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1.  Improving pandemic influenza risk assessment 
eLife  2014;3:e03883.
Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response.
PMCID: PMC4199076  PMID: 25321142
influenza; pandemic; emergence; human; viruses
2.  Simulation-guided design of serological surveys of the cumulative incidence of influenza infection 
BMC Infectious Diseases  2014;14(1):505.
Influenza infection does not always cause clinical illnesses, so serological surveillance has been used to determine the true burden of influenza outbreaks. This study investigates the accuracy of measuring cumulative incidence of influenza infection using different serological survey designs.
We used a simple transmission model to simulate a typical influenza epidemic and obtained the seroprevalence over time. We also constructed four illustrative scenarios for baseline levels of antibodies prior and levels of boosting following infection in the simulated studies. Although illustrative, three of the four scenarios were based on the most detailed empirical data available. We used standard analytical methods to calculate estimated seroprevalence and associated confidence intervals for each of the four scenarios for both cross-sectional and longitudinal study designs. We tested the sensitivity of our results to changes in the sampled size and in our ability to detect small changes in antibody levels.
There were substantial differences between the background antibody titres and levels of boosting within three of our illustrative scenarios which were based on empirical data. These differences propagated through to different and substantial patterns of bias for all scenarios other than those with very low background titre and high levels of boosting. The two survey designs result in similar seroprevalence estimates in general under these scenarios, but when background immunity was high, simulated cross-sectional studies had higher biases. Sensitivity analyses indicated that an ability to accurately detect low levels of antibody boosting within paired sera would substantially improve the performance of serological surveys, even under difficult conditions.
Levels of boosting and background immunity significantly affect the accuracy of seroprevalence estimations, and depending on these levels of immunity responses, different survey designs should be used to estimate seroprevalences. These results suggest that under current measurement criteria, cumulative incidence measured by serological surveys might have been substantially underestimated by failing to include all infections, including mild and asymptomatic infections, in certain scenarios. Dilution protocols more highly resolved than serial 2-fold dilution should be considered for serological surveys.
PMCID: PMC4261848  PMID: 25231414
Infection attack rate; Cumulative incidence; Seroprevalence; Influenza; Serological survey; Cross-sectional study design; Longitudinal study design; Mathmatical modelling
3.  Social contacts and the locations in which they occur as risk factors for influenza infection 
The interaction of human social behaviour and transmission is an intriguing aspect of the life cycle of respiratory viral infections. Although age-specific mixing patterns are often assumed to be the key drivers of the age-specific heterogeneity in transmission, the association between social contacts and biologically confirmed infection has not previously been tested at the individual level. We administered a questionnaire to participants in a longitudinal cohort survey of influenza in which infection was defined by longitudinal paired serology. Using a variety of statistical approaches, we found overwhelming support for the inclusion of individual age in addition to contact variables when explaining odds of infection: the best model not including age explained only 15.7% of the deviance, whereas the best model with age explained 23.6%. However, within age groups, we did observe an association between contacts, locations and infection: median numbers of contacts (or locations) reported by those infected were higher than those from the uninfected group in every age group other than the youngest. Further, we found some support for the retention of location and contact variables in addition to age in our regression models, with excess odds of infection of approximately 10% per additional 10 contacts or one location. These results suggest that, although the relationship between age and incidence of respiratory infection at the level of the individual is not driven by self-reported social contacts, risk within an age group may be.
PMCID: PMC4100506  PMID: 25009062
pandemic; influenza; contact patterns
4.  Surveillance of low pathogenic novel H7N9 avian influenza in commercial poultry barns: detection of outbreaks and estimation of virus introduction time 
BMC Infectious Diseases  2014;14(1):427.
Both high and low pathogenic subtype A avian influenza remain ongoing threats to the commercial poultry industry globally. The emergence of a novel low pathogenic H7N9 lineage in China presents itself as a new concern to both human and animal health and may necessitate additional surveillance in commercial poultry operations in affected regions.
Sampling data was simulated using a mechanistic model of H7N9 influenza transmission within commercial poultry barns together with a stochastic observation process. Parameters were estimated using maximum likelihood. We assessed the probability of detecting an outbreak at time of slaughter using both real-time polymerase chain reaction (rt-PCR) and a hemagglutinin inhibition assay (HI assay) before considering more intense sampling prior to slaughter. The day of virus introduction and R0 were estimated jointly from weekly flock sampling data. For scenarios where R0 was known, we estimated the day of virus introduction into a barn under different sampling frequencies.
If birds were tested at time of slaughter, there was a higher probability of detecting evidence of an outbreak using an HI assay compared to rt-PCR, except when the virus was introduced <2 weeks before time of slaughter. Prior to the initial detection of infection Nsample = 50 (1%) of birds were sampled on a weekly basis once, but after infection was detected, Nsample = 2000 birds (40%) were sampled to estimate both parameters. We accurately estimated the day of virus introduction in isolation with weekly and 2-weekly sampling.
A strong sampling effort would be required to infer both the day of virus introduction and R0. Such a sampling effort would not be required to estimate the day of virus introduction alone once R0 was known, and sampling Nsample = 50 of birds in the flock on a weekly or 2 weekly basis would be sufficient.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2334-14-427) contains supplementary material, which is available to authorized users.
PMCID: PMC4129106  PMID: 25085078
H7N9; Influenza; Surveillance; R0; Poultry
5.  Inferring patterns of influenza transmission in swine from multiple streams of surveillance data 
Swine populations are known to be an important source of new human strains of influenza A, including those responsible for global pandemics. Yet our knowledge of the epidemiology of influenza in swine is dismayingly poor, as highlighted by the emergence of the 2009 pandemic strain and the paucity of data describing its origins. Here, we analyse a unique dataset arising from surveillance of swine influenza at a Hong Kong abattoir from 1998 to 2010. We introduce a state–space model that estimates disease exposure histories by joint inference from multiple modes of surveillance, integrating both virological and serological data. We find that an observed decrease in virus isolation rates is not due to a reduction in the regional prevalence of influenza. Instead, a more likely explanation is increased infection of swine in production farms, creating greater immunity to disease early in life. Consistent with this, we find that the weekly risk of exposure on farms equals or exceeds the exposure risk during transport to slaughter. We discuss potential causes for these patterns, including competition between influenza strains and shifts in the Chinese pork industry, and suggest opportunities to improve knowledge and reduce prevalence of influenza in the region.
PMCID: PMC3673063  PMID: 23658205
influenza; swine; disease ecology; infectious disease surveillance; zoonosis; state–space model
6.  The Contribution of Social Behaviour to the Transmission of Influenza A in a Human Population 
PLoS Pathogens  2014;10(6):e1004206.
Variability in the risk of transmission for respiratory pathogens can result from several factors, including the intrinsic properties of the pathogen, the immune state of the host and the host's behaviour. It has been proposed that self-reported social mixing patterns can explain the behavioural component of this variability, with simulated intervention studies based on these data used routinely to inform public health policy. However, in the absence of robust studies with biological endpoints for individuals, it is unclear how age and social behaviour contribute to infection risk. To examine how the structure and nature of social contacts influenced infection risk over the course of a single epidemic, we designed a flexible disease modelling framework: the population was divided into a series of increasingly detailed age and social contact classes, with the transmissibility of each age-contact class determined by the average contacts of that class. Fitting the models to serologically confirmed infection data from the 2009 Hong Kong influenza A/H1N1p pandemic, we found that an individual's risk of infection was influenced strongly by the average reported social mixing behaviour of their age group, rather than by their personal reported contacts. We also identified the resolution of social mixing that shaped transmission: epidemic dynamics were driven by intense contacts between children, a post-childhood drop in risky contacts and a subsequent rise in contacts for individuals aged 35–50. Our results demonstrate that self-reported social contact surveys can account for age-associated heterogeneity in the transmission of a respiratory pathogen in humans, and show robustly how these individual-level behaviours manifest themselves through assortative age groups. Our results suggest it is possible to profile the social structure of different populations and to use these aggregated data to predict their inherent transmission potential.
Author Summary
For infections such as influenza, there are several aspects to the transmission process, including the properties of the pathogen itself, the host immune system and host behaviour. Although it has been proposed that self-reported social mixing patterns can be used to explain the behavioural component of infection – and mathematical modelling studies based on reported social contacts are used routinely to inform health policy – it is not clear how these contacts contribute to individual- and group-level infection risk. By analysing the relationship between social contacts and infection patterns during the 2009 Hong Kong influenza pandemic, we show that infection risk was strongly influenced by the average reported social mixing behaviour of an individual's age group, rather than by their personal reported contacts. We also demonstrate how social contact surveys can be combined with mathematical models to create useful tools with which to study respiratory infections in humans. This should make it possible to predict how the impact of interventions will vary from one population to the next based on their contacts and, potentially, to explain differences in infection attack rates between groups with different mixing behaviours.
PMCID: PMC4072802  PMID: 24968312
7.  Social mixing patterns in rural and urban areas of southern China 
A dense population, global connectivity and frequent human–animal interaction give southern China an important role in the spread and emergence of infectious disease. However, patterns of person-to-person contact relevant to the spread of directly transmitted infections such as influenza remain poorly quantified in the region. We conducted a household-based survey of travel and contact patterns among urban and rural populations of Guangdong, China. We measured the character and distance from home of social encounters made by 1821 individuals. Most individuals reported 5–10 h of contact with around 10 individuals each day; however, both distributions have long tails. The distribution of distance from home at which contacts were made is similar: most were within a kilometre of the participant's home, while some occurred further than 500 km away. Compared with younger individuals, older individuals made fewer contacts which tended to be closer to home. There was strong assortativity in age-based contact rates. We found no difference between the total number or duration of contacts between urban and rural participants, but urban participants tended to make contacts closer to home. These results can improve mathematical models of infectious disease emergence, spread and control in southern China and throughout the region.
PMCID: PMC4024290  PMID: 24789897
influenza; mathematical modelling; social mixing; contact diary; travel; infectious disease transmission
8.  Multiannual patterns of influenza A transmission in Chinese live-bird market systems 
Avian influenza viruses (AIV) cause huge economic losses in poultry industries and pose a substantial threat to human health. However, predicting AIV epizootics and emergence in humans is confounded by insufficient empirical data on the ecology and dynamics of AIV in poultry systems. To address this gap, we quantified incidence patterns for 13 hemagglutinin subtypes of AIV using six years of surveillance data that were collected from ten different species of poultry and three different types of poultry holdings (contexts) – retail, wholesale or farms.
We collected 42,646 samples in Shantou, China between 2000 and 2006. We screened samples for hemagglutinin subtypes 1–13 of AIV and Avian Paramyxovirus-type-1 (APMV-1) using monospecific antisera in hemagglutination inhibition tests. We analyzed the data to determine seasonality patterns, subtype-host and subtype-subtype interactions as well as subtype bias in incidence in different contexts.
H3, H6, H9 and APMV-1 were the most prevalent. No significant seasonality was found when all subtypes were considered together. For most AIV subtypes and APMV-1, there was subtype specificity for host, context, and co-infection partner. H5 showed the most generalized host usage pattern, followed by H9 and H6.
Subtype-specific patterns due to host, context and other subtypes suggest that risk assessments that exclude these details are likely inaccurate. Surveillance should include longitudinal sampling of multiple host species in multiple contexts. Quantitative models of control strategies must consider multiple subtypes, hosts and source contexts in order to assess effectiveness of interventions.
PMCID: PMC4061500  PMID: 22458429
Avian influenza; H5N1; Host specificity; Risk; Co-infection; Live bird market
9.  The Spatial Resolution of Epidemic Peaks 
PLoS Computational Biology  2014;10(4):e1003561.
The emergence of novel respiratory pathogens can challenge the capacity of key health care resources, such as intensive care units, that are constrained to serve only specific geographical populations. An ability to predict the magnitude and timing of peak incidence at the scale of a single large population would help to accurately assess the value of interventions designed to reduce that peak. However, current disease-dynamic theory does not provide a clear understanding of the relationship between: epidemic trajectories at the scale of interest (e.g. city); population mobility; and higher resolution spatial effects (e.g. transmission within small neighbourhoods). Here, we used a spatially-explicit stochastic meta-population model of arbitrary spatial resolution to determine the effect of resolution on model-derived epidemic trajectories. We simulated an influenza-like pathogen spreading across theoretical and actual population densities and varied our assumptions about mobility using Latin-Hypercube sampling. Even though, by design, cumulative attack rates were the same for all resolutions and mobilities, peak incidences were different. Clear thresholds existed for all tested populations, such that models with resolutions lower than the threshold substantially overestimated population-wide peak incidence. The effect of resolution was most important in populations which were of lower density and lower mobility. With the expectation of accurate spatial incidence datasets in the near future, our objective was to provide a framework for how to use these data correctly in a spatial meta-population model. Our results suggest that there is a fundamental spatial resolution for any pathogen-population pair. If underlying interactions between pathogens and spatially heterogeneous populations are represented at this resolution or higher, accurate predictions of peak incidence for city-scale epidemics are feasible.
Author Summary
Fundamental spatial processes such as individuals' interactions and movement are not sufficiently well understood and yet they define the transmission of infectious diseases through populations. Spatial models of epidemics represent the region of interest (such as a city or country) as a collection of spatial units. To anticipate the magnitude and timing of peak incidence and to predict demand on health care resources in the region a clear understanding is needed of the relationship between the resolution of the representation (number and size of the pixels), the population interactions and the epidemic trajectories. We used a spatially explicit meta-population model of disease transmission to demonstrate that thresholds existed such that models with too low a resolution overestimated peak incidence, implying that ill-defined models may result in incorrect predictions. However, the results suggest that if population interactions are represented in sufficient detail, accurate estimates of peak demands on key health care resources are feasible.
PMCID: PMC3983068  PMID: 24722420
10.  Distinguishing Between Reservoir Exposure and Human-to-Human Transmission for Emerging Pathogens Using Case Onset Data 
PLoS Currents  2014;6:ecurrents.outbreaks.e1473d9bfc99d080ca242139a06c455f.
Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%–32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.
PMCID: PMC3946006  PMID: 24619563
H7N9; infectious diseases; Influenza; statistical inference; zoonoses
11.  Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility 
The Lancet Infectious Diseases  2014;14(1):50-56.
The novel Middle East respiratory syndrome coronavirus (MERS-CoV) had, as of Aug 8, 2013, caused 111 virologically confirmed or probable human cases of infection worldwide. We analysed epidemiological and genetic data to assess the extent of human infection, the performance of case detection, and the transmission potential of MERS-CoV with and without control measures.
We assembled a comprehensive database of all confirmed and probable cases from public sources and estimated the incubation period and generation time from case cluster data. Using data of numbers of visitors to the Middle East and their duration of stay, we estimated the number of symptomatic cases in the Middle East. We did independent analyses, looking at the growth in incident clusters, the growth in viral population, the reproduction number of cluster index cases, and cluster sizes to characterise the dynamical properties of the epidemic and the transmission scenario.
The estimated number of symptomatic cases up to Aug 8, 2013, is 940 (95% CI 290–2200), indicating that at least 62% of human symptomatic cases have not been detected. We find that the case-fatality ratio of primary cases detected via routine surveillance (74%; 95% CI 49–91) is biased upwards because of detection bias; the case-fatality ratio of secondary cases was 20% (7–42). Detection of milder cases (or clinical management) seemed to have improved in recent months. Analysis of human clusters indicated that chains of transmission were not self-sustaining when infection control was implemented, but that R in the absence of controls was in the range 0·8–1·3. Three independent data sources provide evidence that R cannot be much above 1, with an upper bound of 1·2–1·5.
By showing that a slowly growing epidemic is underway either in human beings or in an animal reservoir, quantification of uncertainty in transmissibility estimates, and provision of the first estimates of the scale of the epidemic and extent of case detection biases, we provide valuable information for more informed risk assessment.
Medical Research Council, Bill & Melinda Gates Foundation, EU FP7, and National Institute of General Medical Sciences.
PMCID: PMC3895322  PMID: 24239323
12.  Minimizing the threat of pandemic emergence from avian influenza in poultry systems 
BMC Infectious Diseases  2013;13:592.
Live-animal markets are a culturally important feature of meat distribution chains in many populations, yet they provide an opportunity for the maintenance and transmission of potentially emergent zoonotic pathogens. The ongoing human outbreak of avian H7N9 in China highlights the need for increased surveillance and control in these live-bird markets (LBMs).
Closure of retail markets in affected areas rapidly decreased human cases to rare, sporadic occurrence, but little attention has been paid thus far to the role of upstream elements of the poultry distribution chain such as wholesale markets. This could partly explain why transmission in poultry populations has not been eliminated more broadly. We present surveillance data from both wholesale live-bird markets (wLBMs) and rLBMs in Shantou, China (from 2004–2006), and call on disease-dynamic theory to illustrate why closing rLBMs has only minor effects on the overall volume of transmission. We show that the length of time birds stay in rLBMs can severely limit transmission there, but that the system-wide effect may be reduced substantially by high levels of transmission upstream of retail markets.
Management plans that minimize transmission throughout the entire poultry supply chain are essential for minimizing exposure to the public. These include reducing stay-time of birds in markets to 1 day, standardizing poultry supply chains to limit transmission in pre-retail settings, and monitoring strains with epidemiological traits that pose a high risk of emergence. These actions will further limit human exposure to extant viruses and reduce the likelihood of the emergence of novel strains by decreasing the overall volume of transmission.
PMCID: PMC3878446  PMID: 24341669
Avian influenza; Live-bird markets; Efficacy of controls; Environmental transmission; Wholesale markets
13.  Complex Disease Dynamics and the Design of Influenza Vaccination Programs 
PLoS Medicine  2013;10(11):e1001553.
Reflecting on new research by Cécile Viboud and colleagues, Steven Riley describes how understanding complex influenza dynamics can aid the design of influenza programs in China.
Please see later in the article for the Editors' Summary
PMCID: PMC3833833  PMID: 24260027
14.  Location-specific patterns of exposure to recent pre-pandemic strains of influenza A in southern China 
Nature communications  2011;2:423.
Variation in influenza incidence between locations is commonly observed on large spatial scales. It is unclear whether such variation occurs on smaller spatial scales and whether it is the result of heterogeneities in population demographics or more subtle differences in population structure and connectivity. Here we show significant differences in immunity to influenza A viruses among communities in China not explained by differences in population demographics. We randomly selected households from 5 randomly selected locations near Guangzhou, China to answer a questionnaire and provide a blood sample for serological testing against 5 recently circulating influenza viruses. We find a significant reduction in the frequency of detectable neutralization titers with increasing age, leveling off in older age groups. There are significant differences between locations in age, employment status, vaccination history, household size and housing conditions. However, after adjustment, significant variations in the frequency of detectable neutralization titers persists between locations. These results suggest there are characteristics of communities that drive influenza transmission dynamics apart from individual and household level risk factors, and that such factors have effects independent of strain.
PMCID: PMC3757505  PMID: 21829185
epidemiology; influenza; dynamics
15.  PLOS Currents: Outbreaks --- For findings that the world just can't wait to see 
PLoS Currents  2013;5:ecurrents.outbreaks.8ed218c079fbded60c505f025ed45f67.
PMCID: PMC3712484  PMID: 23872871
16.  Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations 
PLoS Computational Biology  2013;9(5):e1003064.
Rapidly characterizing the amplitude and variability in transmissibility of novel human influenza strains as they emerge is a key public health priority. However, comparison of early estimates of the basic reproduction number during the 2009 pandemic were challenging because of inconsistent data sources and methods. Here, we define and analyze influenza-like-illness (ILI) case data from 2009–2010 for the 50 largest spatially distinct US military installations (military population defined by zip code, MPZ). We used publicly available data from non-military sources to show that patterns of ILI incidence in many of these MPZs closely followed the pattern of their enclosing civilian population. After characterizing the broad patterns of incidence (e.g. single-peak, double-peak), we defined a parsimonious SIR-like model with two possible values for intrinsic transmissibility across three epochs. We fitted the parameters of this model to data from all 50 MPZs, finding them to be reasonably well clustered with a median (mean) value of 1.39 (1.57) and standard deviation of 0.41. An increasing temporal trend in transmissibility (, p-value: 0.013) during the period of our study was robust to the removal of high transmissibility outliers and to the removal of the smaller 20 MPZs. Our results demonstrate the utility of rapidly available – and consistent – data from multiple populations.
Author Summary
The ability to rapidly and reliably characterize novel strains of influenza in terms of their transmissibility is crucial for health planners: without good estimates for key parameters it is not possible to identify the appropriate strength of interventions or the spatial optimization of interventions based on variability in transmissibility. While the transmission of influenza in civilian societies has been relatively well-studied, it has received considerably less attention within military populations; yet the consequences, particularly during wartime, are arguably far greater. We have investigated the incidence for the 50 largest military installations in the USA, and, to the extent possible, compared them with the profiles of the enclosing civilian populations during the 2009 influenza pandemic. We infer that the local civilian population drove the timing of peak incidence at the military installations. We also developed and applied a two-peak SIR model to capture the essential properties of the pandemic at each installation, finding that transmissibility tended to increase during the course of the pandemic.
PMCID: PMC3656103  PMID: 23696723
17.  Anticipating the Prevalence of Avian Influenza Subtypes H9 and H5 in Live-Bird Markets 
PLoS ONE  2013;8(2):e56157.
An ability to forecast the prevalence of specific subtypes of avian influenza viruses (AIV) in live-bird markets would facilitate greatly the implementation of preventative measures designed to minimize poultry losses and human exposure. The minimum requirement for developing predictive quantitative tools is surveillance data of AIV prevalence sampled frequently over several years. Recently, a 4-year time series of monthly sampling of hemagglutinin subtypes 1–13 in ducks, chickens and quail in live-bird markets in southern China has become available. We used these data to investigate whether a simple statistical model, based solely on historical data (variables such as the number of positive samples in host X of subtype Y time t months ago), could accurately predict prevalence of H5 and H9 subtypes in chickens. We also examined the role of ducks and quail in predicting prevalence in chickens within the market setting because between-species transmission is thought to occur within markets but has not been measured. Our best statistical models performed remarkably well at predicting future prevalence (pseudo-R2 = 0.57 for H9 and 0.49 for H5), especially considering the multi-host, multi-subtype nature of AIVs. We did not find prevalence of H5/H9 in ducks or quail to be predictors of prevalence in chickens within the Chinese markets. Our results suggest surveillance protocols that could enable more accurate and timely predictive statistical models. We also discuss which data should be collected to allow the development of mechanistic models.
PMCID: PMC3567063  PMID: 23409145
18.  Evidence for Antigenic Seniority in Influenza A (H3N2) Antibody Responses in Southern China 
PLoS Pathogens  2012;8(7):e1002802.
A key observation about the human immune response to repeated exposure to influenza A is that the first strain infecting an individual apparently produces the strongest adaptive immune response. Although antibody titers measure that response, the interpretation of titers to multiple strains – from the same sera – in terms of infection history is clouded by age effects, cross reactivity and immune waning. From July to September 2009, we collected serum samples from 151 residents of Guangdong Province, China, 7 to 81 years of age. Neutralization tests were performed against strains representing six antigenic clusters of H3N2 influenza circulating between 1968 and 2008, and three recent locally circulating strains. Patterns of neutralization titers were compared based on age at time of testing and age at time of the first isolation of each virus. Neutralization titers were highest for H3N2 strains that circulated in an individual's first decade of life (peaking at 7 years). Further, across strains and ages at testing, statistical models strongly supported a pattern of titers declining smoothly with age at the time a strain was first isolated. Those born 10 or more years after a strain emerged generally had undetectable neutralization titers to that strain (<1∶10). Among those over 60 at time of testing, titers tended to increase with age. The observed pattern in H3N2 neutralization titers can be characterized as one of antigenic seniority: repeated exposure and the immune response combine to produce antibody titers that are higher to more ‘senior’ strains encountered earlier in life.
Author Summary
The human immune response to an influenza infection is not the same for every infection. It has often been observed that we tend to have the highest antibody titer (and presumably our strongest immune response) against strains of influenza that we were exposed to early in life. In this study, we obtained blood samples from 151 people between 7 and 81 years of age and tested the samples for the concentration of antibodies to many different (H3N2) strains. We chose strains according to when they first circulated, starting with a strain isolated just after the 1968 pandemic and going all the way through to very recent strains. We found that a participant's age at the time a strain first circulated was very predictive of the strength of their antibody against that strain. Not just for the first strain they were likely to have seen, but also for the second, third and all subsequent strains circulating during their lifetime. This suggests to us that antibody titers to influenza A H3N2 follow a pattern of antigenic seniority, suggesting that we produce progressively fewer specific antibodies to each subsequent infection as we age.
PMCID: PMC3400560  PMID: 22829765
19.  The infection attack rate and severity of 2009 pandemic influenza (H1N1) in Hong Kong 
Serial cross-sectional data on antibody levels to 2009 pandemic influenza A (H1N1) virus from a population can be used to estimate the infection attack rates and immunity against future infection in the community.
Between April and December 2009, we obtained 12,217 serum specimens from blood donors (16–59 yo), 2,520 from hospital outpatients (5–59yo), and 917 from subjects of a community pediatric cohort study (5–14yo). We estimated infection attack rates by comparing the proportions of specimens with antibody titers ≥1:40 by viral microneutralization before and after the first wave of the pandemic. Estimates were validated using paired sera from 324 individuals that spanned the first wave. Combining these estimates with epidemiologic surveillance data, we calculated the proportion of infections that led to hospitalization, intensive care admission, and death.
We found that 3.3% and 14% of 5–59 yo had antibody titers ≥1:40 before and after the first wave. The overall attack rate was 10.7% with the following age-stratification: 43.4% in 5–14 yo, 15.8% in 15–19 yo, 11.8% in 20–29 yo, and 4–4.6% in 30–59 yo. Case-hospitalization rates were 0.47%–0.87% among 5–59 yo. Case-ICU and case-fatality rates increased from 7.9 and 0.4 per 100,000 infections in 5–14 yo to 75 and 26.5 per 100,000 infections in 50–59 yo.
Almost half of all school-children in Hong Kong were infected during the first wave. Compared to school-children aged 5–14, older adults aged 50–59 had 9.5 and 66 times higher risk of ICU admission and death if infected.
PMCID: PMC3034199  PMID: 20964521
Influenza; serology; attack rate; case-fatality rate; public health
20.  Seroconversion to Pandemic (H1N1) 2009 Virus and Cross-Reactive Immunity to Other Swine Influenza Viruses 
Emerging Infectious Diseases  2011;17(10):1897-1899.
To assess herd immunity to swine influenza viruses, we determined antibodies in 28 paired serum samples from participants in a prospective serologic cohort study in Hong Kong who had seroconverted to pandemic (H1N1) 2009 virus. Results indicated that infection with pandemic (H1N1) 2009 broadens cross-reactive immunity to other recent subtype H1 swine viruses.
PMCID: PMC3310680  PMID: 22000365
human; immunity; swine; influenza; pandemic; H1N1; virus; dispatch
21.  Modelling the Proportion of Influenza Infections within Households during Pandemic and Non-Pandemic Years 
PLoS ONE  2011;6(7):e22089.
The key epidemiological difference between pandemic and seasonal influenza is that the population is largely susceptible during a pandemic, whereas, during non-pandemic seasons a level of immunity exists. The population-level efficacy of household-based mitigation strategies depends on the proportion of infections that occur within households. In general, mitigation measures such as isolation and quarantine are more effective at the population level if the proportion of household transmission is low.
We calculated the proportion of infections within households during pandemic years compared with non-pandemic years using a deterministic model of household transmission in which all combinations of household size and individual infection states were enumerated explicitly. We found that the proportion of infections that occur within households was only partially influenced by the hazard h of infection within household relative to the hazard of infection outside the household, especially for small basic reproductive numbers. During pandemics, the number of within-household infections was lower than one might expect for a given because many of the susceptible individuals were infected from the community and the number of susceptible individuals within household was thus depleted rapidly. In addition, we found that for the value of at which 30% of infections occur within households during non-pandemic years, a similar 31% of infections occur within households during pandemic years.
We suggest that a trade off between the community force of infection and the number of susceptible individuals in a household explains an apparent invariance in the proportion of infections that occur in households in our model. During a pandemic, although there are more susceptible individuals in a household, the community force of infection is very high. However, during non-pandemic years, the force of infection is much lower but there are fewer susceptible individuals within the household.
PMCID: PMC3136504  PMID: 21779380
22.  Epidemiological Characteristics of 2009 (H1N1) Pandemic Influenza Based on Paired Sera from a Longitudinal Community Cohort Study 
PLoS Medicine  2011;8(6):e1000442.
Steven Riley and colleagues analyze a community cohort study from the 2009 (H1N1) influenza pandemic in Hong Kong, and found that more children than adults were infected with H1N1, but children were less likely to progress to severe disease than adults.
While patterns of incidence of clinical influenza have been well described, much uncertainty remains over patterns of incidence of infection. The 2009 pandemic provided both the motivation and opportunity to investigate patterns of mild and asymptomatic infection using serological techniques. However, to date, only broad epidemiological patterns have been defined, based on largely cross-sectional study designs with convenience sampling frameworks.
Methods and Findings
We conducted a paired serological survey of a cohort of households in Hong Kong, recruited using random digit dialing, and gathered data on severe confirmed cases from the public hospital system (>90% inpatient days). Paired sera were obtained from 770 individuals, aged 3 to 103, along with detailed individual-level and household-level risk factors for infection. Also, we extrapolated beyond the period of our study using time series of severe cases and we simulated alternate study designs using epidemiological parameters obtained from our data. Rates of infection during the period of our study decreased substantially with age: for 3–19 years, the attack rate was 39% (31%–49%); 20–39 years, 8.9% (5.3%–14.7%); 40–59 years, 5.3% (3.5%–8.0%); and 60 years or older, 0.77% (0.18%–4.2%). We estimated parameters for a parsimonious model of infection in which a linear age term and the presence of a child in the household were used to predict the log odds of infection. Patterns of symptom reporting suggested that children experienced symptoms more often than adults. The overall rate of confirmed pandemic (H1N1) 2009 influenza (H1N1pdm) deaths was 7.6 (6.2–9.5) per 100,000 infections. However, there was substantial and progressive increase in deaths per 100,000 infections with increasing age from 0.66 (0.65–0.86) for 3–19 years up to 220 (50–4,000) for 60 years and older. Extrapolating beyond the period of our study using rates of severe disease, we estimated that 56% (43%–69%) of 3–19 year olds and 16% (13%–18%) of people overall were infected by the pandemic strain up to the end of January 2010. Using simulation, we found that, during 2009, larger cohorts with shorter follow-up times could have rapidly provided similar data to those presented here.
Should H1N1pdm evolve to be more infectious in older adults, average rates of severe disease per infection could be higher in future waves: measuring such changes in severity requires studies similar to that described here. The benefit of effective vaccination against H1N1pdm infection is likely to be substantial for older individuals. Revised pandemic influenza preparedness plans should include prospective serological cohort studies. Many individuals, of all ages, remained susceptible to H1N1pdm after the main 2009 wave in Hong Kong.
Please see later in the article for the Editors' Summary
Editors' Summary
From June 2009 to August 2010, the world was officially (according to specific WHO criteria—WHO phase 6 pandemic alert) in the grip of an Influenza A pandemic with a new strain of the H1N1 virus. During this time, more than 214 countries and overseas territories reported laboratory confirmed cases of pandemic influenza H1N1 2009 with almost 20,000 deaths.
While much is already known about patterns of incidence of clinical influenza, the patterns of infection incidence are much more uncertain, because many influenza infections are either asymptomatic or cause only mild symptoms. This means that it is difficult to obtain accurate estimates of risk factors for infection and the overall burden of disease using only clinical surveillance. However, without accurate estimates of infection incidence across different risk groups, it is not possible to establish the number of infections that give rise to severe disease (the per infection rate of severe disease). Consequently, it is difficult to give evidence-based advice for individuals, groups, and populations about the potential benefits of interventions including drugs and vaccines that might reduce the risk of influenza infection.
Why Was This Study Done?
During the 2009 pandemic, some countries and territories, such as Hong Kong, were able to investigate patterns of mild and asymptomatic infection using serological techniques, thus providing information that may help to fill this knowledge gap. Given the high levels of polymerase chain reaction (PCR) testing and the robust reporting of hospital episodes, the main H1N1 pandemic wave in Hong Kong (during September 2009) provided an opportunity to implement a prospective cohort study to investigate the incidence of infection.
What Did the Researchers Do and Find? The researchers collected data on the asymptomatic symptoms of influenza by randomly selecting households to participate in the study. Each member of the household willing to participate had a baseline blood sample taken before the main wave of the pandemic (July to September 2009), then, when clinical surveillance suggested that the main peak in transmission had passed, after the main wave (November 2009 to February 2010). During the study period, participants were asked to report any flu-like symptoms in three ways: to phone the study team and report symptoms in real time; to fill out a paper diary with the day and symptoms; and to report symptoms during a follow-up interview. In parallel, the researchers monitored data on every patient with H1N1 admitted to intensive care units or who died while in the hospital. The researchers then estimated the number of H1N1 infections (infection incidence) per severe case by developing a likelihood-based framework. They used a simulation model to investigate alternate study designs and to validate their estimates of the rate of severe disease per infection.
Using these methods, the researchers found that rates of H1N1 infection during the study period decreased substantially with age: for 3–19 years, the attack rate was 39%; 20–39 years, 8.9%; 40–59 years, 5.3%; and 60 years or older, 0.77%. In addition, patterns of symptom reporting indicated that children experienced symptoms more often than adults. The overall rate of confirmed H1N1 deaths was 7.6 per 100,000 infections. However, there was a substantial and progressive increase in deaths per 100,000 infections with increasing age from 0.66 for 3–19 years up to 220 for 60 years and older. Statistical modeling suggested that 56% of 3–19 year olds and 16% of people overall were infected by the pandemic strain up to the end of January 2010.
What Do These Findings Mean?
The results of this study suggest that more children were infected with H1N1 than adults but most of them did not progress to severe disease. Conversely, although fewer older adults were infected with H1N1, this group was much more likely to experience severe disease. Therefore, should H1N1 infection incidence ever increase in older adults, for example by evolving to become more infectious to this group, average rates of severe disease per infection could be much higher than for the 2009 pandemic. Revised pandemic preparedness plans should include prospective serological cohort studies, such as this one, in order to be able to estimate rates of severe disease per infection.
Additional Information
Please access these Web sites via the online version of this summary at
WHO has information about the global response to the 2009 H1N1 pandemic
WHO also provides recommendations for the H1N1 post-pandemic period
The government of Hong Kong's Centre for Health Protection provides information about H1N1 in Hong Kong
PMCID: PMC3119689  PMID: 21713000
24.  Estimation of the serial interval of influenza 
Epidemiology (Cambridge, Mass.)  2009;20(3):344-347.
Estimates of the clinical-onset serial interval of human influenza infection (time between onset of symptoms in an index case and a secondary case) are used to inform public health policy and to construct mathematical models of influenza transmission. We estimate the serial interval of laboratory-confirmed influenza transmission in households.
Index cases were recruited after reporting to a primary healthcare center with symptoms. Members of their households were followed up with repeated home visits.
Assuming a Weibull model and accounting for selection bias inherent in our field study design, we used symptom-onset times from 14 pairs of infector/infectee to estimate a mean serial interval of 3.6 days (95% confidence interval = 2.9–4.3 days), with standard deviation 1.6 days.
The household serial interval of influenza may be longer than previously estimated. Studies of the complete serial interval, based on transmission in all community contexts, are a priority.
PMCID: PMC3057478  PMID: 19279492
25.  Efficient simulation of the spatial transmission dynamics of influenza 
PLoS Currents  2010;2:RRN1141.
Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially-resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we describe a significant improvement in the efficiency of an individual-based stochastic disease simulation framework that has been used for multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations.
PMCID: PMC2808187  PMID: 20130781

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