous pandemic influenza viruses. For all the vaccines, the model predicts that giving more people a low dose of the vaccine would limit the spread of influenza better than giving fewer people the high dose needed for full individual protection. For example, the researchers estimate that dividing the planned US stockpile of one experimental vaccine equally between 160 million people instead of giving it at the fully protective dose to 20 million people might avert about 27 million influenza cases in less than year. However, giving the maximally protective dose to the 9 million US health-care workers and using the remaining vaccine at a lower dose to optimize protection within the general population might avert only 14 million infections.
What Do These Findings Mean?
These findings suggest that, given a limited stockpile of pre-pandemic vaccine, increasing the population coverage of vaccination by using low doses of vaccine might reduce the overall influenza infection rate more effectively than vaccinating fewer people with fully protective doses of vaccine. However, because the researchers' model includes many assumptions, it can only give an indication of how different strategies might perform, not firm numbers for how many influenza cases each strategy is likely to avert. Before public-health officials use this or a similar model to help them decide the best way to use pre-pandemic vaccines to control a human influenza pandemic, they will need more information about the efficacy of these vaccines and about transmission rates of currently circulating viruses. They will also need to know whether pre-pandemic vaccines actually provide good protection against the pandemic virus, as assumed in this study, before they can recommend mass immunization with low doses of pre-pandemic vaccine, selective vaccination with high doses, or a mixed strategy.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040218.
US Centers for Disease Control and Prevention provide information on influenza and influenza vaccination for patients and health professionals (in English, Spanish, Filipino, Chinese, and Vietnamese)
The World Health Organization has a fact sheet on influenza and on the global response to avian influenza (in English, Spanish, French, Russian, Arabic, and Chinese)
The MedlinePlus online encyclopedia devotes a page to flu (in English and Spanish)
The UK Health Protection Agency information on avian, pandemic, and seasonal influenza
The US National Institute of Allergy and Infectious Diseases has a comprehensive feature called “focus on the flu”
doi:10.1371/journal.pmed.0040218
PMCID: PMC1892041  PMID: 17579511
11.  Characterization of Uncultivable Bat Influenza Virus Using a Replicative Synthetic Virus 
PLoS Pathogens  2014;10(10):e1004420.
Bats harbor many viruses, which are periodically transmitted to humans resulting in outbreaks of disease (e.g., Ebola, SARS-CoV). Recently, influenza virus-like sequences were identified in bats; however, the viruses could not be cultured. This discovery aroused great interest in understanding the evolutionary history and pandemic potential of bat-influenza. Using synthetic genomics, we were unable to rescue the wild type bat virus, but could rescue a modified bat-influenza virus that had the HA and NA coding regions replaced with those of A/PR/8/1934 (H1N1). This modified bat-influenza virus replicated efficiently in vitro and in mice, resulting in severe disease. Additional studies using a bat-influenza virus that had the HA and NA of A/swine/Texas/4199-2/1998 (H3N2) showed that the PR8 HA and NA contributed to the pathogenicity in mice. Unlike other influenza viruses, engineering truncations hypothesized to reduce interferon antagonism into the NS1 protein didn't attenuate bat-influenza. In contrast, substitution of a putative virulence mutation from the bat-influenza PB2 significantly attenuated the virus in mice and introduction of a putative virulence mutation increased its pathogenicity. Mini-genome replication studies and virus reassortment experiments demonstrated that bat-influenza has very limited genetic and protein compatibility with Type A or Type B influenza viruses, yet it readily reassorts with another divergent bat-influenza virus, suggesting that the bat-influenza lineage may represent a new Genus/Species within the Orthomyxoviridae family. Collectively, our data indicate that the bat-influenza viruses recently identified are authentic viruses that pose little, if any, pandemic threat to humans; however, they provide new insights into the evolution and basic biology of influenza viruses.
Author Summary
The identification of influenza virus-like sequences in two different bat species has generated great interest in understanding their biology, ability to mix with other influenza viruses, and their public health threat. Unfortunately, bat-influenza viruses couldn't be cultured from the samples containing the influenza-like nucleic acids. We used synthetic genomics strategies to create wild type bat-influenza, or bat-influenza modified by substituting the surface glycoproteins with those of model influenza A viruses. Although influenza virus-like particles were produced from both synthetic genomes, only the modified bat-influenza viruses could be cultured. The modified bat-influenza viruses replicated efficiently in vitro and an H1N1 modified version caused severe disease in mice. Collectively our data show: (1) the two bat-flu genomes identified in other studies are replication competent, suggesting that host cell specificity is the major limitation for propagation of bat-influenza, (2) bat-influenza NS1 antagonizes host interferon response more efficiently than that of a model influenza A virus, (3) bat-influenza has both genetic and protein incompatibility with influenza A or B viruses, and (4) that these bat-influenza lineages pose little pandemic threat.
doi:10.1371/journal.ppat.1004420
PMCID: PMC4183581  PMID: 25275541
12.  A Comparative Analysis of Influenza Vaccination Programs 
PLoS Medicine  2006;3(10):e387.
Background
The threat of avian influenza and the 2004–2005 influenza vaccine supply shortage in the United States have sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus.
Methods and Findings
We present a comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high-risk populations and morbidity-based strategies that target high-prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we assume that vaccine supplies are limited and then evaluate the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions.
We find that the optimal strategy depends critically on the viral transmission level (reproductive rate) of the virus: morbidity-based strategies outperform mortality-based strategies for moderately transmissible strains, while the reverse is true for highly transmissible strains. These results hold for a range of mortality rates reported for prior influenza epidemics and pandemics. Furthermore, we show that vaccination delays and multiple introductions of disease into the community have a more detrimental impact on morbidity-based strategies than mortality-based strategies.
Conclusions
If public health officials have reasonable estimates of the viral transmission rate and the frequency of new introductions into the community prior to an outbreak, then these methods can guide the design of optimal vaccination priorities. When such information is unreliable or not available, as is often the case, this study recommends mortality-based vaccination priorities.
A comparative analysis of two classes of suggested vaccination strategies, mortality-based strategies that target high-risk populations and morbidity-based strategies that target high-prevalence populations.
Editors' Summary
Background.
Influenza—a viral infection of the nose, throat, and airways that is transmitted in airborne droplets released by coughing or sneezing—is a serious public health threat. Most people recover quickly from influenza, but some individuals, especially infants, old people, and individuals with chronic health problems, can develop pneumonia and die. In the US, seasonal outbreaks (epidemics) of flu cause an estimated 36,000 excess deaths annually. And now there are fears that avian influenza might start a human pandemic—a global epidemic that could kill millions. Seasonal outbreaks of influenza occur because flu viruses continually change the viral proteins (antigens) to which the immune system responds. “Antigenic drift”—small changes in these proteins—means that an immune system response that combats flu one year may not provide complete protection the next winter. “Antigenic shift”—large antigen changes—can cause pandemics because communities have no immunity to the changed virus. Annual vaccination with vaccines based on the currently circulating viruses controls seasonal flu epidemics; to control a pandemic, vaccines based on the antigenically altered virus would have to be quickly developed.
Why Was This Study Done?
Most countries target vaccination efforts towards the people most at risk of dying from influenza, and to health-care workers who are likely come into contact with flu patients. But is this the best way to reduce the burden of illness (morbidity) and death (mortality) caused by influenza, particularly at the start of a pandemic, when vaccine would be limited? Old people and infants are much less likely to catch and spread influenza than school children, students, and employed adults, so could vaccination of these sections of the population—instead of those most at risk of death—be the best way to contain influenza outbreaks? In this study, the researchers used an analytical method called “contact network epidemiology” to compare two types of vaccination strategies: the currently favored mortality-based strategy, which targets high-risk individuals, and a morbidity-based strategy, which targets those segments of the community in which most influenza cases occur.
What Did the Researchers Do and Find?
Most models of disease transmission assume that each member of a community is equally likely to infect every other member. But a baby is unlikely to transmit flu to, for example, an unrelated, housebound elderly person. Contact network epidemiology takes the likely relationships between people into account when modeling disease transmission. Using information from Vancouver, British Columbia, Canada, on household size, age distribution, and occupations, and other factors such as school sizes, the researchers built a model population of a quarter of a million interconnected people. They then investigated how different vaccination strategies controlled the spread of influenza in this population. The optimal strategy depended on the level of viral transmissibility—the likelihood that an infectious person transmits influenza to a susceptible individual with whom he or she has contact. For moderately transmissible flu viruses, a morbidity-based vaccination strategy, in which the people most likely to catch the flu are vaccinated, was more effective at containing seasonal and pandemic outbreaks than a mortality-based strategy, in which the people most likely to die if they caught the flu are vaccinated. For highly transmissible strains, this situation was reversed. The level of transmissibility at which this reversal occurred depended on several factors, including whether vaccination was delayed and how many times influenza was introduced into the community.
What Do These Findings Mean?
The researchers tested their models by checking that they could replicate real influenza epidemics and pandemics, but, as with all mathematical models, they included many assumptions about influenza in their calculations, which may affect their results. Also, because the contact network used data from Vancouver, their results might not be applicable to other cities, or to nonurban areas. Nevertheless, their findings have important public health implications. When there are reasonable estimates of the viral transmission rate, and it is known how often influenza is being introduced into a community, contact network models could help public health officials choose between morbidity- and mortality-based vaccination strategies. When the viral transmission rate is unreliable or unavailable (for example, at the start of a pandemic), the best policy would be the currently preferred strategy of mortality-based vaccination. More generally, the use of contact network models should improve estimates of how infectious diseases spread through populations and indicate the best ways to control human epidemics and pandemics.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030387.
US Centers for Disease Control and Prevention information about influenza for patients and professionals, including key facts on vaccination
US National Institute of Allergy and Infectious Diseases feature on seasonal, avian, and pandemic influenza
World Health Organization fact sheet on influenza, with links to information on vaccination
UK Health Protection Agency information on seasonal, avian, and pandemic influenza
MedlinePlus entry on influenza
doi:10.1371/journal.pmed.0030387
PMCID: PMC1584413  PMID: 17020406
13.  Global Migration Dynamics Underlie Evolution and Persistence of Human Influenza A (H3N2) 
PLoS Pathogens  2010;6(5):e1000918.
The global migration patterns of influenza viruses have profound implications for the evolutionary and epidemiological dynamics of the disease. We developed a novel approach to reconstruct the genetic history of human influenza A (H3N2) collected worldwide over 1998 to 2009 and used it to infer the global network of influenza transmission. Consistent with previous models, we find that China and Southeast Asia lie at the center of this global network. However, we also find that strains of influenza circulate outside of Asia for multiple seasons, persisting through dynamic migration between northern and southern regions. The USA acts as the primary hub of temperate transmission and, together with China and Southeast Asia, forms the trunk of influenza's evolutionary tree. These findings suggest that antiviral use outside of China and Southeast Asia may lead to the evolution of long-term local and potentially global antiviral resistance. Our results might also aid the design of surveillance efforts and of vaccines better tailored to different geographic regions.
Author Summary
Infections by the influenza A virus show highly seasonal patterns in temperate regions. Winter is flu season. Over the course of the autumn and winter, a small number of initial infections grow to encompass a significant proportion of the population. At the end of the winter, infection disappears. It has been suggested that the strains founding each temperate flu season originate from China and Southeast Asia, where influenza A is less seasonal. We test this hypothesis through analysis of genetic sequences from viruses sampled throughout the world between 1998 and 2009. We find that although China and Southeast Asia play the largest role in the migration network, temperate regions, particularly the USA, also make important contributions. Not all temperate strains of influenza die out with the end of the winter season. Instead many strains emigrate to more favorable climes. Thus, we find patterns of influenza transmission to be highly dynamic. Because of emigration out of temperate regions, mutations harbored by temperate strains of influenza A can spread to the global virus population. This means that regional public health decisions regarding influenza may have global impacts.
doi:10.1371/journal.ppat.1000918
PMCID: PMC2877742  PMID: 20523898
14.  Extreme Evolutionary Conservation of Functionally Important Regions in H1N1 Influenza Proteome 
PLoS ONE  2013;8(11):e81027.
The H1N1 subtype of influenza A virus has caused two of the four documented pandemics and is responsible for seasonal epidemic outbreaks, presenting a continuous threat to public health. Co-circulating antigenically divergent influenza strains significantly complicates vaccine development and use. Here, by combining evolutionary, structural, functional, and population information about the H1N1 proteome, we seek to answer two questions: (1) do residues on the protein surfaces evolve faster than the protein core residues consistently across all proteins that constitute the influenza proteome? and (2) in spite of the rapid evolution of surface residues in influenza proteins, are there any protein regions on the protein surface that do not evolve? To answer these questions, we first built phylogenetically-aware models of the patterns of surface and interior substitutions. Employing these models, we found a single coherent pattern of faster evolution on the protein surfaces that characterizes all influenza proteins. The pattern is consistent with the events of inter-species reassortment, the worldwide introduction of the flu vaccine in the early 80’s, as well as the differences caused by the geographic origins of the virus. Next, we developed an automated computational pipeline to comprehensively detect regions of the protein surface residues that were 100% conserved over multiple years and in multiple host species. We identified conserved regions on the surface of 10 influenza proteins spread across all avian, swine, and human strains; with the exception of a small group of isolated strains that affected the conservation of three proteins. Surprisingly, these regions were also unaffected by genetic variation in the pandemic 2009 H1N1 viral population data obtained from deep sequencing experiments. Finally, the conserved regions were intrinsically related to the intra-viral macromolecular interaction interfaces. Our study may provide further insights towards the identification of novel protein targets for influenza antivirals.
doi:10.1371/journal.pone.0081027
PMCID: PMC3839886  PMID: 24282564
15.  Identifying Selection in the Within-Host Evolution of Influenza Using Viral Sequence Data 
PLoS Computational Biology  2014;10(7):e1003755.
The within-host evolution of influenza is a vital component of its epidemiology. A question of particular interest is the role that selection plays in shaping the viral population over the course of a single infection. We here describe a method to measure selection acting upon the influenza virus within an individual host, based upon time-resolved genome sequence data from an infection. Analysing sequence data from a transmission study conducted in pigs, describing part of the haemagglutinin gene (HA1) of an influenza virus, we find signatures of non-neutrality in six of a total of sixteen infections. We find evidence for both positive and negative selection acting upon specific alleles, while in three cases, the data suggest the presence of time-dependent selection. In one infection we observe what is potentially a specific immune response against the virus; a non-synonymous mutation in an epitope region of the virus is found to be under initially positive, then strongly negative selection. Crucially, given the lack of homologous recombination in influenza, our method accounts for linkage disequilibrium between nucleotides at different positions in the haemagglutinin gene, allowing for the analysis of populations in which multiple mutations are present at any given time. Our approach offers a new insight into the dynamics of influenza infection, providing a detailed characterisation of the forces that underlie viral evolution.
Author Summary
The evolution of the influenza virus is of great importance for human health. Through evolution, current influenza viruses develop the ability to infect people who have been vaccinated against earlier strains. New strains of influenza that infect birds and pigs could evolve to infect and spread between people, causing a global pandemic. The influenza virus lives within a human or animal host, so that viral evolution happens within, or in the spread between, individuals. As such, what happens to the virus during the course of an infection is a question of great interest. We here describe a statistical method that uses viral genome sequence data to measure how evolution affects the influenza virus within a single host. Studying data from infections transmitted between pigs, we find evidence for evolutionary adaptation in six of sixteen animals for which data were available. In one case, an immune response mounted by a pig against the virus is apparent. Our method provides a statistical framework for using sequence data to study viral evolution on very short timescales, enabling new research into within-host viral evolution.
doi:10.1371/journal.pcbi.1003755
PMCID: PMC4117419  PMID: 25080215
16.  Vaccinating to Protect a Vulnerable Subpopulation 
PLoS Medicine  2007;4(5):e174.
Background
Epidemic influenza causes serious mortality and morbidity in temperate countries each winter. Research suggests that schoolchildren are critical in the spread of influenza virus, while the elderly and the very young are most vulnerable to the disease. Under these conditions, it is unclear how best to focus prevention efforts in order to protect the population. Here we investigate the question of how to protect a population against a disease when one group is particularly effective at spreading disease and another group is more vulnerable to the effects of the disease.
Methods and Findings
We developed a simple mathematical model of an epidemic that includes assortative mixing between groups of hosts. We evaluate the impact of different vaccine allocation strategies across a wide range of parameter values. With this model we demonstrate that the optimal vaccination strategy is extremely sensitive to the assortativity of population mixing, as well as to the reproductive number of the disease in each group. Small differences in parameter values can change the best vaccination strategy from one focused on the most vulnerable individuals to one focused on the most transmissive individuals.
Conclusions
Given the limited amount of information about relevant parameters, we suggest that changes in vaccination strategy, while potentially promising, should be approached with caution. In particular, we find that, while switching vaccine to more active groups may protect vulnerable groups in many cases, switching too much vaccine, or switching vaccine under slightly different conditions, may lead to large increases in disease in the vulnerable group. This outcome is more likely when vaccine limitation is stringent, when mixing is highly structured, or when transmission levels are high.
Jonathan Dushoff and colleagues model the benefits of different vaccination strategies and suggest that small differences in how populations mix can change the best vaccination strategy from one focused on the most vulnerable individuals to one focused on the most transmissive individuals.
Editors' Summary
Background.
Every winter, millions of people take to their beds with influenza—a viral infection of the nose, throat, and airways that is transmitted in airborne droplets released by coughing and sneezing. Most people who catch flu recover within a few days, but some develop serious complications such as pneumonia, and in the US alone, about 36,000 people—mainly infants, elderly, and chronically ill individuals—die every year. To minimize the morbidity (illness) and mortality (death) associated with seasonal (epidemic) influenza, the World Health Organization recommends that these vulnerable people be vaccinated against influenza every autumn. Annual vaccination is necessary because flu viruses continually make small changes to the viral proteins that the immune system recognizes.
Why Was This Study Done?
Although infants and the elderly are particularly vulnerable to influenza, schoolchildren are more likely to spread the flu virus. Also, vaccination is more effective in schoolchildren than in elderly people. So could vaccination of schoolchildren be the best way to reduce influenza morbidity and mortality? Some Japanese and US data suggest that it might be, but policymakers need to know more about the likely effects of changing the current influenza vaccination strategy. They need to know in what circumstances the direct effects of vaccination (protection of vaccinated individuals from disease) outweigh its indirect effects (reduced infection in vulnerable individuals caused by the reduced spread of disease in the whole population) and when the opposite is true. In this study, the researchers have used mathematical modeling to investigate how vaccination affects the spread of diseases such as influenza for which a “core” group in the population spreads the disease and a distinct “vulnerable” group is sensitive to its effects.
What Did the Researchers Do and Find?
The researchers developed a mathematical model in which members of each group mixed mainly with their own group (assortative mixing) and used it to predict how changing the proportion of a limited amount of vaccine given to each group might affect disease spread under different conditions. For example, they report that in a population in which the two groups were very unlikely to mix and viral transmission was low, switching vaccine from the vulnerable group to the core group initially increased infections in the vulnerable group because fewer individuals were directly protected but, as more vaccine was allocated to the core group, fewer vulnerable people became infected because the size of the epidemic decreased. When viral transmission was high, vaccination of the vulnerable group was always best. However, when viral transmission was moderate, shifting vaccine from the vulnerable group first increased, then decreased infections in this group before increasing them again. This last change occurred when vaccination in the vulnerable group was so low that viral transmission was sufficient to maintain the epidemic within this group.
What Do These Findings Mean?
As with all mathematical modeling, the researchers' findings depend on the assumptions included in the model, many of which are based on limited information. The model also considers a population that contains only two groups, an unlikely situation in real life. Nevertheless, these findings indicate that in a population in which one group of people is mainly responsible for the spread of a disease and another is most vulnerable to its effects, the best vaccination strategy is very sensitive to how the groups mix and how well the disease spreads in each group. Small changes in these poorly understood parameters can change the optimal vaccination strategy from one that vaccinates vulnerable individuals to one that mainly vaccinates the people who spread the disease. Importantly, a beneficial change in strategy can become deleterious if taken too far, so policy makers need to approach potentially promising changes in vaccination policy cautiously. Finally, for influenza, the model supports the idea that using some vaccine stocks in schoolchildren might decrease morbidity and mortality among elderly people but suggests that—even if this turns out to be correct—if all the vaccine were given to schoolchildren, more old people might die. Thus, the most prudent policy would be to supplement rather than replace vaccination of the elderly with vaccination of children.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040174.
US Centers for Disease Control and Prevention provide information about influenza for patients and professionals, including key facts about the flu vaccine (in English and Spanish)
World Health Organization, fact sheet on influenza and information on vaccination (in English, Spanish, French, Arabic, Chinese and Russian)
UK Health Protection Agency, information on seasonal influenza
MedlinePlus encyclopedia entries on influenza and the influenza vaccine (in English and Spanish)
Public disease mortality and morbidity data at the International Infectious Disease Data Archive (IIDDA)
doi:10.1371/journal.pmed.0040174
PMCID: PMC1872043  PMID: 17518515
17.  Co-evolution positions and rules for antigenic variants of human influenza A/H3N2 viruses 
BMC Bioinformatics  2009;10(Suppl 1):S41.
Background
In pandemic and epidemic forms, avian and human influenza viruses often cause significant damage to human society and economics. Gradually accumulated mutations on hemagglutinin (HA) cause immunologically distinct circulating strains, which lead to the antigenic drift (named as antigenic variants). The "antigenic variants" often requires a new vaccine to be formulated before each annual epidemic. Mapping the genetic evolution to the antigenic drift of influenza viruses is an emergent issue to public health and vaccine development
Results
We developed a method for identifying antigenic critical amino acid positions, rules, and co-mutated positions for antigenic variants. The information gain (IG) and the entropy are used to measure the score of an amino acid position on hemagglutinin (HA) for discriminating between antigenic variants and similar viruses. A position with high IG and entropy implied that this position is highly correlated to an antigenic drift. Nineteen positions with high IG and high genetic diversity are identified as antigenic critical positions on the HA proteins. Most of these antigenic critical positions are located on five epitopes or on the surface based on the HA structure. Based on IG values and entropies of these 19 positions on the HA, the decision tree was applied to create a rule-based model and to identify rules for predicting antigenic variants of a given two HA sequences which are often a vaccine strain and a circulating strain. The predicting accuracies of this model on two sets, which consist of a training set (181 hemagglutination inhibition (HI) assays) and an independent test set (31,878 HI assays), are 91.2% and 96.2% respectively.
Conclusion
Our method is able to identify critical positions, rules, and co-mutated positions on HA for predicting the antigenic variants. The information gains and the entropies of HA positions provide insight to the antigenic drift and co-evolution positions for influenza seasons. We believe that our method is robust and is potential useful for studying influenza virus evolution and vaccine development.
doi:10.1186/1471-2105-10-S1-S41
PMCID: PMC2648776  PMID: 19208143
18.  Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD) 
Executive Summary
In July 2010, the Medical Advisory Secretariat (MAS) began work on a Chronic Obstructive Pulmonary Disease (COPD) evidentiary framework, an evidence-based review of the literature surrounding treatment strategies for patients with COPD. This project emerged from a request by the Health System Strategy Division of the Ministry of Health and Long-Term Care that MAS provide them with an evidentiary platform on the effectiveness and cost-effectiveness of COPD interventions.
After an initial review of health technology assessments and systematic reviews of COPD literature, and consultation with experts, MAS identified the following topics for analysis: vaccinations (influenza and pneumococcal), smoking cessation, multidisciplinary care, pulmonary rehabilitation, long-term oxygen therapy, noninvasive positive pressure ventilation for acute and chronic respiratory failure, hospital-at-home for acute exacerbations of COPD, and telehealth (including telemonitoring and telephone support). Evidence-based analyses were prepared for each of these topics. For each technology, an economic analysis was also completed where appropriate. In addition, a review of the qualitative literature on patient, caregiver, and provider perspectives on living and dying with COPD was conducted, as were reviews of the qualitative literature on each of the technologies included in these analyses.
The Chronic Obstructive Pulmonary Disease Mega-Analysis series is made up of the following reports, which can be publicly accessed at the MAS website at: http://www.hqontario.ca/en/mas/mas_ohtas_mn.html.
Chronic Obstructive Pulmonary Disease (COPD) Evidentiary Framework
Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Smoking Cessation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Community-Based Multidisciplinary Care for Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Pulmonary Rehabilitation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Long-term Oxygen Therapy for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Acute Respiratory Failure Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Chronic Respiratory Failure Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Hospital-at-Home Programs for Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Cost-Effectiveness of Interventions for Chronic Obstructive Pulmonary Disease Using an Ontario Policy Model
Experiences of Living and Dying With COPD: A Systematic Review and Synthesis of the Qualitative Empirical Literature
For more information on the qualitative review, please contact Mita Giacomini at: http://fhs.mcmaster.ca/ceb/faculty_member_giacomini.htm.
For more information on the economic analysis, please visit the PATH website: http://www.path-hta.ca/About-Us/Contact-Us.aspx.
The Toronto Health Economics and Technology Assessment (THETA) collaborative has produced an associated report on patient preference for mechanical ventilation. For more information, please visit the THETA website: http://theta.utoronto.ca/static/contact.
Objective
The objective of this analysis was to determine the effectiveness of the influenza vaccination and the pneumococcal vaccination in patients with chronic obstructive pulmonary disease (COPD) in reducing the incidence of influenza-related illness or pneumococcal pneumonia.
Clinical Need: Condition and Target Population
Influenza Disease
Influenza is a global threat. It is believed that the risk of a pandemic of influenza still exists. Three pandemics occurred in the 20th century which resulted in millions of deaths worldwide. The fourth pandemic of H1N1 influenza occurred in 2009 and affected countries in all continents.
Rates of serious illness due to influenza viruses are high among older people and patients with chronic conditions such as COPD. The influenza viruses spread from person to person through sneezing and coughing. Infected persons can transfer the virus even a day before their symptoms start. The incubation period is 1 to 4 days with a mean of 2 days. Symptoms of influenza infection include fever, shivering, dry cough, headache, runny or stuffy nose, muscle ache, and sore throat. Other symptoms such as nausea, vomiting, and diarrhea can occur.
Complications of influenza infection include viral pneumonia, secondary bacterial pneumonia, and other secondary bacterial infections such as bronchitis, sinusitis, and otitis media. In viral pneumonia, patients develop acute fever and dyspnea, and may further show signs and symptoms of hypoxia. The organisms involved in bacterial pneumonia are commonly identified as Staphylococcus aureus and Hemophilus influenza. The incidence of secondary bacterial pneumonia is most common in the elderly and those with underlying conditions such as congestive heart disease and chronic bronchitis.
Healthy people usually recover within one week but in very young or very old people and those with underlying medical conditions such as COPD, heart disease, diabetes, and cancer, influenza is associated with higher risks and may lead to hospitalization and in some cases death. The cause of hospitalization or death in many cases is viral pneumonia or secondary bacterial pneumonia. Influenza infection can lead to the exacerbation of COPD or an underlying heart disease.
Streptococcal Pneumonia
Streptococcus pneumoniae, also known as pneumococcus, is an encapsulated Gram-positive bacterium that often colonizes in the nasopharynx of healthy children and adults. Pneumococcus can be transmitted from person to person during close contact. The bacteria can cause illnesses such as otitis media and sinusitis, and may become more aggressive and affect other areas of the body such as the lungs, brain, joints, and blood stream. More severe infections caused by pneumococcus are pneumonia, bacterial sepsis, meningitis, peritonitis, arthritis, osteomyelitis, and in rare cases, endocarditis and pericarditis.
People with impaired immune systems are susceptible to pneumococcal infection. Young children, elderly people, patients with underlying medical conditions including chronic lung or heart disease, human immunodeficiency virus (HIV) infection, sickle cell disease, and people who have undergone a splenectomy are at a higher risk for acquiring pneumococcal pneumonia.
Technology
Influenza and Pneumococcal Vaccines
Trivalent Influenza Vaccines in Canada
In Canada, 5 trivalent influenza vaccines are currently authorized for use by injection. Four of these are formulated for intramuscular use and the fifth product (Intanza®) is formulated for intradermal use.
The 4 vaccines for intramuscular use are:
Fluviral (GlaxoSmithKline), split virus, inactivated vaccine, for use in adults and children ≥ 6 months;
Vaxigrip (Sanofi Pasteur), split virus inactivated vaccine, for use in adults and children ≥ 6 months;
Agriflu (Novartis), surface antigen inactivated vaccine, for use in adults and children ≥ 6 months; and
Influvac (Abbott), surface antigen inactivated vaccine, for use in persons ≥ 18 years of age.
FluMist is a live attenuated virus in the form of an intranasal spray for persons aged 2 to 59 years. Immunization with current available influenza vaccines is not recommended for infants less than 6 months of age.
Pneumococcal Vaccine
Pneumococcal polysaccharide vaccines were developed more than 50 years ago and have progressed from 2-valent vaccines to the current 23-valent vaccines to prevent diseases caused by 23 of the most common serotypes of S pneumoniae. Canada-wide estimates suggest that approximately 90% of cases of pneumococcal bacteremia and meningitis are caused by these 23 serotypes. Health Canada has issued licenses for 2 types of 23-valent vaccines to be injected intramuscularly or subcutaneously:
Pneumovax 23® (Merck & Co Inc. Whitehouse Station, NJ, USA), and
Pneumo 23® (Sanofi Pasteur SA, Lion, France) for persons 2 years of age and older.
Other types of pneumococcal vaccines licensed in Canada are for pediatric use. Pneumococcal polysaccharide vaccine is injected only once. A second dose is applied only in some conditions.
Research Questions
What is the effectiveness of the influenza vaccination and the pneumococcal vaccination compared with no vaccination in COPD patients?
What is the safety of these 2 vaccines in COPD patients?
What is the budget impact and cost-effectiveness of these 2 vaccines in COPD patients?
Research Methods
Literature search
Search Strategy
A literature search was performed on July 5, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from January 1, 2000 to July 5, 2010. The search was updated monthly through the AutoAlert function of the search up to January 31, 2011. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. Articles with an unknown eligibility were reviewed with a second clinical epidemiologist and then a group of epidemiologists until consensus was established. Data extraction was carried out by the author.
Inclusion Criteria
studies comparing clinical efficacy of the influenza vaccine or the pneumococcal vaccine with no vaccine or placebo;
randomized controlled trials published between January 1, 2000 and January 31, 2011;
studies including patients with COPD only;
studies investigating the efficacy of types of vaccines approved by Health Canada;
English language studies.
Exclusion Criteria
non-randomized controlled trials;
studies investigating vaccines for other diseases;
studies comparing different variations of vaccines;
studies in which patients received 2 or more types of vaccines;
studies comparing different routes of administering vaccines;
studies not reporting clinical efficacy of the vaccine or reporting immune response only;
studies investigating the efficacy of vaccines not approved by Health Canada.
Outcomes of Interest
Primary Outcomes
Influenza vaccination: Episodes of acute respiratory illness due to the influenza virus.
Pneumococcal vaccination: Time to the first episode of community-acquired pneumonia either due to pneumococcus or of unknown etiology.
Secondary Outcomes
rate of hospitalization and mechanical ventilation
mortality rate
adverse events
Quality of Evidence
The quality of each included study was assessed taking into consideration allocation concealment, randomization, blinding, power/sample size, withdrawals/dropouts, and intention-to-treat analyses. The quality of the body of evidence was assessed as high, moderate, low, or very low according to the GRADE Working Group criteria. The following definitions of quality were used in grading the quality of the evidence:
Summary of Efficacy of the Influenza Vaccination in Immunocompetent Patients With COPD
Clinical Effectiveness
The influenza vaccination was associated with significantly fewer episodes of influenza-related acute respiratory illness (ARI). The incidence density of influenza-related ARI was:
All patients: vaccine group: (total of 4 cases) = 6.8 episodes per 100 person-years; placebo group: (total of 17 cases) = 28.1 episodes per 100 person-years, (relative risk [RR], 0.2; 95% confidence interval [CI], 0.06−0.70; P = 0.005).
Patients with severe airflow obstruction (forced expiratory volume in 1 second [FEV1] < 50% predicted): vaccine group: (total of 1 case) = 4.6 episodes per 100 person-years; placebo group: (total of 7 cases) = 31.2 episodes per 100 person-years, (RR, 0.1; 95% CI, 0.003−1.1; P = 0.04).
Patients with moderate airflow obstruction (FEV1 50%−69% predicted): vaccine group: (total of 2 cases) = 13.2 episodes per 100 person-years; placebo group: (total of 4 cases) = 23.8 episodes per 100 person-years, (RR, 0.5; 95% CI, 0.05−3.8; P = 0.5).
Patients with mild airflow obstruction (FEV1 ≥ 70% predicted): vaccine group: (total of 1 case) = 4.5 episodes per 100 person-years; placebo group: (total of 6 cases) = 28.2 episodes per 100 person-years, (RR, 0.2; 95% CI, 0.003−1.3; P = 0.06).
The Kaplan-Meier survival analysis showed a significant difference between the vaccinated group and the placebo group regarding the probability of not acquiring influenza-related ARI (log-rank test P value = 0.003). Overall, the vaccine effectiveness was 76%. For categories of mild, moderate, or severe COPD the vaccine effectiveness was 84%, 45%, and 85% respectively.
With respect to hospitalization, fewer patients in the vaccine group compared with the placebo group were hospitalized due to influenza-related ARIs, although these differences were not statistically significant. The incidence density of influenza-related ARIs that required hospitalization was 3.4 episodes per 100 person-years in the vaccine group and 8.3 episodes per 100 person-years in the placebo group (RR, 0.4; 95% CI, 0.04−2.5; P = 0.3; log-rank test P value = 0.2). Also, no statistically significant differences between the 2 groups were observed for the 3 categories of severity of COPD.
Fewer patients in the vaccine group compared with the placebo group required mechanical ventilation due to influenza-related ARIs. However, these differences were not statistically significant. The incidence density of influenza-related ARIs that required mechanical ventilation was 0 episodes per 100 person-years in the vaccine group and 5 episodes per 100 person-years in the placebo group (RR, 0.0; 95% CI, 0−2.5; P = 0.1; log-rank test P value = 0.4). In addition, no statistically significant differences between the 2 groups were observed for the 3 categories of severity of COPD. The effectiveness of the influenza vaccine in preventing influenza-related ARIs and influenza-related hospitalization was not related to age, sex, severity of COPD, smoking status, or comorbid diseases.
safety
Overall, significantly more patients in the vaccine group than the placebo group experienced local adverse reactions (vaccine: 17 [27%], placebo: 4 [6%]; P = 0.002). Significantly more patients in the vaccine group than the placebo group experienced swelling (vaccine 4, placebo 0; P = 0.04) and itching (vaccine 4, placebo 0; P = 0.04). Systemic reactions included headache, myalgia, fever, and skin rash and there were no significant differences between the 2 groups for these reactions (vaccine: 47 [76%], placebo: 51 [81%], P = 0.5).
With respect to lung function, dyspneic symptoms, and exercise capacity, there were no significant differences between the 2 groups at 1 week and at 4 weeks in: FEV1, maximum inspiratory pressure at residual volume, oxygen saturation level of arterial blood, visual analogue scale for dyspneic symptoms, and the 6 Minute Walking Test for exercise capacity.
There was no significant difference between the 2 groups with regard to the probability of not acquiring total ARIs (influenza-related and/or non-influenza-related); (log-rank test P value = 0.6).
Summary of Efficacy of the Pneumococcal Vaccination in Immunocompetent Patients With COPD
Clinical Effectiveness
The Kaplan-Meier survival analysis showed no significant differences between the group receiving the penumoccocal vaccination and the control group for time to the first episode of community-acquired pneumonia due to pneumococcus or of unknown etiology (log-rank test 1.15; P = 0.28). Overall, vaccine efficacy was 24% (95% CI, −24 to 54; P = 0.33).
With respect to the incidence of pneumococcal pneumonia, the Kaplan-Meier survival analysis showed a significant difference between the 2 groups (vaccine: 0/298; control: 5/298; log-rank test 5.03; P = 0.03).
Hospital admission rates and median length of hospital stays were lower in the vaccine group, but the difference was not statistically significant. The mortality rate was not different between the 2 groups.
Subgroup Analysis
The Kaplan-Meier survival analysis showed significant differences between the vaccine and control groups for pneumonia due to pneumococcus and pneumonia of unknown etiology, and when data were analyzed according to subgroups of patients (age < 65 years, and severe airflow obstruction FEV1 < 40% predicted). The accumulated percentage of patients without pneumonia (due to pneumococcus and of unknown etiology) across time was significantly lower in the vaccine group than in the control group in patients younger than 65 years of age (log-rank test 6.68; P = 0.0097) and patients with a FEV1 less than 40% predicted (log-rank test 3.85; P = 0.0498).
Vaccine effectiveness was 76% (95% CI, 20−93; P = 0.01) for patients who were less than 65 years of age and −14% (95% CI, −107 to 38; P = 0.8) for those who were 65 years of age or older. Vaccine effectiveness for patients with a FEV1 less than 40% predicted and FEV1 greater than or equal to 40% predicted was 48% (95% CI, −7 to 80; P = 0.08) and −11% (95% CI, −132 to 47; P = 0.95), respectively. For patients who were less than 65 years of age (FEV1 < 40% predicted), vaccine effectiveness was 91% (95% CI, 35−99; P = 0.002).
Cox modelling showed that the effectiveness of the vaccine was dependent on the age of the patient. The vaccine was not effective in patients 65 years of age or older (hazard ratio, 1.53; 95% CI, 0.61−a2.17; P = 0.66) but it reduced the risk of acquiring pneumonia by 80% in patients less than 65 years of age (hazard ratio, 0.19; 95% CI, 0.06−0.66; P = 0.01).
safety
No patients reported any local or systemic adverse reactions to the vaccine.
PMCID: PMC3384373  PMID: 23074431
19.  Reducing the Impact of the Next Influenza Pandemic Using Household-Based Public Health Interventions 
PLoS Medicine  2006;3(9):e361.
Background
The outbreak of highly pathogenic H5N1 influenza in domestic poultry and wild birds has caused global concern over the possible evolution of a novel human strain [1]. If such a strain emerges, and is not controlled at source [2,3], a pandemic is likely to result. Health policy in most countries will then be focused on reducing morbidity and mortality.
Methods and Findings
We estimate the expected reduction in primary attack rates for different household-based interventions using a mathematical model of influenza transmission within and between households. We show that, for lower transmissibility strains [2,4], the combination of household-based quarantine, isolation of cases outside the household, and targeted prophylactic use of anti-virals will be highly effective and likely feasible across a range of plausible transmission scenarios. For example, for a basic reproductive number (the average number of people infected by a typically infectious individual in an otherwise susceptible population) of 1.8, assuming only 50% compliance, this combination could reduce the infection (symptomatic) attack rate from 74% (49%) to 40% (27%), requiring peak quarantine and isolation levels of 6.2% and 0.8% of the population, respectively, and an overall anti-viral stockpile of 3.9 doses per member of the population. Although contact tracing may be additionally effective, the resources required make it impractical in most scenarios.
Conclusions
National influenza pandemic preparedness plans currently focus on reducing the impact associated with a constant attack rate, rather than on reducing transmission. Our findings suggest that the additional benefits and resource requirements of household-based interventions in reducing average levels of transmission should also be considered, even when expected levels of compliance are only moderate.
Voluntary household-based quarantine and external isolation are likely to be effective in limiting the morbidity and mortality of an influenza pandemic, even if such a pandemic cannot be entirely prevented, and even if compliance with these interventions is moderate.
Editors' Summary
Background.
Naturally occurring variation in the influenza virus can lead both to localized annual epidemics and to less frequent global pandemics of catastrophic proportions. The most destructive of the three influenza pandemics of the 20th century, the so-called Spanish flu of 1918–1919, is estimated to have caused 20 million deaths. As evidenced by ongoing tracking efforts and news media coverage of H5N1 avian influenza, contemporary approaches to monitoring and communications can be expected to alert health officials and the general public of the emergence of new, potentially pandemic strains before they spread globally.
Why Was This Study Done?
In order to act most effectively on advance notice of an approaching influenza pandemic, public health workers need to know which available interventions are likely to be most effective. This study was done to estimate the effectiveness of specific preventive measures that communities might implement to reduce the impact of pandemic flu. In particular, the study evaluates methods to reduce person-to-person transmission of influenza, in the likely scenario that complete control cannot be achieved by mass vaccination and anti-viral treatment alone.
What Did the Researchers Do and Find?
The researchers developed a mathematical model—essentially a computer simulation—to simulate the course of pandemic influenza in a hypothetical population at risk for infection at home, through external peer networks such as schools and workplaces, and through general community transmission. Parameters such as the distribution of household sizes, the rate at which individuals develop symptoms from nonpandemic viruses, and the risk of infection within households were derived from demographic and epidemiologic data from Hong Kong, as well as empirical studies of influenza transmission. A model based on these parameters was then used to calculate the effects of interventions including voluntary household quarantine, voluntary individual isolation in a facility outside the home, and contact tracing (that is, asking infectious individuals to identify people whom they may have infected and then warning those people) on the spread of pandemic influenza through the population. The model also took into account the anti-viral treatment of exposed, asymptomatic household members and of individuals in isolation, and assumed that all intervention strategies were put into place before the arrival of individuals infected with the pandemic virus.
  Using this model, the authors predicted that even if only half of the population were to comply with public health interventions, the proportion infected during the first year of an influenza pandemic could be substantially reduced by a combination of household-based quarantine, isolation of actively infected individuals in a location outside the household, and targeted prophylactic treatment of exposed individuals with anti-viral drugs. Based on an influenza-associated mortality rate of 0.5% (as has been estimated for New York City in the 1918–1919 pandemic), the magnitude of the predicted benefit of these interventions is a reduction from 49% to 27% in the proportion of the population who become ill in the first year of the pandemic, which would correspond to 16,000 fewer deaths in a city the size of Hong Kong (6.8 million people). In the model, anti-viral treatment appeared to be about as effective as isolation when each was used in combination with household quarantine, but would require stockpiling 3.9 doses of anti-viral for each member of the population. Contact tracing was predicted to provide a modest additional benefit over quarantine and isolation, but also to increase considerably the proportion of the population in quarantine.
What Do These Findings Mean?
This study predicts that voluntary household-based quarantine and external isolation can be effective in limiting the morbidity and mortality of an influenza pandemic, even if such a pandemic cannot be entirely prevented, and even if compliance with these interventions is far from uniform. These simulations can therefore inform preparedness plans in the absence of data from actual intervention trials, which would be impossible outside (and impractical within) the context of an actual pandemic. Like all mathematical models, however, the one presented in this study relies on a number of assumptions regarding the characteristics and circumstances of the situation that it is intended to represent. For example, the authors found that the efficacy of policies to reduce the rate of infection vary according to the ease with which a given virus spreads from person to person. Because this parameter (known as the basic reproductive ratio, R0) cannot be reliably predicted for a new viral strain based on past epidemics, the authors note that in an actual influenza pandemic rapid determinations of R0 in areas already involved would be necessary to finalize public health responses in threatened areas. Further, the implementation of the interventions that appear beneficial in this model would require devoting attention and resources to practical considerations, such as how to staff isolation centers and provide food and water to those in household quarantine. However accurate the scientific data and predictive models may be, their effectiveness can only be realized through well-coordinated local, as well as international, efforts.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030361.
• World Health Organization influenza pandemic preparedness page
• US Department of Health and Human Services avian and pandemic flu information site
• Pandemic influenza page from the Public Health Agency of Canada
• Emergency planning page on pandemic flu from the England Department of Health
• Wikipedia entry on pandemic influenza with links to individual country resources (note: Wikipedia is a free Internet encyclopedia that anyone can edit)
doi:10.1371/journal.pmed.0030361
PMCID: PMC1526768  PMID: 16881729
20.  Network Analysis of Global Influenza Spread 
PLoS Computational Biology  2010;6(11):e1001005.
Although vaccines pose the best means of preventing influenza infection, strain selection and optimal implementation remain difficult due to antigenic drift and a lack of understanding global spread. Detecting viral movement by sequence analysis is complicated by skewed geographic and seasonal distributions in viral isolates. We propose a probabilistic method that accounts for sampling bias through spatiotemporal clustering and modeling regional and seasonal transmission as a binomial process. Analysis of H3N2 not only confirmed East-Southeast Asia as a source of new seasonal variants, but also increased the resolution of observed transmission to a country level. H1N1 data revealed similar viral spread from the tropics. Network analysis suggested China and Hong Kong as the origins of new seasonal H3N2 strains and the United States as a region where increased vaccination would maximally disrupt global spread of the virus. These techniques provide a promising methodology for the analysis of any seasonal virus, as well as for the continued surveillance of influenza.
Author Summary
As evidenced by several historic vaccine failures, the design and implementation of the influenza vaccine remains an imperfect science. The virus's rapid rate of evolution makes the selection of representative strains for vaccine composition a difficult process. From a global health viewpoint, how to optimally implement a limited stockpile of vaccines is another fundamental question that remains unanswered. An understanding of how influenza spreads around the world would greatly aid the design and implementation process, but regional and seasonal bias in collected virus samples hampers epidemiologic analysis. Here, we show that it is possible to counter this data bias through probabilistic modeling and represent the global viral spread as a network of seeding events between different regions of the world. On a local scale, our technique can output the most likely origins of a virus circulating in a given location. On a global scale, we can pinpoint regions of the world that would maximally disrupt viral transmission with an increase in vaccine implementation. We demonstrate our method on seasonal H3N2 and H1N1 and foresee similar application to other seasonal viruses, including swine-origin H1N1, once more seasonal data is collected.
doi:10.1371/journal.pcbi.1001005
PMCID: PMC2987833  PMID: 21124942
21.  Evolution of an Eurasian Avian-like Influenza Virus in Naïve and Vaccinated Pigs 
PLoS Pathogens  2012;8(5):e1002730.
Influenza viruses are characterized by an ability to cross species boundaries and evade host immunity, sometimes with devastating consequences. The 2009 pandemic of H1N1 influenza A virus highlights the importance of pigs in influenza emergence, particularly as intermediate hosts by which avian viruses adapt to mammals before emerging in humans. Although segment reassortment has commonly been associated with influenza emergence, an expanded host-range is also likely to be associated with the accumulation of specific beneficial point mutations. To better understand the mechanisms that shape the genetic diversity of avian-like viruses in pigs, we studied the evolutionary dynamics of an Eurasian Avian-like swine influenza virus (EA-SIV) in naïve and vaccinated pigs linked by natural transmission. We analyzed multiple clones of the hemagglutinin 1 (HA1) gene derived from consecutive daily viral populations. Strikingly, we observed both transient and fixed changes in the consensus sequence along the transmission chain. Hence, the mutational spectrum of intra-host EA-SIV populations is highly dynamic and allele fixation can occur with extreme rapidity. In addition, mutations that could potentially alter host-range and antigenicity were transmitted between animals and mixed infections were commonplace, even in vaccinated pigs. Finally, we repeatedly detected distinct stop codons in virus samples from co-housed pigs, suggesting that they persisted within hosts and were transmitted among them. This implies that mutations that reduce viral fitness in one host, but which could lead to fitness benefits in a novel host, can circulate at low frequencies.
Author Summary
The latest human influenza pandemic highlights the ability of influenza viruses to jump species barriers and emerge in new hosts, as well as the role of pigs in generating viruses with pandemic potential. The mutational power of influenza virus, caused by intrinsically error-prone viral polymerases, has been directly linked to viral emergence, as adaptive mutations present in the reservoir host are likely to be key to the evolution of sustained transmission in new hosts. Hence, studying how mutations are generated, maintained and transmitted in and among pigs is critical to understanding how novel viruses could emerge. Here we characterized the evolution and mutational spectra of influenza virus populations within naïve and vaccinated pigs linked by natural transmission, by analyzing multiple viral sequences obtained at different times post-infection. We show that the genetic make-up of influenza viruses in pigs is highly dynamic: the frequency of particular mutations, including those that could potentially alter host specificity or result in vaccine escape, fluctuated markedly, including one rapid fixation event. We also show that co-infections are common and multiple viruses – even defective ones – were transmitted between pigs despite being vaccinated. Our results provide empirical evidence of the complex dynamics of influenza viral populations in pigs and provide insight on the evolutionary basis of RNA viral emergence.
doi:10.1371/journal.ppat.1002730
PMCID: PMC3364949  PMID: 22693449
22.  The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin 
eLife  2014;3:e03300.
Influenza is notable for its evolutionary capacity to escape immunity targeting the viral hemagglutinin. We used deep mutational scanning to examine the extent to which a high inherent mutational tolerance contributes to this antigenic evolvability. We created mutant viruses that incorporate most of the ≈104 amino-acid mutations to hemagglutinin from A/WSN/1933 (H1N1) influenza. After passaging these viruses in tissue culture to select for functional variants, we used deep sequencing to quantify mutation frequencies before and after selection. These data enable us to infer the preference for each amino acid at each site in hemagglutinin. These inferences are consistent with existing knowledge about the protein's structure and function, and can be used to create a model that describes hemagglutinin's evolution far better than existing phylogenetic models. We show that hemagglutinin has a high inherent tolerance for mutations at antigenic sites, suggesting that this is one factor contributing to influenza's antigenic evolution.
DOI: http://dx.doi.org/10.7554/eLife.03300.001
eLife digest
Influenza is a major threat to human health largely because the flu virus evolves rapidly to escape recognition by the immune system. These ongoing changes also explain why flu vaccines become less effective over time and need to be reformulated every year.
Hemagglutinin is a protein on the surface of the flu virus that helps the virus bind to and infect host cells. The surface proteins of most viruses are recognized by the immune system, and influenza hemagglutinin is no exception. However, hemagglutinin is unusual in that it evolves exceptionally rapidly to avoid being recognized by the immune system. This raises an important question: what is it about the influenza hemagglutinin protein that allows it to change so readily?
Thyagarajan and Bloom address this question by making mutant copies of the gene that encodes the hemagglutinin protein. There are over 10,000 ways in which the protein can be mutated, and Thyagarajan and Bloom managed to make the vast majority of the possible changes. The mutated genes were then re-introduced into the virus, and the mutant viruses were allowed to replicate in cells for several generations.
Thyagarajan and Bloom sequenced the viruses that had replicated—which meant that the mutant copies of the hemagglutinin protein in these viruses still worked—and looked to see where in the protein the changes had occurred. Those regions that rarely changed included the part of the protein that binds to host cells, whereas other regions—especially those that are recognized by the immune system—were much more likely to contain mutations. Thyagarajan and Bloom then went on to show that not all influenza proteins share hemaglutinin's capacity to change the regions targeted by the immune system, suggesting that this capacity is possibly a unique feature of this protein.
Thyagarajan and Bloom also suggest that this capacity to tolerate mutations in parts of proteins that are recognized by the immune system might be important for shaping a virus's ability to evolve to escape this recognition. Future work is now needed to see how tolerant to mutations other viral proteins are, and to reveal which properties of a protein determine its tolerance to mutations.
DOI: http://dx.doi.org/10.7554/eLife.03300.002
doi:10.7554/eLife.03300
PMCID: PMC4109307  PMID: 25006036
influenza; hemagglutinin; phylogenetics; evolvability; antigenic evolution; deep mutational scanning; viruses
23.  The Impact of Matching Vaccine Strains and Post-SARS Public Health Efforts on Reducing Influenza-Associated Mortality among the Elderly 
PLoS ONE  2010;5(6):e11317.
Public health administrators do not have effective models to predict excess influenza-associated mortality and monitor viral changes associated with it. This study evaluated the effect of matching/mismatching vaccine strains, type/subtype pattern changes in Taiwan's influenza viruses, and the impact of post-SARS (severe acute respiratory syndrome) public health efforts on excess influenza-associated mortalities among the elderly. A negative binomial model was developed to estimate Taiwan's monthly influenza-associated mortality among the elderly. We calculated three winter and annual excess influenza-associated mortalities [pneumonia and influenza (P&I), respiratory and circulatory, and all-cause] from the 1999–2000 through the 2006–2007 influenza seasons. Obtaining influenza virus sequences from the months/years in which death from P&I was excessive, we investigated molecular variation in vaccine-mismatched influenza viruses by comparing hemagglutinin 1 (HA1) of the circulating and vaccine strains. We found that the higher the isolation rate of A (H3N2) and vaccine-mismatched influenza viruses, the greater the monthly P&I mortality. However, this significant positive association became negative for higher matching of A (H3N2) and public health efforts with post-SARS effect. Mean excess P&I mortality for winters was significantly higher before 2003 than after that year [mean ± S.D.: 1.44±1.35 vs. 0.35±1.13, p = 0.04]. Further analysis revealed that vaccine-matched circulating influenza A viruses were significantly associated with lower excess P&I mortality during post-SARS winters (i.e., 2005–2007) than during pre-SARS winters [0.03±0.06 vs. 1.57±1.27, p = 0.01]. Stratification of these vaccine-matching and post-SARS effect showed substantial trends toward lower elderly excess P&I mortalities in winters with either mismatching vaccines during the post-SARS period or matching vaccines during the pre-SARS period. Importantly, all three excess mortalities were at their highest in May, 2003, when inter-hospital nosocomial infections were peaking. Furthermore, vaccine-mismatched H3N2 viruses circulating in the years with high excess P&I mortality exhibited both a lower amino acid identity percentage of HA1 between vaccine and circulating strains and a higher numbers of variations at epitope B. Our model can help future decision makers to estimate excess P&I mortality effectively, select and test virus strains for antigenic variation, and evaluate public health strategy effectiveness.
doi:10.1371/journal.pone.0011317
PMCID: PMC2892467  PMID: 20592764
24.  Monitoring the Impact of Influenza by Age: Emergency Department Fever and Respiratory Complaint Surveillance in New York City 
PLoS Medicine  2007;4(8):e247.
Background
The importance of understanding age when estimating the impact of influenza on hospitalizations and deaths has been well described, yet existing surveillance systems have not made adequate use of age-specific data. Monitoring influenza-related morbidity using electronic health data may provide timely and detailed insight into the age-specific course, impact and epidemiology of seasonal drift and reassortment epidemic viruses. The purpose of this study was to evaluate the use of emergency department (ED) chief complaint data for measuring influenza-attributable morbidity by age and by predominant circulating virus.
Methods and Findings
We analyzed electronically reported ED fever and respiratory chief complaint and viral surveillance data in New York City (NYC) during the 2001–2002 through 2005–2006 influenza seasons, and inferred dominant circulating viruses from national surveillance reports. We estimated influenza-attributable impact as observed visits in excess of a model-predicted baseline during influenza periods, and epidemic timing by threshold and cross correlation. We found excess fever and respiratory ED visits occurred predominantly among school-aged children (8.5 excess ED visits per 1,000 children aged 5–17 y) with little or no impact on adults during the early-2002 B/Victoria-lineage epidemic; increased fever and respiratory ED visits among children younger than 5 y during respiratory syncytial virus-predominant periods preceding epidemic influenza; and excess ED visits across all ages during the 2003–2004 (9.2 excess visits per 1,000 population) and 2004–2005 (5.2 excess visits per 1,000 population) A/H3N2 Fujian-lineage epidemics, with the relative impact shifted within and between seasons from younger to older ages. During each influenza epidemic period in the study, ED visits were increased among school-aged children, and each epidemic peaked among school-aged children before other impacted age groups.
Conclusions
Influenza-related morbidity in NYC was highly age- and strain-specific. The impact of reemerging B/Victoria-lineage influenza was focused primarily on school-aged children born since the virus was last widespread in the US, while epidemic A/Fujian-lineage influenza affected all age groups, consistent with a novel antigenic variant. The correspondence between predominant circulating viruses and excess ED visits, hospitalizations, and deaths shows that excess fever and respiratory ED visits provide a reliable surrogate measure of incident influenza-attributable morbidity. The highly age-specific impact of influenza by subtype and strain suggests that greater age detail be incorporated into ongoing surveillance. Influenza morbidity surveillance using electronic data currently available in many jurisdictions can provide timely and representative information about the age-specific epidemiology of circulating influenza viruses.
Don Olson and colleagues report that influenza-related morbidity in NYC from 2001 to 2006 was highly age- and strain-specific and conclude that surveillance using electronic data can provide timely and representative information about the epidemiology of circulating influenza viruses.
Editors' Summary
Background.
Seasonal outbreaks (epidemics) of influenza (a viral infection of the nose, throat, and airways) send millions of people to their beds every winter. Most recover quickly, but flu epidemics often disrupt daily life and can cause many deaths. Seasonal epidemics occur because influenza viruses continually make small changes to the viral proteins (antigens) that the human immune system recognizes. Consequently, an immune response that combats influenza one year may provide partial or no protection the following year. Occasionally, an influenza virus with large antigenic changes emerges that triggers an influenza pandemic, or global epidemic. To help prepare for both seasonal epidemics and pandemics, public-health officials monitor influenza-related illness and death, investigate unusual outbreaks of respiratory diseases, and characterize circulating strains of the influenza virus. While traditional influenza-related illness surveillance systems rely on relatively slow voluntary clinician reporting of cases with influenza-like illness symptoms, some jurisdictions have also started to use “syndromic” surveillance systems. These use electronic health-related data rather than clinical impression to track illness in the community. For example, increased visits to emergency departments for fever or respiratory (breathing) problems can provide an early warning of an influenza outbreak.
Why Was This Study Done?
Rapid illness surveillance systems have been shown to detect flu outbreaks earlier than is possible through monitoring deaths from pneumonia or influenza. Increases in visits to emergency departments by children for fever or respiratory problems can provide an even earlier indicator. Researchers have not previously examined in detail how fever and respiratory problems by age group correlate with the predominant circulating respiratory viruses. Knowing details like this would help public-health officials detect and respond to influenza epidemics and pandemics. In this study, the researchers have used data collected between 2001 and 2006 in New York City emergency departments to investigate these aspects of syndromic surveillance for influenza.
What Did the Researchers Do and Find?
The researchers analyzed emergency department visits categorized broadly into a fever and respiratory syndrome (which provides an estimate of the total visits attributable to influenza) or more narrowly into an influenza-like illness syndrome (which specifically indicates fever with cough and/or sore throat) with laboratory-confirmed influenza surveillance data. They found that emergency department visits were highest during peak influenza periods, and that the affect on different age groups varied depending on the predominant circulating viruses. In early 2002, an epidemic reemergence of B/Victoria-lineage influenza viruses caused increased visits among school-aged children, while adult visits did not increase. By contrast, during the 2003–2004 season, when the predominant virus was an A/H3N2 Fujian-lineage influenza virus, excess visits occurred in all age groups, though the relative increase was greatest and earliest among school-aged children. During periods of documented respiratory syncytial virus (RSV) circulation, increases in fever and respiratory emergency department visits occurred in children under five years of age regardless of influenza circulation. Finally, the researchers found that excess visits to emergency departments for fever and respiratory symptoms preceded deaths from pneumonia or influenza by about two weeks.
What Do These Findings Mean?
These findings indicate that excess emergency department visits for fever and respiratory symptoms can provide a reliable and timely surrogate measure of illness due to influenza. They also provide new insights into how different influenza viruses affect people of different ages and how the timing and progression of each influenza season differs. These results, based on data collected over only five years in one city, might not be generalizable to other settings or years, warn the researchers. However, the present results strongly suggest that the routine monitoring of influenza might be improved by using electronic health-related data, such as emergency department visit data, and by examining it specifically by age group. Furthermore, by showing that school-aged children can be the first people to be affected by seasonal influenza, these results highlight the important role this age group plays in community-wide transmission of influenza, an observation that could influence the implementation of public-health strategies such as vaccination that aim to protect communities during influenza epidemics and pandemics.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040247.
• US Centers for Disease Control and Prevention provides information on influenza for patients and health professionals and on influenza surveillance in the US (in English, Spanish, and several other languages)
• World Health Organization has a fact sheet on influenza and on global surveillance for influenza (in English, Spanish, French, Russian, Arabic, and Chinese)
• The MedlinePlus encyclopedia contains a page on flu (in English and Spanish)
• US National Institute of Allergy and Infectious Diseases has a feature called “focus on flu”
• A detailed report from the US Centers for Disease Control and Prevention titled “Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks” includes a simple description of syndromic surveillance
• The International Society for Disease Surveillance has a collaborative syndromic surveillance public wiki
• The Anthropology of the Contemporary Research Collaboratory includes working papers and discussions by cultural anthropologists studying modern vital systems security and syndromic surveillance
doi:10.1371/journal.pmed.0040247
PMCID: PMC1939858  PMID: 17683196
25.  Effects of vaccination and population structure on influenza epidemic spread in the presence of two circulating strains 
BMC Public Health  2011;11(Suppl 1):S8.
Background
Human influenza is characterized by seasonal epidemics, caused by rapid viral adaptation to population immunity. Vaccination against influenza must be updated annually, following surveillance of newly appearing viral strains. During an influenza season, several strains may be co-circulating, which will influence their individual evolution; furthermore, selective forces acting on the strains will be mediated by the transmission dynamics in the population. Clearly, viral evolution and public health policy are strongly interconnected. Understanding population-level dynamics of coexisting viral influenza infections, would be of great benefit in designing vaccination strategies.
Methods
We use a Markov network to extend a previous homogeneous model of two co-circulating influenza viral strains by including vaccination (either prior to or during an outbreak), age structure, and heterogeneity of the contact network. We explore the effects of changes in vaccination rate, cross-immunity, and delay in appearance of the second strain, on the size and timing of infection peaks, attack rates, and disease-induced mortality rate; and compare the outcomes of the network and corresponding homogeneous models.
Results
Pre-vaccination is more effective than vaccination during an outbreak, resulting in lower attack rates for the first strain but higher attack rates for the second strain, until a “threshold” vaccination level of ~30-40% is reached, after which attack rates due to both strains sharply dropped. A small increase in mortality was found for increasing pre-vaccination coverage below about 40%, due to increasing numbers of strain 2 infections. The amount of cross-immunity present determines whether a second wave of infection will occur. Some significant differences were found between the homogeneous and network models, including timing and height of peak infection(s).
Conclusions
Contact and age structure significantly influence the propagation of disease in the population. The present model explores only qualitative behaviour, based on parameters derived for homogeneous influenza models, but may be used for realistic populations through statistical estimates of inter-age contact patterns. This could have significant implications for vaccination strategies in realistic models of populations in which more than one strain is circulating.
doi:10.1186/1471-2458-11-S1-S8
PMCID: PMC3317581  PMID: 21356137

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