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1.  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
2.  Global Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study 
PLoS Medicine  2013;10(11):e1001558.
Lone Simonsen and colleagues use a two-stage statistical modeling approach to estimate the global mortality burden of the 2009 influenza pandemic from mortality data obtained from multiple countries.
Please see later in the article for the Editors' Summary
Background
Assessing the mortality impact of the 2009 influenza A H1N1 virus (H1N1pdm09) is essential for optimizing public health responses to future pandemics. The World Health Organization reported 18,631 laboratory-confirmed pandemic deaths, but the total pandemic mortality burden was substantially higher. We estimated the 2009 pandemic mortality burden through statistical modeling of mortality data from multiple countries.
Methods and Findings
We obtained weekly virology and underlying cause-of-death mortality time series for 2005–2009 for 20 countries covering ∼35% of the world population. We applied a multivariate linear regression model to estimate pandemic respiratory mortality in each collaborating country. We then used these results plus ten country indicators in a multiple imputation model to project the mortality burden in all world countries. Between 123,000 and 203,000 pandemic respiratory deaths were estimated globally for the last 9 mo of 2009. The majority (62%–85%) were attributed to persons under 65 y of age. We observed a striking regional heterogeneity, with almost 20-fold higher mortality in some countries in the Americas than in Europe. The model attributed 148,000–249,000 respiratory deaths to influenza in an average pre-pandemic season, with only 19% in persons <65 y. Limitations include lack of representation of low-income countries among single-country estimates and an inability to study subsequent pandemic waves (2010–2012).
Conclusions
We estimate that 2009 global pandemic respiratory mortality was ∼10-fold higher than the World Health Organization's laboratory-confirmed mortality count. Although the pandemic mortality estimate was similar in magnitude to that of seasonal influenza, a marked shift toward mortality among persons <65 y of age occurred, so that many more life-years were lost. The burden varied greatly among countries, corroborating early reports of far greater pandemic severity in the Americas than in Australia, New Zealand, and Europe. A collaborative network to collect and analyze mortality and hospitalization surveillance data is needed to rapidly establish the severity of future pandemics.
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—and hundreds of thousands of people (mainly elderly individuals) die as a result. These seasonal epidemics occur because small but frequent changes in the influenza virus mean that the immune response produced by infection with one year's virus provides only partial protection against the next year's virus. Influenza viruses also occasionally emerge that are very different. Human populations have virtually no immunity to these new viruses, which can start global epidemics (pandemics) that kill millions of people. The most recent influenza pandemic, which was first recognized in Mexico in March 2009, was caused by the 2009 influenza A H1N1 pandemic (H1N1pdm09) virus. This virus spread rapidly, and on 11 June 2009, the World Health Organization (WHO) declared that an influenza pandemic was underway. H1N1pdm09 caused a mild disease in most people it infected, but by the time WHO announced that the pandemic was over (10 August 2010), there had been 18,632 laboratory-confirmed deaths from H1N1pdm09.
Why Was This Study Done?
The modest number of laboratory-confirmed H1N1pdm09 deaths has caused commentators to wonder whether the public health response to H1N1pdm09 was excessive. However, as is the case with all influenza epidemics, the true mortality (death) burden from H1N1pdm09 is substantially higher than these figures indicate because only a minority of influenza-related deaths are definitively diagnosed by being confirmed in laboratory. Many influenza-related deaths result from secondary bacterial infections or from exacerbation of preexisting chronic conditions, and are not recorded as related to influenza infection. A more complete assessment of the impact of H1N1pdm09 on mortality is essential for the optimization of public health responses to future pandemics. In this modeling study (the Global Pandemic Mortality [GLaMOR] project), researchers use a two-stage statistical modeling approach to estimate the global mortality burden of the 2009 influenza pandemic from mortality data obtained from multiple countries.
What Did the Researchers Do and Find?
The researchers obtained weekly virology data from the World Health Organization FluNet database and national influenza centers to identify influenza active periods, and obtained weekly national underlying cause-of-death time series for 2005–2009 from collaborators in more than 20 countries (35% of the world's population). They used a multivariate linear regression model to measure the numbers and rates of pandemic influenza respiratory deaths in each of these countries. Then, in the second stage of their analysis, they used a multiple imputation model that took into account country-specific geographical, economic, and health indicators to project the single-country estimates to all world countries. The researchers estimated that between 123,000 and 203,000 pandemic influenza respiratory deaths occurred globally from 1 April through 31 December 2009. Most of these deaths (62%–85%) occurred in people younger than 65 years old. There was a striking regional heterogeneity in deaths, with up to 20-fold higher mortality in Central and South American countries than in European countries. Finally, the model attributed 148,000–249,000 respiratory deaths to influenza in an average pre-pandemic season. Notably, only 19% of these deaths occurred in people younger than 65 years old.
What Do These Findings Mean?
These findings suggest that respiratory mortality from the 2009 influenza pandemic was about 10-fold higher than laboratory-confirmed mortality. The true total mortality burden is likely to be even higher because deaths that occurred late in the winter of 2009–2010 and in later pandemic waves were missed in this analysis, and only pandemic influenza deaths that were recorded as respiratory deaths were included. The lack of single-country estimates from low-income countries may also limit the accuracy of these findings. Importantly, although the researchers' estimates of mortality from H1N1pdm09 and from seasonal influenza were of similar magnitude, the shift towards mortality among younger people means that more life-years were lost during the 2009 influenza pandemic than during an average pre-pandemic influenza season. Although the methods developed by the GLaMOR project can be used to make robust and comparable mortality estimates in future influenza pandemics, the lack of timeliness of such estimates needs to be remedied. One potential remedy, suggest the researchers, would be to establish a collaborative network that analyzes timely hospitalization and/or mortality data provided by sentinel countries. Such a network should be able to provide the rapid and reliable data about the severity of pandemic threats that is needed to guide public health policy decisions.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001558.
The US Centers for Disease Control and Prevention provides information about influenza for patients and professionals, including archived information on H1N1pdm09
Flu.gov, a US government website, provides access to information on seasonal and pandemic influenza H1N1pdm09
The World Health Organization provides information on influenza and on the global response to H1N1pdm09, including a publication on the evolution of H1N1pdm09 (some information in several languages). Information on FluNet, a global tool for influenza surveillance, is also available
Public Health England provides information on pandemic influenza and archived information on H1N1pdm09
More information for patients about H1N1pdm09 is available through Choices, an information resource provided by the UK National Health Service
More information about the GLaMOR project is available
doi:10.1371/journal.pmed.1001558
PMCID: PMC3841239  PMID: 24302890
3.  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
4.  Surveillance recommendations based on an exploratory analysis of respiratory syncytial virus reports derived from the European Influenza Surveillance System 
Background
Respiratory syncytial virus (RSV) is an important pathogen that can cause severe illness in infants and young children. In this study, we assessed whether data on RSV collected by the European Influenza Surveillance Scheme (EISS) could be used to build an RSV surveillance system in Europe.
Methods
Influenza and RSV data for the 2002–2003 winter season were analysed for England, France, the Netherlands and Scotland. Data from sentinel physician networks and other sources, mainly hospitals, were collected. Respiratory specimens were tested for influenza and RSV mainly by virus culture and polymerase chain reaction amplification.
Results
Data on RSV were entered timely into the EISS database. RSV contributed noticeably to influenza-like illness: in England sentinel RSV detections were common in all age groups, but particularly in young children with 20 (40.8%) of the total number of sentinel swabs testing positive for RSV. Scotland and France also reported the highest percentages of RSV detections in the 0–4 year age group, respectively 10.3% (N = 29) and 12.2% (N = 426). In the Netherlands, RSV was detected in one person aged over 65 years.
Conclusion
We recommend that respiratory specimens collected in influenza surveillance are also tested systematically for RSV and emphasize the use of both community derived data and data from hospitals for RSV surveillance. RSV data from the EISS have been entered in a timely manner and we consider that the EISS model can be used to develop an RSV surveillance system equivalent to the influenza surveillance in Europe.
doi:10.1186/1471-2334-6-128
PMCID: PMC1560143  PMID: 16899110
5.  Influenza activity in Europe during eight seasons (1999–2007): an evaluation of the indicators used to measure activity and an assessment of the timing, length and course of peak activity (spread) across Europe 
Background
The European Influenza Surveillance Scheme (EISS) has collected clinical and virological data on influenza since 1996 in an increasing number of countries. The EISS dataset was used to characterise important epidemiological features of influenza activity in Europe during eight winters (1999–2007). The following questions were addressed: 1) are the sentinel clinical reports a good measure of influenza activity? 2) how long is a typical influenza season in Europe? 3) is there a west-east and/or south-north course of peak activity ('spread') of influenza in Europe?
Methods
Influenza activity was measured by collecting data from sentinel general practitioners (GPs) and reports by national reference laboratories. The sentinel reports were first evaluated by comparing them to the laboratory reports and were then used to assess the timing and spread of influenza activity across Europe during eight seasons.
Results
We found a good match between the clinical sentinel data and laboratory reports of influenza collected by sentinel physicians (overall match of 72% for +/- 1 week difference). We also found a moderate to good match between the clinical sentinel data and laboratory reports of influenza from non-sentinel sources (overall match of 60% for +/- 1 week). There were no statistically significant differences between countries using ILI (influenza-like illness) or ARI (acute respiratory disease) as case definition. When looking at the peak-weeks of clinical activity, the average length of an influenza season in Europe was 15.6 weeks (median 15 weeks; range 12–19 weeks). Plotting the peak weeks of clinical influenza activity reported by sentinel GPs against the longitude or latitude of each country indicated that there was a west-east spread of peak activity (spread) of influenza across Europe in four winters (2001–2002, 2002–2003, 2003–2004 and 2004–2005) and a south-north spread in three winters (2001–2002, 2004–2005 and 2006–2007).
Conclusion
We found that: 1) the clinical data reported by sentinel physicians is a valid indicator of influenza activity; 2) the length of influenza activity across the whole of Europe was surprisingly long, ranging from 12–19 weeks; 3) in 4 out of the 8 seasons, there was a west-east spread of influenza, in 3 seasons a south-north spread; not associated with type of dominant virus in those seasons.
doi:10.1186/1471-2334-7-141
PMCID: PMC2216029  PMID: 18047685
6.  Contribution of respiratory pathogens to influenza-like illness consultations 
Epidemiology and Infection  2012;141(10):2196-2204.
SUMMARY
Influenza-like illnesses (ILIs) are caused by several respiratory pathogens. These pathogens show weak to strong seasonal activity implying seasonality in ILI consultations. In this paper, the contribution of pathogens to seasonality of ILI consultations was statistically modelled. Virological count data were first smoothed using modulation models for seasonal time series. Second, Poisson regression was used regressing ILI consultation counts on the smoothed time series. Using ratios of the estimated regression parameters, relative measures of the underreporting of pathogens were obtained. Influenza viruses A and B, parainfluenza virus and respiratory syncytial virus (RSV) significantly contributed to explain the seasonal variation in ILI consultations. We also found that RSV was the least and influenza virus A is the most underreported pathogen in Belgian laboratory surveillance. The proposed methods and results are helpful in interpreting the data of clinical and laboratory surveillance, which are the essential parts of influenza surveillance.
doi:10.1017/S0950268812002506
PMCID: PMC3757921  PMID: 23217849
Infectious disease epidemiology; influenza; statistics; surveillance system
7.  Emergency department surveillance as a proxy for the prediction of circulating respiratory viral disease in Eastern Ontario 
BACKGROUND:
Seasonal outbreaks of winter respiratory viruses are responsible for increases in morbidity and mortality in the community. Previous studies have used hospitalizations, intensive care unit and emergency department (ED) visits as indicators of seasonal influenza incidence.
OBJECTIVES:
To evaluate whether ED visits can be used as a proxy to detect respiratory viral disease outbreaks, as measured by laboratory confirmation.
METHODS:
An Emergency Department Syndromic Surveillance system was used to collect ED chief complaints in Eastern Ontario from 2006 to 2010. Comparable laboratory-confirmed cases of respiratory viral infections were collected from the Public Health Ontario Laboratory in Kingston, Ontario. Correlations between ED visits and laboratory-confirmed cases were calculated.
RESULTS:
Laboratory-confirmed cases of selected respiratory viruses were significantly correlated with ED visits for respiratory and fever/influenza-like illness. In particular, respiratory syncytial virus (Spearman’s rho = 0.593), rhinovirus (Spearman’s rho = 0.280), influenza A (Spearman’s rho = 0.528), influenza B (Spearman’s rho = 0.426) and pH1N1 (Spearman’s rho = 0.470) increased laboratory test levels were correlated with increased volume of ED visits across a number of age demographics. For the entire study population and all studied viruses, the Spearman’s rho was 0.702, suggesting a strong correlation with ED visits. Laboratory-confirmed cases lagged in reporting by between one and two weeks for influenza A and pH1N1 compared with ED visit volume.
CONCLUSION:
These findings support the use of an Emergency Department Syndromic Surveillance system to track the incidence of respiratory viral disease in the community. These methods are efficient and can be performed using automated electronic data entry versus the inherent delays in the primary care sentinel surveillance system, and can aid the timely implementation of preventive and preparatory health interventions.
PMCID: PMC3852452  PMID: 24421826
Emergency department surveillance; Respiratory; Syndromic surveillance; Viral disease
8.  Influenza Surveillance among Outpatients and Inpatients in Morocco, 1996–2009 
PLoS ONE  2011;6(9):e24579.
Background
There is limited information about the epidemiology of influenza in Africa. We describe the epidemiology and seasonality of influenza in Morocco from 1996 to 2009 with particular emphasis on the 2007–2008 and 2008–2009 influenza seasons. Successes and challenges of the enhanced surveillance system introduced in 2007 are also discussed.
Methods
Virologic sentinel surveillance for influenza virus was initiated in Morocco in 1996 using a network of private practitioners that collected oro-pharyngeal and naso-pharyngeal swabs from outpatients presenting with influenza-like-illness (ILI). The surveillance network expanded over the years to include inpatients presenting with severe acute respiratory illness (SARI) at hospitals and syndromic surveillance for ILI and acute respiratory infection (ARI). Respiratory samples and structured questionnaires were collected from eligible patients, and samples were tested by immunofluorescence assays and by viral isolation for influenza viruses.
Results
We obtained a total of 6465 respiratory specimens during 1996 to 2009, of which, 3102 were collected during 2007–2009. Of those, 2249 (72%) were from patients with ILI, and 853 (27%) were from patients with SARI. Among the 3,102 patients, 98 (3%) had laboratory-confirmed influenza, of whom, 85 (87%) had ILI and 13 (13%) had SARI. Among ILI patients, the highest proportion of laboratory-confirmed influenza occurred in children less than 5 years of age (3/169; 2% during 2007–2008 and 23/271; 9% during 2008–2009) and patients 25–59 years of age (8/440; 2% during 2007–2009 and 21/483; 4% during 2008–2009). All SARI patients with influenza were less than 14 years of age. During all surveillance years, influenza virus circulation was seasonal with peak circulation during the winter months of October through April.
Conclusion
Influenza results in both mild and severe respiratory infections in Morocco, and accounted for a large proportion of all hospitalizations for severe respiratory illness among children 5 years of age and younger.
doi:10.1371/journal.pone.0024579
PMCID: PMC3169614  PMID: 21931764
9.  Comparison of five influenza surveillance systems during the 2009 pandemic and their association with media attention 
BMC Public Health  2013;13:881.
Background
During the 2009 influenza pandemic period, routine surveillance of influenza-like-illness (ILI) was conducted in The Netherlands by a network of sentinel general practitioners (GPs). In addition during the pandemic period, four other ILI/influenza surveillance systems existed. For pandemic preparedness, we evaluated the performance of the sentinel system and the others to assess which of the four could be useful additions in the future. We also assessed whether performance of the five systems was influenced by media reports during the pandemic period.
Methods
The trends in ILI consultation rates reported by sentinel GPs from 20 April 2009 through 3 January 2010 were compared with trends in data from the other systems: ILI cases self-reported through the web-based Great Influenza Survey (GIS); influenza-related web searches through Google Flu Trends (GFT); patients admitted to hospital with laboratory-confirmed pandemic influenza, and detections of influenza virus by laboratories. In addition, correlations were determined between ILI consultation rates of the sentinel GPs and data from the four other systems. We also compared the trends of the five surveillance systems with trends in pandemic-related newspaper and television coverage and determined correlation coefficients with and without time lags.
Results
The four other systems showed similar trends and had strong correlations with the ILI consultation rates reported by sentinel GPs. The number of influenza virus detections was the only system to register a summer peak. Increases in the number of newspaper articles and television broadcasts did not precede increases in activity among the five surveillance systems.
Conclusions
The sentinel general practice network should remain the basis of influenza surveillance, as it integrates epidemiological and virological information and was able to maintain stability and continuity under pandemic pressure. Hospital and virological data are important during a pandemic, tracking the severity, molecular and phenotypic characterization of the viruses and confirming whether ILI incidence is truly related to influenza virus infections. GIS showed that web-based, self-reported ILI can be a useful addition, especially if virological self-sampling is added and an epidemic threshold could be determined. GFT showed negligible added value.
doi:10.1186/1471-2458-13-881
PMCID: PMC3849360  PMID: 24063523
Influenza virus; Pandemic; Surveillance; Influenza-like illness; Media attention
10.  First introduction of pandemic influenza A/H1N1 and detection of respiratory viruses in pediatric patients in Central African Republic 
Virology Journal  2013;10:49.
Background
Acute viral respiratory illnesses in children in sub-Saharan Africa have received relatively little attention, although they are much more frequent causes of morbidity and mortality than in developed countries. Active surveillance is essential to identify the causative agents and to improve clinical management, especially in the context of possible circulation of pandemic viruses.
Findings
A prospective study was conducted in the Central African Republic (CAR) between January and December 2010 among infants and children aged 0–15 years attending sentinel sites for influenza-like illness or acute respiratory illness. Nasopharyngeal swabs were collected, and one-step real-time and multiplex reverse transcription-polymerase chain reaction were used to detect respiratory viruses. Respiratory viruses were detected in 49 of the 329 (14.9%) nasopharyngeal samples: 29 (8.8%) contained influenza viruses (5 (1.5%) had pandemic influenza A/H1N1 virus and 24 (7.3%) had influenza B viruses), 11 (3.3%) contained parainfluenza viruses types 1 and 3 and 9 (2.7%) contained human respiratory syncytial virus. Most cases were detected during the rainy season in the CAR. Analysis of the amplicon sequences confirmed the identity of each detected virus.
Conclusions
The influenza surveillance system in the CAR has provided valuable data on the seasonality of influenza and the circulation of other respiratory viruses. Our network could therefore play a valuable role in the prevention and control of influenza epidemics in the CAR.
doi:10.1186/1743-422X-10-49
PMCID: PMC3598402  PMID: 23391188
Molecular diagnosis; Acute respiratory illness; Pandemic influenza A/H1N1 2009; Influenza B; Respiratory syncytial virus; Parainfluenza virus; Pediatric patients
11.  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
12.  Risk factors associated with fatal influenza, Romania, October 2009 – May 2011 
Background
Limited data are available from Central and Eastern Europe on risk factors for severe complications of influenza. Such data are essential to prioritize prevention and treatment resources and to adapt influenza vaccination recommendations.
Objectives
To use sentinel surveillance data to identify risk factors for fatal outcomes among hospitalized patients with severe acute respiratory infections (SARI) and among hospitalized patients with laboratory-confirmed influenza.
Methods
Retrospective analysis of case-based surveillance data collected from sentinel hospitals in Romania during the 2009/2010 and 2010/2011 winter influenza seasons was performed to evaluate risk factors for fatal outcomes using multivariate logistic regression.
Results
During 2009/2010 and 2010/2011, sentinel hospitals reported 661 SARI patients of which 230 (35%) tested positive for influenza. In the multivariate analyses, infection with influenza A(H1N1)pdm09 was the strongest risk factor for death among hospitalized SARI patients (OR: 6·6; 95% CI: 3·3–13·1). Among patients positive for influenza A(H1N1)pdm09 virus infection (n = 148), being pregnant (OR: 7·1; 95% CI: 1·6–31·2), clinically obese (OR: 2·9;95% CI: 1·6–31·2), and having an immunocompromising condition (OR: 3·7;95% CI: 1·1–13·4) were significantly associated with fatal outcomes.
Conclusion
These findings are consistent with several other investigations of risk factors associated with influenza A(H1N1)pdm09 virus infections. They also support the more recent 2012 recommendations by the WHO Strategic Advisory Group of Experts on Immunization (SAGE) that pregnant women are an important risk group for influenza vaccination. Ongoing sentinel surveillance can be useful tool to monitor risk factors for complications of influenza virus infections during each influenza season, and pandemics as well.
doi:10.1111/irv.12209
PMCID: PMC4177790  PMID: 24251915
Influenza; risk factors; Romania; severe acute respiratory illness; surveillance
13.  Epidemiology of the 2012 influenza season in Victoria, Australia 
Objective
To assess the magnitude and severity of the 2012 influenza season in Victoria, Australia using surveillance data from five sources.
Methods
Data from influenza notifications, sentinel general practices, a sentinel hospital network, a sentinel locum service and strain typing databases for 2012 were descriptively analysed.
Results
Influenza and influenza-like illness activity was moderate compared to previous years, although a considerable increase in notified laboratory-confirmed influenza was observed. Type A influenza comprised between 83% and 87% of cases from the general practitioners, hospitals and notifiable surveillance data. Influenza A/H3 was dominant in July and August, and most tested isolates were antigenically similar to the A/Perth/16/2009 virus used in the vaccine. There was a smaller peak of influenza type B in September. No tested viruses were resistant to any neuraminidase inhibitor antivirals. Higher proportions of type A/H3, hospitalized cases and those with a comorbid condition indicated for influenza vaccination were aged 65 years or older. Influenza vaccination coverage among influenza-like illness patients was 24% in sentinel general practices and 50% in hospitals.
Discussion
The 2012 influenza season in Victoria was average compared to previous years, with an increased dominance of A/H3 accompanied by increases in older and hospitalized cases. Differences in magnitude and the epidemiological profile of cases detected by the different data sources demonstrate the importance of using a range of surveillance data to assess the relative severity of influenza seasons.
doi:10.5365/WPSAR.2013.4.2.007
PMCID: PMC3854100  PMID: 24319614
14.  Viral Etiologies of Hospitalized Acute Lower Respiratory Infection Patients in China, 2009-2013 
PLoS ONE  2014;9(6):e99419.
Background
Acute lower respiratory infections (ALRIs) are an important cause of acute illnesses and mortality worldwide and in China. However, a large-scale study on the prevalence of viral infections across multiple provinces and seasons has not been previously reported from China. Here, we aimed to identify the viral etiologies associated with ALRIs from 22 Chinese provinces.
Methods and Findings
Active surveillance for hospitalized ALRI patients in 108 sentinel hospitals in 24 provinces of China was conducted from January 2009-September 2013. We enrolled hospitalized all-age patients with ALRI, and collected respiratory specimens, blood or serum collected for diagnostic testing for respiratory syncytial virus (RSV), human influenza virus, adenoviruses (ADV), human parainfluenza virus (PIV), human metapneumovirus (hMPV), human coronavirus (hCoV) and human bocavirus (hBoV).
We included 28,369 ALRI patients from 81 (of the 108) sentinel hospitals in 22 (of the 24) provinces, and 10,387 (36.6%) were positive for at least one etiology. The most frequently detected virus was RSV (9.9%), followed by influenza (6.6%), PIV (4.8%), ADV (3.4%), hBoV (1.9), hMPV (1.5%) and hCoV (1.4%). Co-detections were found in 7.2% of patients. RSV was the most common etiology (17.0%) in young children aged <2 years. Influenza viruses were the main cause of the ALRIs in adults and elderly. PIV, hBoV, hMPV and ADV infections were more frequent in children, while hCoV infection was distributed evenly in all-age. There were clear seasonal peaks for RSV, influenza, PIV, hBoV and hMPV infections.
Conclusions
Our findings could serve as robust evidence for public health authorities in drawing up further plans to prevent and control ALRIs associated with viral pathogens. RSV is common in young children and prevention measures could have large public health impact. Influenza was most common in adults and influenza vaccination should be implemented on a wider scale in China.
doi:10.1371/journal.pone.0099419
PMCID: PMC4063718  PMID: 24945280
15.  Evaluation of the Novel Respiratory Virus Surveillance Program: Pediatric Early Warning Sentinel Surveillance (PEWSS) 
Public Health Reports  2013;128(Suppl 2):88-96.
Objectives
Infections caused by respiratory viruses are associated with recurrent epidemics and widespread morbidity and mortality. Routine surveillance of these pathogens is necessary to determine virus activity, monitor for changes in circulating strains, and plan for public health preparedness. The Southern Nevada Health District in Las Vegas, Nevada, recruited five pediatric medical practices to serve as sentinel sites for the Pediatric Early Warning Sentinel Surveillance (PEWSS) program.
Methods
Sentinel staff collected specimens throughout the year from ill children who met the influenza-like illness case definition and submitted specimens to the Southern Nevada Public Health Laboratory for molecular testing for influenza and six non-influenza viruses.
Results
Laboratory results were analyzed and reported to the medical and general communities in weekly bulletins year-round. PEWSS data were also used to establish viral respiratory seasonal baselines and in influenza vaccination campaigns. The surveillance program was evaluated using the Centers for Disease Control and Prevention's (CDC's) Updated Guidelines for Evaluating Public Health Surveillance Systems. PEWSS met three of six program usefulness criteria and seven of nine surveillance system attributes, which exceeded the CDC Guidelines evaluation criteria for a useful and complete public health surveillance program.
Conclusion
We found that PEWSS is a useful and complete public health surveillance system that is simple, flexible, accessible, and stable.
PMCID: PMC3730010  PMID: 23997308
16.  Evaluation of Coseasonality of Influenza and Invasive Pneumococcal Disease: Results from Prospective Surveillance 
PLoS Medicine  2011;8(6):e1001042.
Using a combination of modeling and statistical analyses, David Fisman and colleagues show that influenza likely influences the incidence of invasive pneumococcal disease by enhancing risk of invasion in colonized individuals.
Background
The wintertime co-occurrence of peaks in influenza and invasive pneumococcal disease (IPD) is well documented, but how and whether wintertime peaks caused by these two pathogens are causally related is still uncertain. We aimed to investigate the relationship between influenza infection and IPD in Ontario, Canada, using several complementary methodological tools.
Methods and Findings
We evaluated a total number of 38,501 positive influenza tests in Central Ontario and 6,191 episodes of IPD in the Toronto/Peel area, Ontario, Canada, between 1 January 1995 and 3 October 2009, reported through population-based surveillance. We assessed the relationship between the seasonal wave forms for influenza and IPD using fast Fourier transforms in order to examine the relationship between these two pathogens over yearly timescales. We also used three complementary statistical methods (time-series methods, negative binomial regression, and case-crossover methods) to evaluate the short-term effect of influenza dynamics on pneumococcal risk. Annual periodicity with wintertime peaks could be demonstrated for IPD, whereas periodicity for influenza was less regular. As for long-term effects, phase and amplitude terms of pneumococcal and influenza seasonal sine waves were not correlated and meta-analysis confirmed significant heterogeneity of influenza, but not pneumococcal phase terms. In contrast, influenza was shown to Granger-cause pneumococcal disease. A short-term association between IPD and influenza could be demonstrated for 1-week lags in both case-crossover (odds ratio [95% confidence interval] for one case of IPD per 100 influenza cases  = 1.10 [1.02–1.18]) and negative binomial regression analysis (incidence rate ratio [95% confidence interval] for one case of IPD per 100 influenza cases  = 1.09 [1.05–1.14]).
Conclusions
Our data support the hypothesis that influenza influences bacterial disease incidence by enhancing short-term risk of invasion in colonized individuals. The absence of correlation between seasonal waveforms, on the other hand, suggests that bacterial disease transmission is affected to a lesser extent.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Although some pathogens (disease-causing organisms) cause illness all year round, others are responsible for seasonal peaks of illness. These peaks occur because of a complex interplay of factors such as the loss of immunity to the pathogen over time and seasonal changes in the pathogen's ability to infect new individuals. Thus, in temperate countries in the northern hemisphere, illness caused by influenza viruses (pathogens that infect the nose, throat, and airways) usually peaks between December and March, perhaps because weather conditions during these months favor the survival of influenza virus in the environment and thus increase its chances of being transferred among people. Another illness that peaks during the winter months in temperate regions is pneumonia, a severe lung infection that is often caused by Streptococcus pneumoniae. These bacteria can colonize the back of the throat without causing disease but occasionally spread into the lungs and other organs where they cause potentially fatal invasive pneumococcal disease (IPD).
Why Was This Study Done?
Although the co-occurrence of seasonal peaks of influenza and IPD is well documented, it is unclear whether (or how) these peaks are causally related. For example, do the peaks of influenza and IPD both occur in the winter because influenza enhances person-to-person transmission of S. pneumoniae (hypothesis 1)? Alternatively, do the diseases co-occur because influenza infection increases the risk of IPD in individuals who are already colonized with S. pneumoniae (hypothesis 2)? Healthcare professionals need to know whether there is a causal relationship between influenza and IPD so that they can target vaccination for both diseases to those individuals most at risk of developing the potentially serious complications of these diseases. In this study, the researchers use several mathematical and statistical methods and data on influenza and IPD collected in Ontario, Canada to investigate the relationship between these seasonal illnesses.
What Did the Researchers Do and Find?
Between January 1995 and October 2009, 38,501 positive influenza tests were recorded in Ontario by the Canadian national influenza surveillance network. Over the same time period, the Toronto Invasive Bacterial Diseases Network (a group of hospitals, laboratories, and doctors that undertakes population-based surveillance for serious bacterial infections in the Toronto and Peel Regions of Ontario) recorded 6,191 IPD episodes. The researchers used a mathematical method called fast Fourier transforms that compares the shape of wave forms to look for any relationship between infections with the two pathogens over yearly timescales (a test of hypothesis 1) and three statistical methods to evaluate the short-term effect of influenza dynamics on IPD risk (tests of hypothesis 2). Although they found wintertime peaks for infections with both pathogens, there was no correlation between the seasonal wave forms for influenza and IPD. That is, there was no relationship between the seasonal patterns of the two infections. By contrast, two of the statistical methods used to test hypothesis 2 revealed a short-term association between infections with influenza and with IPD. Moreover, the third statistical method (the Granger causality Wald test, a type of time-series analysis) provided evidence that data collected at intervals on influenza can be used to predict peaks in IPD infections.
What Do These Findings Mean?
These findings support (but do not prove) the hypothesis that influenza influences IPD incidence by enhancing the short-term risk of bacterial invasion in individuals already colonized with S. pneumoniae, possibly by increasing the permeability of the lining of the airways to bacteria. By contrast, the lack of correlation between the seasonal wave forms for the two diseases suggests that person-to-person transfer of S. pneumoniae is affected by influenza infections to a lesser extent. These findings have important implications for disease control policy. First, they suggest that the increased number of influenza infections in pandemic years may not necessarily be accompanied by a marked surge in IPD. Second, because the findings suggest that some cases of IPD may be influenza-attributable, the extension of influenza vaccination to school-age children and young adults (a group of people at particular risk of IPD who are not normally vaccinated against influenza) could reduce the incidence of IPD as well as the incidence of influenza.
Additional Information
Please access these Web sites via the online version of this summary at http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015493
A related research article by the same authors evaluating links between respiratory viruses and invasive meningococcal disease can be found in PLoS One (e0015493)
The US Centers for Disease Control and Prevention provides information for patients and health professionals on all aspects of seasonal influenza and pneumococcal disease and pneumococcal vaccination
The UK National Health Service Choices website also provides information for patients about seasonal influenza and pneumococcal infection
MedlinePlus has links to further information about influenza and pneumococcal infections (in English and Spanish)
FluWatch is the Canadian national surveillance system for influenza
More information about the Toronto Invasive Bacterial Network is available
The International Association for Ecology and Health provides information on the physical environment and its influence on health
doi:10.1371/journal.pmed.1001042
PMCID: PMC3110256  PMID: 21687693
17.  User Experience of the U.S. Department of Defense (DoD) Respiratory Disease Dashboard 
Objective
Evaluate the user experience of a novel electronic disease reporting and analysis system deployed across the DoD global laboratory surveillance network
Introduction
Lessons learned from the 2009 influenza pandemic have driven many changes in the standards and practices of respiratory disease surveillance worldwide. In response to the needs for timely information sharing of emerging respiratory pathogens (1), the DoD Armed Forces Health Surveillance Center (AFHSC) collaborated with the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to develop an Internet-based data management system known as the Respiratory Disease Dashboard (RDD). The goal of the RDD is to provide the AFHSC global respiratory disease surveillance network a centralized system for the monitoring and tracking of lab-confirmed respiratory pathogens, thereby streamlining the data reporting process and enhancing the timeliness for detection of potential pandemic threats. This system consists of a password-protected internet portal that allows users to directly input respiratory specimen data and visualize data on an interactive, global map. Currently, eight DoD partner laboratories are actively entering respiratory pathogen data into the RDD, encompassing specimens from sentinel sites in eleven countries: Cambodia, Colombia, Kenya, Ecuador, Egypt, Honduras, Nicaragua, Paraguay, Peru, Uganda, and the United States. A user satisfaction survey was conducted to guide further development of the RDD and to support other disease surveillance efforts at the AFHSC.
Methods
User training was provided to partner laboratories during a transition of data submission from Excel spreadsheet to RDD electronic data entry between November 2011 and May 2012. A user experience survey was distributed to the participating laboratories in August 2012 and based on the experience of 139 entries. The survey adopted elements of the SWOT (Strength-Weaknesses-Opportunities-Threats) analysis to determine the system’s strengths and weaknesses as well as to solicit users’ perspectives on the efficiency of the system in assisting with disease surveillance data entry and visualization. Questionnaires in an open-ended (free-text response) format were distributed to all eight participating laboratories. Common themes were identified based on the solicited responses.
Results
Although only four of eight participating laboratory partners replied to the survey (50% survey response rate), all survey were completed without any omission of questions (100% completion rate). 2/25 (8%) total responses were neutral comments and therefore omitted in the thematic analysis (Table 1). In general, there was a distinct dichotomy in opinion between overseas laboratories and domestic laboratories with regard to the usefulness of the RDD, with overseas laboratories viewing the RDD as more useful than domestic laboratories. A review of the comparison between weekly specimens submitted to the AFHSC via Excel spreadsheet and data entered directly into the RDD revealed misunderstandings about the meaning of the data entry labels in the RDD interface. It was noted by four laboratories that a “Quick Start” user manual would be useful to clarify the definitions of some data labels.
Conclusions
Overall, this user experience evaluation has identified the needs for additional training on RDD data entry procedures and a “Quick Start” user manual to support the standardization of surveillance definitions. In general, users appreciate the visualization of the global DoD laboratory network data. This evaluation demonstrated the importance of active participation from data contributors and the invaluable organizational support in the development of the RDD as an electronic disease reporting and analysis system.
PMCID: PMC3692836
Outbreak Detection; Disease Surveillance; User Experience Evaluation; Data Management
18.  Evaluating the New York City Emergency Department Syndromic Surveillance for Monitoring Influenza Activity during the 2009-10 Influenza Season 
PLoS Currents  2012;4:e500563f3ea181.
Objective: To use laboratory data to assess the specificity of syndromes used by the New York City emergency department (ED) syndromic surveillance system to monitor influenza activity. Design: For the period from October 1, 2009 through March 31, 2010, we examined the correlation between citywide ED syndrome assignment and laboratory-confirmed influenza and respiratory syncytial virus (RSV). In addition, ED syndromic data from five select NYC hospitals were matched at the patient and visit level to corresponding laboratory reports of influenza and RSV. The matched dataset was used to evaluate syndrome assignment by disease and to calculate the sensitivity and specificity of the influenza-like illness (ILI) syndrome. Results: Citywide ED visits for ILI correlated well with influenza laboratory diagnoses (R=0.92). From October 1, 2009, through March 31, 2010, there were 264,532 ED visits at the five select hospitals, from which the NYC Department of Health and Mental Hygiene (DOHMH) received confirmatory laboratory reports of 655 unique cases of influenza and 1348 cases of RSV. The ED visit of most (56%) influenza cases had been categorized in the fever/flu syndrome; only 15% were labeled ILI. Compared to other influenza-related syndromes, ILI had the lowest sensitivity (15%) but the highest specificity (90%) for laboratory-confirmed influenza. Sensitivity and specificity varied by age group and influenza activity level. Conclusions: The ILI syndrome in the NYC ED syndromic surveillance system served as a specific but not sensitive indicator for influenza during the 2009-2010 influenza season. Despite its limited sensitivity, the ILI syndrome can be more informative for tracking influenza trends than the fever/flu or respiratory syndromes because it is less likely to capture cases of other respiratory viruses. However, ED ILI among specific age groups should be interpreted alongside laboratory surveillance data. ILI remains a valuable tool for monitoring influenza activity and trends as it facilitates comparisons nationally and across jurisdictions and is easily communicated to the public.
doi:10.1371/500563f3ea181
PMCID: PMC3441153  PMID: 22984645
19.  Strategy to Enhance Influenza Surveillance Worldwide1 
Emerging Infectious Diseases  2009;15(8):1271-1278.
Sentinel surveillance for severe acute respiratory infection and influenza-like illness is effective in resource-limited settings.
The emergence of a novel strain of influenza virus A (H1N1) in April 2009 focused attention on influenza surveillance capabilities worldwide. In consultations before the 2009 outbreak of influenza subtype H1N1, the World Health Organization had concluded that the world was unprepared to respond to an influenza pandemic, due in part to inadequate global surveillance and response capacity. We describe a sentinel surveillance system that could enhance the quality of influenza epidemiologic and laboratory data and strengthen a country’s capacity for seasonal, novel, and pandemic influenza detection and prevention. Such a system would 1) provide data for a better understanding of the epidemiology and extent of seasonal influenza, 2) provide a platform for the study of other acute febrile respiratory illnesses, 3) provide virus isolates for the development of vaccines, 4) inform local pandemic planning and vaccine policy, 5) monitor influenza epidemics and pandemics, and 6) provide infrastructure for an early warning system for outbreaks of new virus subtypes.
doi:10.3201/eid1508.081422
PMCID: PMC2815958  PMID: 19751590
influenza; human influenza influenza A virus; avian influenza; H5N1 subtype; sentinel surveillance; epidemiology; viruses; policy review
20.  Viral etiology and seasonality of influenza-like illness in Gabon, March 2010 to June 2011 
BMC Infectious Diseases  2014;14:373.
Background
Surveillance of influenza-like illness (ILI) in Central Africa began only recently, and few data are therefore available on the circulation of influenza virus and other respiratory viruses. In Gabon, a Central African country, we established a surveillance network in four major towns in order to analyze cases of ILI among patients who visited health centers between March 2010 and June 2011, and to determine the viral etiology.
Methods
Nasal swabs were sent for analysis to the Centre International de Recherches Médicales de Franceville, where they were screened for 17 respiratory viruses in a multiplex real-time reverse transcription polymerase chain reaction for all pathogens according the following pairs: adenovirus/parainfluenza virus 4, respiratory syncytial virus/human metapneumovirus, parainfluenza virus 1/parainfluenza virus 2, pandemic influenza virus A/seasonal influenza virus A (H1N1, H3N2)/seasonal influenza virus B, human coronaviruses 229E/OC43, human coronaviruses NL63/HKU1, rhinovirus/human parechovirus, and enterovirus/parainfluenza virus 3.
Results
We analyzed a total of 1041 specimens, of which 639 (61%) were positive for at least one virus. Three-quarters of the patients were children under five years old. We therefore focused on this age group, in which 68.1% of patients were positive for at least one virus. The most common viruses were adenoviruses (17.5%), followed by parainfluenza viruses (PIVs) 1–4 (16.8%), enteroviruses (EV) (14.7%), respiratory syncytial virus (RSV) (13.5%), and influenza virus (11.9%). The prevalence of some viruses was subject to geographic and seasonal variations. One-third of positive samples contained more than one virus.
Conclusions
Like most studies in the world, the virus PIVs, EV, RSV, Influenza virus, HRV were predominant among children under five years old in Gabon. An exception is made for adenoviruses which have a high prevalence in our study. However adenoviruses can be detected in asymptomatic persons. These finding gave a better knowledge of the circulation and the seasonality of the viruses involved in ILI in Gabon.
doi:10.1186/1471-2334-14-373
PMCID: PMC4107952  PMID: 25000832
Gabon; Surveillance network; Influenza-like illness; Viruses; Seasonality
21.  Characterizing the Epidemiology of the 2009 Influenza A/H1N1 Pandemic in Mexico 
PLoS Medicine  2011;8(5):e1000436.
Gerardo Chowell and colleagues address whether school closures and other social distancing strategies were successful in reducing pandemic flu transmission in Mexico by analyzing the age- and state-specific incidence of influenza morbidity and mortality in 32 Mexican states.
Background
Mexico's local and national authorities initiated an intense public health response during the early stages of the 2009 A/H1N1 pandemic. In this study we analyzed the epidemiological patterns of the pandemic during April–December 2009 in Mexico and evaluated the impact of nonmedical interventions, school cycles, and demographic factors on influenza transmission.
Methods and Findings
We used influenza surveillance data compiled by the Mexican Institute for Social Security, representing 40% of the population, to study patterns in influenza-like illness (ILIs) hospitalizations, deaths, and case-fatality rate by pandemic wave and geographical region. We also estimated the reproduction number (R) on the basis of the growth rate of daily cases, and used a transmission model to evaluate the effectiveness of mitigation strategies initiated during the spring pandemic wave. A total of 117,626 ILI cases were identified during April–December 2009, of which 30.6% were tested for influenza, and 23.3% were positive for the influenza A/H1N1 pandemic virus. A three-wave pandemic profile was identified, with an initial wave in April–May (Mexico City area), a second wave in June–July (southeastern states), and a geographically widespread third wave in August–December. The median age of laboratory confirmed ILI cases was ∼18 years overall and increased to ∼31 years during autumn (p<0.0001). The case-fatality ratio among ILI cases was 1.2% overall, and highest (5.5%) among people over 60 years. The regional R estimates were 1.8–2.1, 1.6–1.9, and 1.2–1.3 for the spring, summer, and fall waves, respectively. We estimate that the 18-day period of mandatory school closures and other social distancing measures implemented in the greater Mexico City area was associated with a 29%–37% reduction in influenza transmission in spring 2009. In addition, an increase in R was observed in late May and early June in the southeast states, after mandatory school suspension resumed and before summer vacation started. State-specific fall pandemic waves began 2–5 weeks after school reopened for the fall term, coinciding with an age shift in influenza cases.
Conclusions
We documented three spatially heterogeneous waves of the 2009 A/H1N1 pandemic virus in Mexico, which were characterized by a relatively young age distribution of cases. Our study highlights the importance of school cycles on the transmission dynamics of this pandemic influenza strain and suggests that school closure and other mitigation measures could be useful to mitigate future influenza pandemics.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
From June 2009 to August 2010, the world was officially (according to specific World Health Organization [WHO] criteria—WHO phase 6 pandemic alert) in the grip of an Influenza A pandemic with a new strain of the H1N1 virus. The epidemic in Mexico, which had the second confirmed global case of H1N1 virus was first noted in early April 2009, when reports of respiratory hospitalizations and deaths among 62 young adults in Mexico alerted local health officials to the occurrence of atypical rates of respiratory illness. In line with its inter-institutional National Pandemic Influenza Preparedness and Response Plan, the Ministry of Health cancelled school attendance in the greater Mexico City area on April 24 and expanded these measures to the rest the country three days later. The Ministry of Health then implemented in Mexico City other “social distancing” strategies such as closing cinemas and restaurants and cancelling large public gatherings.
Why Was This Study Done?
School closures and other intense social distancing strategies can be very disruptive to the population, but as yet it is uncertain whether these measures were successful in reducing disease transmission. In addition, there have been no studies concentrating on recurrent pandemic waves in Mexico. So in this study the authors addressed these issues by analyzing the age- and state-specific incidence of influenza morbidity and mortality in 32 Mexican States and quantified the association between local influenza transmission rates, school cycles, and demographic factors.
What Did the Researchers Do and Find?
The researchers used the epidemiological surveillance system of the Mexican Institute for Social Security—a Mexican health system that covers private sector workers and their families, a group representative of the general population, that comprises roughly 40% of the Mexican population (107 million individuals), with a network of 1,099 primary health care units and 259 hospitals nationwide. Then the researchers compiled state- and age-specific time series of incident influenza-like illness and H1N1 influenza cases by day of symptom onset to analyze the geographic dissemination patterns of the pandemic across Mexico and defined three temporally distinct pandemic waves in 2009: spring (April 1–May 20), summer (May 21–August 1), and fall (August 2–December 31). The researchers then applied a mathematical model of influenza transmission to daily case data to assess the effectiveness of mandatory school closures and other social distancing measures implemented during April 24–May 11, in reducing influenza transmission rates.
The Mexican Institute for Social Security reported a total of 117,626 people with influenza-like illness from April 1 to December 31, 2009, of which 36,044 were laboratory tested (30.6%) and 27,440 (23.3%) were confirmed with H1N1 influenza. During this period, 1,370 people with influenza-like illness died of which 585 (1.5 per 100,000) were confirmed to have H1N1 influenza. The median age of people with laboratory confirmed influenza like illness (H1N1) was 18 years overall but increased to 31 years during the autumn wave. The overall case-fatality ratio among people with influenza like illness was 1.2%, but highest (5.5%) among people over 60 years. The researchers found that the 18-day period of mandatory school closures and other social distancing measures implemented in the greater Mexico City area was associated with a substantial (29%–37%) reduction in influenza transmission in spring 2009 but increased in late May and early June in the southeast states, after mandatory school suspension resumed and before summer vacation started. State-specific pandemic waves began 2–5 weeks after school reopened for the fall term, coinciding with an age shift in influenza cases.
What Do These Findings Mean?
These findings show that the age distribution of pandemic influenza morbidity was greater in younger age groups, while the risk of severe disease was skewed towards older age groups, and that there were substantial geographical variation in pandemic patterns across Mexico, in part related to population size. But most importantly, these findings support the effectiveness of early mitigation efforts including mandatory school closures and cancellation of large public gatherings, reinforcing the importance of school cycles in the transmission of pandemic influenza. This analysis increases understanding of the age and transmission patterns of the Mexican 2009 influenza pandemic at various geographic scales, which is crucial for designing more efficient public health interventions against future influenza pandemics.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000436.
The World Health Organization provides information about the global response to the 2009 H1N1 pandemic
doi:10.1371/journal.pmed.1000436
PMCID: PMC3101203  PMID: 21629683
22.  Demonstrating the Use of High-Volume Electronic Medical Claims Data to Monitor Local and Regional Influenza Activity in the US 
PLoS ONE  2014;9(7):e102429.
Introduction
Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.
Material and Methods
We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003–2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (<5, 5–29, 30–59, and 60+ years). To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City. We also used control data on laboratory-confirmed Respiratory Syncytial Virus (RSV) activity to test the specificity of IMS-ILI for influenza surveillance.
Results
Regional IMS-ILI indicators were highly synchronous with CDC's reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80–0.97, P<0.001). Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children. IMS-ILI indicators were more correlated with reference influenza data than control RSV indicators (rho = 0.93 with influenza v. rho = 0.33 with RSV, P<0.05). City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).
Conclusion
Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.
doi:10.1371/journal.pone.0102429
PMCID: PMC4114744  PMID: 25072598
23.  Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method 
PLoS Medicine  2011;8(7):e1001051.
Using weekly influenza surveillance data from the US CDC, Edward Goldstein and colleagues develop a statistical method to predict the sizes of epidemics caused by seasonal influenza strains. This method could inform decisions about the most appropriate vaccines or drugs needed early in the influenza season.
Background
The epidemic sizes of influenza A/H3N2, A/H1N1, and B infections vary from year to year in the United States. We use publicly available US Centers for Disease Control (CDC) influenza surveillance data between 1997 and 2009 to study the temporal dynamics of influenza over this period.
Methods and Findings
Regional outpatient surveillance data on influenza-like illness (ILI) and virologic surveillance data were combined to define a weekly proxy for the incidence of each strain in the United States. All strains exhibited a negative association between their cumulative incidence proxy (CIP) for the whole season (from calendar week 40 of each year to calendar week 20 of the next year) and the CIP of the other two strains (the complementary CIP) from the start of the season up to calendar week 2 (or 3, 4, or 5) of the next year. We introduce a method to predict a particular strain's CIP for the whole season by following the incidence of each strain from the start of the season until either the CIP of the chosen strain or its complementary CIP exceed certain thresholds. The method yielded accurate predictions, which generally occurred within a few weeks of the peak of incidence of the chosen strain, sometimes after that peak. For the largest seasons in the data, which were dominated by A/H3N2, prediction of A/H3N2 incidence always occurred at least several weeks in advance of the peak.
Conclusion
Early circulation of one influenza strain is associated with a reduced total incidence of the other strains, consistent with the presence of interference between subtypes. Routine ILI and virologic surveillance data can be combined using this new method to predict the relative size of each influenza strain's epidemic by following the change in incidence of a given strain in the context of the incidence of cocirculating strains.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every winter in temperate countries, millions of people catch influenza, a viral infection of the nose, throat, and airways. Most infected individuals recover quickly but seasonal influenza outbreaks (epidemics) kill about half a million people annually. Epidemics of influenza occur because small but frequent changes in the viral proteins (antigens) to which the immune system responds mean that an immune response produced one year provides only partial protection against influenza the next year. Annual immunization with a vaccine that contains killed influenza viruses of the major circulating strains boosts this natural immunity and greatly reduces a person's chances of catching influenza. Influenza epidemics in temperate latitudes are usually caused by an influenza B virus or one of two influenza A subtypes called A/H3N2 and A/H1N1. The names of the influenza A viruses indicate the types of two major influenza antigens—hemagglutinin (H3 or H1) and neuraminidase (N2 or N1)—present in the viruses.
Why Was This Study Done?
At present, there is no way to predict whether influenza B or an influenza A subtype will be dominant (responsible for the majority of infections) in any given influenza season. There is also no way to predict the size of the epidemic that will be caused by each viral strain. Public health officials would like to be able to make predictions of this sort early in the winter to help them determine which measures to recommend to minimize the illness and death caused by influenza. In this study, the researchers use weekly influenza surveillance data collected by the US Centers for Disease Control and Prevention (CDC) to study the temporal dynamics of seasonal influenza in the United States between 1997 and 2009 and to develop a statistical method to predict the sizes of epidemics caused by influenza A/H1N1, A/H3N2, and B.
What Did the Researchers Do and Find?
The CDC influenza surveillance system collects information on the proportion of patients attending US outpatient facilities who have an influenza-like illness (fever and a cough and/or a sore throat in the absence of any known cause other than influenza) and on the proportion of respiratory viral isolates testing positive for specific influenza strains at US viral surveillance laboratories. The researchers combined these data to define a weekly “proxy” incidence of each influenza strain across the United States (an estimate of the number of new cases per week in the US population) and a cumulative incidence proxy (CIP) for each influenza season. For each strain, there was a negative association between its whole-season CIP and the early-season CIP of the other two strains (the complementary CIP). That is, high infection rates with one strain appeared to interfere with the transmission of other strains. Given this relationship, the researchers then developed a statistical algorithm (a step-by-step problem solving method) that accurately predicted the whole-season CIP for a particular strain by following the incidence of each strain from the start of the season until either its CIP or the complementary CIP had exceeded a specific threshold. So, for example, for influenza B, the algorithm provided an accurate prediction of the whole-season CIP before the peak of influenza B incidence for each season included in the study. Similarly, prediction of whole-season A/H3N2 incidence always occurred several weeks in advance of its weekly incidence peak.
What Do These Findings Mean?
These findings suggest that early circulation of one influenza strain is associated with a reduced total incidence of other strains, possibly because of cross-subtype immunity. Importantly, they also suggest that routine early-season surveillance data can be used to predict the relative size of the epidemics caused by each influenza strain in the United States and in other countries where sufficient surveillance data are available. Because the algorithm makes many assumptions and simplifies the behavior of influenza epidemics, its predictions may not always be accurate. Moreover, it needs to be tested with data collected over more influenza seasons. Nevertheless, the algorithm's ability to predict the relative epidemic size of A/H3N2, the influenza strain with the highest death rates, several weeks before its peak in seasons in which it was the dominant strain suggests that this predictive method could help public-health officials introduce relevant preventative and/or treatment measures early in each influenza season.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001051.
The US Centers for Disease Control and Prevention provides information for patients and health professionals on all aspects of seasonal influenza, including information about the US influenza surveillance system
The UK National Health Service Choices Web site also provides information for patients about seasonal influenza; the UK Health Protection Agency provides information on influenza surveillance in the UK
MedlinePlus has links to further information about influenza l (in English and Spanish)
doi:10.1371/journal.pmed.1001051
PMCID: PMC3130020  PMID: 21750666
24.  Laboratory Surveillance of Influenza-Like Illness in Seven Teaching Hospitals, South Korea: 2011–2012 Season 
PLoS ONE  2013;8(5):e64295.
Background
A well-constructed and properly operating influenza surveillance scheme is essential for public health. This study was conducted to evaluate the distribution of respiratory viruses in patients with influenza-like illness (ILI) through the first teaching hospital-based surveillance scheme for ILI in South Korea.
Methods
Respiratory specimens were obtained from adult patients (≥18 years) who visited the emergency department (ED) with ILI from week 40, 2011 to week 22, 2012. Multiplex PCR was performed to detect respiratory viruses: influenza virus, adenovirus, coronavirus, respiratory syncytial virus, rhinovirus, human metapneumovirus, parainfluenza virus, bocavirus, and enterovirus.
Results
Among 1,983 patients who visited the ED with ILI, 811 (40.9%) were male. The median age of patients was 43 years. Influenza vaccination rate was 21.7% (430/1,983) during the 2011–2012 season. At least one comorbidity was found in 18% of patients. The positive rate of respiratory viruses was 52.1% (1,033/1,983) and the total number of detected viruses was 1,100. Influenza A virus was the dominant agent (677, 61.5%) in all age groups. The prevalence of human metapneumovirus was higher in patients more than 50 years old, while adenovirus was detected only in younger adults. In 58 (5.6%) cases, two or more respiratory viruses were detected. The co-incidence case was identified more frequently in patients with hematologic malignancy or organ transplantation recipients, however it was not related to clinical outcomes.
Conclusion
This study is valuable as the first extensive laboratory surveillance of the epidemiology of respiratory viruses in ILI patients through a teaching hospital-based influenza surveillance system in South Korea.
doi:10.1371/journal.pone.0064295
PMCID: PMC3661466  PMID: 23717587
25.  Estimating Sensitivity of Laboratory Testing for Influenza in Canada through Modelling 
PLoS ONE  2009;4(8):e6681.
Background
The weekly proportion of laboratory tests that are positive for influenza is used in public health surveillance systems to identify periods of influenza activity. We aimed to estimate the sensitivity of influenza testing in Canada based on results of a national respiratory virus surveillance system.
Methods and Findings
The weekly number of influenza-negative tests from 1999 to 2006 was modelled as a function of laboratory-confirmed positive tests for influenza, respiratory syncytial virus (RSV), adenovirus and parainfluenza viruses, seasonality, and trend using Poisson regression. Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests. The sensitivity of influenza testing was estimated to be 33% (95%CI 32–34%), varying from 30–40% depending on the season and region.
Conclusions
The estimated sensitivity of influenza tests reported to this national laboratory surveillance system is considerably less than reported test characteristics for most laboratory tests. A number of factors may explain this difference, including sample quality and specimen procurement issues as well as test characteristics. Improved diagnosis would permit better estimation of the burden of influenza.
doi:10.1371/journal.pone.0006681
PMCID: PMC2722738  PMID: 19688094

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