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Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that “sensor-detectable”, proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than “self-reportable” talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency. [ABSTRACT FROM AUTHOR]

Developing appropriate risk messages during challenging situations like public health outbreaks is complicated. The focus of this paper is on how First Nations and Metis people in Manitoba, Canada, responded to the public health management of pandemic H1N1, using a focus group methodology (n = 23 focus groups). Focus group conversations explored participant reactions to messaging regarding the identification of H1N1 virus risk groups, the H1N1 vaccine and how priority groups to receive the vaccine were established. To better contextualize the intentions of public health professionals, key informant interviews (n = 20) were conducted with different health decision makers (e.g., public health officials, people responsible for communications, representatives from some First Nations and Metis self-governing organizations). While risk communication practice has improved, ‘one size’ messaging campaigns do not work effectively, particularly when communicating about who is most ‘at-risk’. Public health agencies need to pay more attention to the specific socio-economic, historical and cultural contexts of First Nations and Metis citizens when planning for, communicating and managing responses associated with pandemic outbreaks to better tailor both the messages and delivery. More attention is needed to directly engage First Nations and Metis communities in the development and dissemination of risk messaging. [ABSTRACT FROM AUTHOR]

The worldwide spread of a novel influenza A (H1N1) virus in 2009 showed that influenza remains a significant health threat, even for individuals in the prime of life. This paper focuses on the unusually high young adult mortality observed during the Spanish flu pandemic of 1918. Using historical records from Canada and the U.S., we report a peak of mortality at the exact age of 28 during the pandemic and argue that this increased mortality resulted from an early life exposure to influenza during the previous Russian flu pandemic of 1889–90. We posit that in specific instances, development of immunological memory to an influenza virus strain in early life may lead to a dysregulated immune response to antigenically novel strains encountered in later life, thereby increasing the risk of death. Exposure during critical periods of development could also create holes in the T cell repertoire and impair fetal maturation in general, thereby increasing mortality from infectious diseases later in life. Knowledge of the age-pattern of susceptibility to mortality from influenza could improve crisis management during future influenza pandemics. [ABSTRACT FROM AUTHOR]

Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China. [ABSTRACT FROM AUTHOR]

Infectious disease transmission in animals is an inherently spatial process in which a host’s home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that a uniform local attack rate is only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a movement kernel), and a natural history consistent with pandemic influenza; we show that local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, under the less intuitively likely scenario, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The strength and direction of correlation changes sign for other kernel parameter values. We show that simulation models in which it is assumed implicitly that only infectious individuals move are assuming a slightly unusual specific correlation between population density and attack rate. However, we also show that this potential structural bias can be corrected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic dual-mobility kernel. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates. More generally, these results suggest a genuine risk that mechanistic models of high-resolution attack rate data may reach spurious conclusions if the precise implications of spatial force-of-infection assumptions are not first fully characterized, prior to models being fit to data. [ABSTRACT FROM AUTHOR]

Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry. The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST2 phylogenetics platform. [ABSTRACT FROM AUTHOR]

Public health related decisions often have to balance the cost of intervention strategies with the benefit of the reduction in disease burden. While the cost can often be inferred, forward modelling of the effect of different intervention options is complicated and disease specific. Here we introduce a package that is aimed to simplify this process. The package allows one to infer parameters using a Bayesian approach, perform forward modelling of the likely results of the proposed intervention and finally perform cost effectiveness analysis of the results. The package is based on a method previously used in the UK to inform vaccination strategies for influenza, with extensions to make it easily adaptable to other diseases and data sources. [ABSTRACT FROM AUTHOR]

In recent years, serious infectious diseases tend to transcend national borders and widely spread in a global scale. The incidence and prevalence of epidemics are highly influenced not only by pathogen-dependent disease characteristics such as the force of infection, the latent period, and the infectious period, but also by human mobility and contact patterns. However, the effect of heterogeneous mobility of individuals on epidemic outcomes is not fully understood. Here, we aim to elucidate how spatial mobility of individuals contributes to the final epidemic size in a spatial susceptible-exposed-infectious-recovered (SEIR) model with mobile individuals in a square lattice. After illustrating the interplay between the mobility parameters and the other parameters on the spatial epidemic spreading, we propose an index as a function of system parameters, which largely governs the final epidemic size. The main contribution of this study is to show that the proposed index is useful for estimating how parameter scaling affects the final epidemic size. To demonstrate the effectiveness of the proposed index, we show that there is a positive correlation between the proposed index computed with the real data of human airline travels and the actual number of positive incident cases of influenza B in the entire world, implying that the growing incidence of influenza B is attributed to increased human mobility. [ABSTRACT FROM AUTHOR]

Reliable discrimination of recent influenza A infection from previous exposure using hemagglutination inhibition (HI) or virus neutralization tests is currently not feasible. This is due to low sensitivity of the tests and the interference of antibody responses generated by previous infections. Here we investigate the diagnostic characteristics of a newly developed antibody (HA1) protein microarray using data from cross-sectional serological studies carried out before and after the pandemic of 2009. The data are analysed by mixture models, providing a probabilistic classification of sera (susceptible, prior-exposed, recently infected). Estimated sensitivity and specificity for identifying A/2009 infections are low using HI (66% and 51%), and high when using A/2009 microarray data alone or together with A/1918 microarray data (96% and 95%). As a heuristic, a high A/2009 to A/1918 antibody ratio (>1.05) is indicative of recent infection, while a low ratio is indicative of a pre-existing response, even if the A/2009 titer is high. We conclude that highly sensitive and specific classification of individual sera is possible using the protein microarray, thereby enabling precise estimation of age-specific infection attack rates in the population even if sample sizes are small. [ABSTRACT FROM AUTHOR]

Background: The role of meteorological factors on influenza transmission in the tropics is less defined than in the temperate regions. We assessed the association between influenza activity and temperature, specific humidity and rainfall in 6 study areas that included 11 departments or provinces within 3 tropical Central American countries: Guatemala, El Salvador and Panama. Method/Findings: Logistic regression was used to model the weekly proportion of laboratory-confirmed influenza positive samples during 2008 to 2013 (excluding pandemic year 2009). Meteorological data was obtained from the Tropical Rainfall Measuring Mission satellite and the Global Land Data Assimilation System. We found that specific humidity was positively associated with influenza activity in El Salvador (Odds Ratio (OR) and 95% Confidence Interval of 1.18 (1.07–1.31) and 1.32 (1.08–1.63)) and Panama (OR = 1.44 (1.08–1.93) and 1.97 (1.34–2.93)), but negatively associated with influenza activity in Guatemala (OR = 0.72 (0.6–0.86) and 0.79 (0.69–0.91)). Temperature was negatively associated with influenza in El Salvador's west-central departments (OR = 0.80 (0.7–0.91)) whilst rainfall was positively associated with influenza in Guatemala's central departments (OR = 1.05 (1.01–1.09)) and Panama province (OR = 1.10 (1.05–1.14)). In 4 out of the 6 locations, specific humidity had the highest contribution to the model as compared to temperature and rainfall. The model performed best in estimating 2013 influenza activity in Panama and west-central El Salvador departments (correlation coefficients: 0.5–0.9). Conclusions/Significance: The findings highlighted the association between influenza activity and specific humidity in these 3 tropical countries. Positive association with humidity was found in El Salvador and Panama. Negative association was found in the more subtropical Guatemala, similar to temperate regions. Of all the study locations, Guatemala had annual mean temperature and specific humidity that were lower than the others. [ABSTRACT FROM AUTHOR]

The 2009 H1N1 influenza pandemic provides a unique opportunity for detailed examination of the spatial dynamics of an emerging pathogen. In the US, the pandemic was characterized by substantial geographical heterogeneity: the 2009 spring wave was limited mainly to northeastern cities while the larger fall wave affected the whole country. Here we use finely resolved spatial and temporal influenza disease data based on electronic medical claims to explore the spread of the fall pandemic wave across 271 US cities and associated suburban areas. We document a clear spatial pattern in the timing of onset of the fall wave, starting in southeastern cities and spreading outwards over a period of three months. We use mechanistic models to tease apart the external factors associated with the timing of the fall wave arrival: differential seeding events linked to demographic factors, school opening dates, absolute humidity, prior immunity from the spring wave, spatial diffusion, and their interactions. Although the onset of the fall wave was correlated with school openings as previously reported, models including spatial spread alone resulted in better fit. The best model had a combination of the two. Absolute humidity or prior exposure during the spring wave did not improve the fit and population size only played a weak role. In conclusion, the protracted spread of pandemic influenza in fall 2009 in the US was dominated by short-distance spatial spread partially catalysed by school openings rather than long-distance transmission events. This is in contrast to the rapid hierarchical transmission patterns previously described for seasonal influenza. The findings underline the critical role that school-age children play in facilitating the geographic spread of pandemic influenza and highlight the need for further information on the movement and mixing patterns of this age group. [ABSTRACT FROM AUTHOR]

Background: Historically, counting influenza recorded in administrative health outcome databases has been considered insufficient to estimate influenza attributable morbidity and mortality in populations. We used database record linkage to evaluate whether modern databases have similar limitations. Methods: Person-level records were linked across databases of laboratory notified influenza, emergency department (ED) presentations, hospital admissions and death registrations, from the population (∼6.9 million) of New South Wales (NSW), Australia, 2005 to 2008. Results: There were 2568 virologically diagnosed influenza infections notified. Among those, 25% of 40 who died, 49% of 1451 with a hospital admission and 7% of 1742 with an ED presentation had influenza recorded on the respective database record. Compared with persons aged ≥65 years and residents of regional and remote areas, respectively, children and residents of major cities were more likely to have influenza coded on their admission record. Compared with older persons and admitted patients, respectively, working age persons and non-admitted persons were more likely to have influenza coded on their ED record. On both ED and admission records, persons with influenza type A infection were more likely than those with type B infection to have influenza coded. Among death registrations, hospital admissions and ED presentations with influenza recorded as a cause of illness, 15%, 28% and 1.4%, respectively, also had laboratory notified influenza. Time trends in counts of influenza recorded on the ED, admission and death databases reflected the trend in counts of virologically diagnosed influenza. Conclusions: A minority of the death, hospital admission and ED records for persons with a virologically diagnosed influenza infection identified influenza as a cause of illness. Few database records with influenza recorded as a cause had laboratory confirmation. The databases have limited value for estimating incidence of influenza outcomes, but can be used for monitoring variation in incidence over time. [ABSTRACT FROM AUTHOR]

In 2008, 800 adults living within rural Kampong Cham Province, Cambodia were enrolled in a prospective cohort study of zoonotic influenza transmission. After enrollment, participants were contacted weekly for 24 months to identify acute influenza-like illnesses (ILI). Follow-up sera were collected at 12 and 24 months. A transmission substudy was also conducted among the family contacts of cohort members reporting ILI who were influenza A positive. Samples were assessed using serological or molecular techniques looking for evidence of infection with human and avian influenza viruses. Over 24 months, 438 ILI investigations among 284 cohort members were conducted. One cohort member was hospitalized with a H5N1 highly pathogenic avian influenza (HPAI) virus infection and withdrew from the study. Ninety-seven ILI cases (22.1%) were identified as influenza A virus infections by real-time RT-PCR; none yielded evidence for AIV. During the 2 years of follow-up, 21 participants (3.0%) had detectable antibody titers (≥1∶10) against the studied AIVs: 1 against an avian-like A/Migratory duck/Hong Kong/MPS180/2003(H4N6), 3 against an avian-like A/Teal/Hong Kong/w312/97(H6N1), 9 (3 of which had detectible antibody titers at both 12- and 24-month follow-up) against an avian-like A/Hong Kong/1073/1999(H9N2), 6 (1 detected at both 12- and 24-month follow-up) against an avian-like A/Duck/Memphis/546/74(H11N9), and 2 against an avian-like A/Duck/Alberta/60/76(H12N5). With the exception of the one hospitalized cohort member with H5N1 infection, no other symptomatic avian influenza infections were detected among the cohort. Serological evidence for subclinical infections was sparse with only one subject showing a 4-fold rise in microneutralization titer over time against AvH12N5. In summary, despite conducting this closely monitored cohort study in a region enzootic for H5N1 HPAI, we were unable to detect subclinical avian influenza infections, suggesting either that these infections are rare or that our assays are insensitive at detecting them. [ABSTRACT FROM AUTHOR]

Patterns of poultry exposure in rural and urban areas in China have not been systematically evaluated and compared. The objective of our study is to investigate patterns in human exposure to poultry in rural and urban China. We conducted a two-stage household-based clustered survey on population exposure to live/sick/dead poultry in Xiuning and Shenzhen. Half of the rural households (51%) in Xiuning raised poultry, mostly (78%) free-range. Around half of those households (40%) allowed poultry to stay in their living areas. One quarter of villagers reported having contact with sick or dead poultry. In Shenzhen, 37% urban residents visited live poultry markets. Among these, 40% purchased live poultry and 16% touched the poultry or cages during purchase. Our findings indicated that human exposure to poultry was different in rural and urban areas in China. This discrepancy could contribute to the observed differences in epidemiologic characteristics between urban and rural cases of influenza A(H7N9) and A(H5N1) virus infection. [ABSTRACT FROM AUTHOR]

During late April 2009, the first cases of 2009 pandemic influenza A (H1N1) (pH1N1) in Illinois were reported. On-going, sustained local transmission resulted in an estimated 500,000 infected persons. We conducted a mixed method analysis using both quantitative (surveillance) and qualitative (interview) data; surveillance data was used to analyze demographic distribution of hospitalized cases and follow-up interview data was used to assess health seeking behavior. Invitations to participate in a telephone interview were sent to 120 randomly selected Illinois residents that were hospitalized during April–December 2009. During April–December 2009, 2,824 pH1N1 hospitalizations occurred in Illinois hospitals; median age (interquartile range) at admission was 24 (range: 6–49) years. Hospitalization rates/100,000 persons for blacks and Hispanics, regardless of age or sex were 2–3 times greater than for whites (blacks, 36/100,000 (95% Confidence Interval ([95% CI], 33–39)); Hispanics, 35/100,000 [95%CI,32–37] (; whites, 13/100,000[95%CI, 12–14); p<0.001). Mortality rates were higher for blacks (0.9/100,000; p<0.09) and Hispanics (1/100,000; p<0.04) when compared with the mortality rates for whites (0.6/100,000). Of 33 interview respondents, 31 (94%) stated that they had heard of pH1N1 before being hospitalized, and 24 (73%) did not believed they were at risk for pH1N1. On average, respondents reported experiencing symptoms for 2 days (range: 1–7) before seeking medical care. When asked how to prevent pH1N1 infection in the future, the most common responses were getting vaccinated and practicing hand hygiene. Blacks and Hispanics in Illinois experienced disproportionate pH1N1 hospitalization and mortality rates. Public health education and outreach efforts in preparation for future influenza pandemics should include prevention messaging focused on perception of risk, and ensure community wide access to prevention messages and practices. [ABSTRACT FROM AUTHOR]

Background: Influenza has been associated with heavy burden of mortality and morbidity in subtropical regions. However, timely forecast of influenza epidemic in these regions has been hindered by unclear seasonality of influenza viruses. In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen, China. Methods: Dynamic linear models with the predictors of single or multiple surveillance data for influenza-like illness (ILI) were adopted to forecast influenza epidemics from 2006 to 2012 in Shenzhen. Temporal coherence of these surveillance data with laboratory-confirmed influenza cases was evaluated by wavelet analysis and only the coherent data streams were entered into the model. Timeliness, sensitivity and specificity of these models were also evaluated to compare their performance. Results: Both influenza virology data and ILI consultation rates in Shenzhen demonstrated a significant annual seasonal cycle (p<0.05) during the entire study period, with occasional deviations observed in some data streams. The forecasting models that combined multi-stream ILI surveillance data generally outperformed the models with single-stream ILI data, by providing more timely, sensitive and specific alerts. Conclusions: Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen. [ABSTRACT FROM AUTHOR]

We analysed an 11-year dataset (1998–2009) of Influenza-Like Illness (ILI) that was based on surveillance of ∽23% of Israel's population. We examined whether the level of synchrony of ILI epidemics in Israel's 12 largest cities is high enough to view Israel as a single epidemiological unit. Two methods were developed to assess the synchrony: (1) City-specific attack rates were fitted to a simple model in order to estimate the temporal differences in attack rates and spatial differences in reporting rates of ILI. The model showed good fit to the data (R2  =  0.76) and revealed considerable differences in reporting rates of ILI in different cities (up to a factor of 2.2). (2) A statistical test was developed to examine the null hypothesis (H0) that ILI incidence curves in two cities are essentially identical, and was tested using ILI data. Upon examining all possible pairs of incidence curves, 77.4% of pairs were found not to be different (H0 was not rejected). It was concluded that all cities generally have the same attack rate and follow the same epidemic curve each season, although the attack rate changes from season to season, providing strong support for the “Israel is one city” hypothesis. The cities which were the most out of synchronization were Bnei Brak, Beersheba and Haifa, the latter two being geographically remote from all other cities in the dataset and the former geographically very close to several other cities but socially separate due to being populated almost exclusively by ultra-orthodox Jews. Further evidence of assortative mixing of the ultra-orthodox population can be found in the 2001–2002 season, when ultra-orthodox cities and neighborhoods showed distinctly different incidence curves compared to the general population. [ABSTRACT FROM AUTHOR]

Background: Little is known about risk factors for severe outcomes in patients infected with H5N1 and no systematic review has been conducted. Understanding risk factors is an important step for prioritizing prophylaxis or treatment in the event of a pandemic. Objectives: To systematically evaluate risk factors for severe outcomes in patients with avian influenza H5N1 infection. Data sources: MEDLINE, EMBASE, CINAHL, GlobalHealth, and CENTRAL through March 2011 Eligibility criteria for selecting studies: Observational studies of any design published in English, French, Spanish, German or Korean that reported on risk factor-outcome combinations of interest in participants with confirmed H5N1 infections. Outcomes considered included death, ventilator support, hospital and ICU admission, pneumonia, and composite outcomes. Study appraisal: Risk of bias was assessed using the Newcastle-Ottawa scale (NOS). Results: We identified 20 studies reporting on 999 patients infected with H5N1. The majority of studies (n = 14, 70%) were at intermediate risk of bias, i.e. 4–6 points on the NOS. Females were at increased risk of death (OR 1.75, 95% CI 1.27–2.44), while young age, in particular <5 years of age (OR 0.44, 95% CI 0.25–0.79 for death), was protective. Data on traditional risk factors was scarce and requires further studies. Another major limitation in the published literature was lack of adjustment for confounders. Interpretation: Females were at increased risk for complications following H5N1 infection while young age protected against severe outcomes. Research on traditional risk factors was limited and is required. [ABSTRACT FROM AUTHOR]

Background: Influenza is a viral infection that can lead to serious complications and death(s) in vulnerable groups if not diagnosed and managed in a timely manner. This study was conducted to improve the accuracy of predicting influenza through various clinical and statistical models. Methodology: A retrospective cross sectional analysis was done on demographic and epidemiological data collected from March 2009 to March 2010. Patients were classified as ILI or SARI using WHO case definitions. Respiratory specimens were tested by RT-PCR. Clinical symptoms and co-morbid conditions were analyzed using binary logistic regression models. Results: In the first approach, analysis compared children (≤12) and adults (>12). Of 1,243 cases, 262 (21%) tested positive for A(H1N1)pdm09 and the proportion of children (≤12) and adults (>12) were 27% and 73% respectively. Four symptoms predicted influenza in children: fever (OR 2.849, 95% CI 1.931–8.722), cough (OR 1.99, 95% CI 1.512–3.643), diarrhea (OR 2.100, 95% CI 2.040–3.25) and respiratory disease (OR 3.269, 95% CI 2.128–12.624). In adults, the strongest clinical predictor was fever (OR 2.80, 95% CI 1.025–3.135) followed by cough (OR 1.431, 95% CI 1.032–2.815). In the second instance, patients were separated into two groups: SARI 326 (26%) and ILI 917 (74%) cases. Male to female ratio was 1.41∶1.12 for SARI and 2∶1.5 for ILI cases. Chi-square test showed that fever, cough and sore throat were significant factors for A(H1N1)pdm09 infections (p = 0.008). Conclusion: Studies in a primary care setting should be encouraged focused on patients with influenza-like illness to develop sensitive clinical case definition that will help to improve accuracy of detecting influenza infections. Formulation of a standard “one size fits all” case definition that best correlates with influenza infections can help guide decisions for additional diagnostic testing and also discourage unjustified antibiotic prescription and usage in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Evidence regarding the mortality rate after administration of the pandemic influenza A (H1N1) 2009 vaccine on patients with underlying diseases is currently scarce. We conducted a case-control study in Japan to compare the mortality rates of patients with idiopathic interstitial pneumonia after the vaccines were administered and were not administered. Methods: Between October 2009 and March 2010, we collected clinical records in Japan and conducted a 1∶1 matched case-control study. Patients with idiopathic interstitial pneumonia who died during this period were considered case patients, and those who survived were considered control patients. We determined and compared the proportion of each group that received the pandemic influenza A (H1N1) 2009 vaccine and estimated the odds ratio. Finally, we conducted simulations that compensated for the shortcomings of the study associated with adjusted severity of idiopathic interstitial pneumonia. Results: The case and control groups each comprised of 75 patients with idiopathic interstitial pneumonia. The proportion of patients who received the pandemic influenza A (H1N1) 2009 vaccine was 30.7% and 38.7% for the case and control groups, respectively. During that winter, the crude conditional odds ratio of mortality was 0.63 (95% confidence interval, 0.25–1.47) and the adjusted conditional odds ratio was 1.18 (95% confidence interval, 0.33–4.49); neither was significant. The simulation study showed more accurate conditional odds ratios of 0.63–0.71. Conclusions: In our study, we detected no evidence that the influenza A (H1N1) 2009 vaccine increased the mortality rate of patients with idiopathic interstitial pneumonia. The results, however, are limited by the small sample size and low statistical power. A larger-scale study is required. [ABSTRACT FROM AUTHOR]

Background: China's one-child-per-couple policy, introduced in 1979, led to profound demographic changes for nearly a quarter of the world's population. Several decades later, the consequences include decreased fertility rates, population aging, decreased household sizes, changes in family structure, and imbalanced sex ratios. The epidemiology of communicable diseases may have been affected by these changes since the transmission dynamics of infectious diseases depend on demographic characteristics of the population. Of particular interest is influenza because China and Southeast Asia lie at the center of a global transmission network of influenza. Moreover, changes in household structure may affect influenza transmission. Is it possible that the pronounced demographic changes that have occurred in China have affected influenza transmission? Methods and Findings: To address this question, we developed a continuous-time, stochastic, individual-based simulation model for influenza transmission. With this model, we simulated 30 years of influenza transmission and compared influenza transmission rates in populations with and without the one-child policy control. We found that the average annual attack rate is reduced by 6.08% (SD 2.21%) in the presence of the one-child policy compared to a population in which no demographic changes occurred. There was no discernible difference in the secondary attack rate, −0.15% (SD 1.85%), between the populations with and without a one-child policy. We also forecasted influenza transmission over a ten-year time period in a population with a two-child policy under a hypothesis that a two-child-per-couple policy will be carried out in 2015, and found a negligible difference in the average annual attack rate compared to the population with the one-child policy. Conclusions: This study found that the average annual attack rate is slightly lowered in a population with a one-child policy, which may have resulted from a decrease in household size and the proportion of children in the population. [ABSTRACT FROM AUTHOR]

Avian (AIV) and equine influenza virus (EIV) have been repeatedly shown to circulate among Mongolia’s migrating birds or domestic horses. In 2009, 439 Mongolian adults, many with occupational exposure to animals, were enrolled in a prospective cohort study of zoonotic influenza transmission. Sera were drawn upon enrollment and again at 12 and 24 months. Participants were contacted monthly for 24 months and queried regarding episodes of acute influenza-like illnesses (ILI). Cohort members confirmed to have acute influenza A infections, permitted respiratory swab collections which were studied with rRT-PCR for influenza A. Serologic assays were performed against equine, avian, and human influenza viruses. Over the 2 yrs of follow-up, 100 ILI investigations in the cohort were conducted. Thirty-six ILI cases (36%) were identified as influenza A infections by rRT-PCR; none yielded evidence for AIV or EIV. Serological examination of 12 mo and 24 mo annual sera revealed 37 participants had detectable antibody titers (≥1∶10) against studied viruses during the course of study follow-up: 21 against A/Equine/Mongolia/01/2008(H3N8); 4 against an avian A/Teal/Hong Kong/w3129(H6N1), 11 against an avian-like A/Hong Kong/1073/1999(H9N2), and 1 against an avian A/Migrating duck/Hong Kong/MPD268/2007(H10N4) virus. However, all such titers were <1∶80 and none were statistically associated with avian or horse exposures. A number of subjects had evidence of seroconversion to zoonotic viruses, but the 4-fold titer changes were again not associated with avian or horse exposures. As elevated antibodies against seasonal influenza viruses were high during the study period, it seems likely that cross-reacting antibodies against seasonal human influenza viruses were a cause of the low-level seroreactivity against AIV or EIV. Despite the presence of AIV and EIV circulating among wild birds and horses in Mongolia, there was little evidence of AIV or EIV infection in this prospective study of Mongolians with animal exposures. [ABSTRACT FROM AUTHOR]

Objectives: Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics. Methods: We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003–2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI. Results: Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51. Conclusions: ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities. [ABSTRACT FROM AUTHOR]

Background: Communicable disease outbreaks of novel or existing pathogens threaten human health around the globe. It would be desirable to rapidly characterize such outbreaks and develop accurate projections of their duration and cumulative size even when limited preliminary data are available. Here we develop a mathematical model to aid public health authorities in tracking the expansion and contraction of outbreaks with explicit representation of factors (other than population immunity) that may slow epidemic growth. Methodology: The Incidence Decay and Exponential Adjustment (IDEA) model is a parsimonious function that uses the basic reproduction number R0, along with a discounting factor to project the growth of outbreaks using only basic epidemiological information (e.g., daily incidence counts). Principal Findings: Compared to simulated data, IDEA provides highly accurate estimates of total size and duration for a given outbreak when R0 is low or moderate, and also identifies turning points or new waves. When tested with an outbreak of pandemic influenza A (H1N1), the model generates estimated incidence at the i+1th serial interval using data from the ith serial interval within an average of 20% of actual incidence. Conclusions and Significance: This model for communicable disease outbreaks provides rapid assessments of outbreak growth and public health interventions. Further evaluation in the context of real-world outbreaks will establish the utility of IDEA as a tool for front-line epidemiologists. [ABSTRACT FROM AUTHOR]

Background: In 2008, 800 rural Thai adults living within Kamphaeng Phet Province were enrolled in a prospective cohort study of zoonotic influenza transmission. Serological analyses of enrollment sera suggested this cohort had experienced subclinical avian influenza virus (AIV) infections with H9N2 and H5N1 viruses. Methods: After enrollment, participants were contacted weekly for 24mos for acute influenza-like illnesses (ILI). Cohort members confirmed to have influenza A infections were enrolled with their household contacts in a family transmission study involving paired sera and respiratory swab collections. Cohort members also provided sera at 12 and 24 months after enrollment. Serologic and real-time RT-PCR assays were performed against avian, swine, and human influenza viruses. Results: Over the 2 yrs of follow-up, 81 ILI investigations in the cohort were conducted; 31 (38%) were identified as influenza A infections by qRT-PCR. Eighty-three household contacts were enrolled; 12 (14%) reported ILIs, and 11 (92%) of those were identified as influenza infections. A number of subjects were found to have slightly elevated antibodies against avian-like A/Hong Kong/1073/1999(H9N2) virus: 21 subjects (2.7%) at 12-months and 40 subjects (5.1%) at 24-months. Among these, two largely asymptomatic acute infections with H9N2 virus were detected by >4-fold increases in annual serologic titers (final titers 1∶80). While controlling for age and influenza vaccine receipt, moderate poultry exposure was significantly associated with elevated H9N2 titers (adjusted OR = 2.3; 95% CI, 1.04–5.2) at the 24-month encounter. One subject had an elevated titer (1∶20) against H5N1 during follow-up. Conclusions: From 2008–10, evidence for AIV infections was sparse among this rural population. Subclinical H9N2 AIV infections likely occurred, but serological results were confounded by antibody cross-reactions. There is a critical need for improved serological diagnostics to more accurately detect subclinical AIV infections in humans. [ABSTRACT FROM AUTHOR]

Background: Timely information about disease severity can be central to the detection and management of outbreaks of acute respiratory infections (ARI), including influenza. We asked if two resources: 1) free text, and 2) structured data from an electronic medical record (EMR) could complement each other to identify patients with pneumonia, an ARI severity landmark. Methods: A manual EMR review of 2747 outpatient ARI visits with associated chest imaging identified x-ray reports that could support the diagnosis of pneumonia (kappa score  = 0.88 (95% CI 0.82∶0.93)), along with attendant cases with Possible Pneumonia (adds either cough, sputum, fever/chills/night sweats, dyspnea or pleuritic chest pain) or with Pneumonia-in-Plan (adds pneumonia stated as a likely diagnosis by the provider). The x-ray reports served as a reference to develop a text classifier using machine-learning software that did not require custom coding. To identify pneumonia cases, the classifier was combined with EMR-based structured data and with text analyses aimed at ARI symptoms in clinical notes. Results: 370 reference cases with Possible Pneumonia and 250 with Pneumonia-in-Plan were identified. The x-ray report text classifier increased the positive predictive value of otherwise identical EMR-based case-detection algorithms by 20–70%, while retaining sensitivities of 58–75%. These performance gains were independent of the case definitions and of whether patients were admitted to the hospital or sent home. Text analyses seeking ARI symptoms in clinical notes did not add further value. Conclusion: Specialized software development is not required for automated text analyses to help identify pneumonia patients. These results begin to map an efficient, replicable strategy through which EMR data can be used to stratify ARI severity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: In April 2009, the most recent pandemic of influenza A began. We present the first estimates of pandemic mortality based on the newly-released final data on deaths in 2009 and 2010 in the United States. Methods: We obtained data on influenza and pneumonia deaths from the National Center for Health Statistics (NCHS). Age- and sex-specific death rates, and age-standardized death rates, were calculated. Using negative binomial Serfling-type methods, excess mortality was calculated separately by sex and age groups. Results: In many age groups, observed pneumonia and influenza cause-specific mortality rates in October and November 2009 broke month-specific records since 1959 when the current series of detailed US mortality data began. Compared to the typical pattern of seasonal flu deaths, the 2009 pandemic age-specific mortality, as well as influenza-attributable (excess) mortality, skewed much younger. We estimate 2,634 excess pneumonia and influenza deaths in 2009–10; the excess death rate in 2009 was 0.79 per 100,000. Conclusions: Pandemic influenza mortality skews younger than seasonal influenza. This can be explained by a protective effect due to antigenic cycling. When older cohorts have been previously exposed to a similar antigen, immune memory results in lower death rates at older ages. Age-targeted vaccination of younger people should be considered in future pandemics. [ABSTRACT FROM AUTHOR]

For in vivo studies of influenza dynamics where within-host measurements are fit with a mathematical model, infectivity assays (e.g. 50% tissue culture infectious dose; TCID50) are often used to estimate the infectious virion concentration over time. Less frequently, measurements of the total (infectious and non-infectious) viral particle concentration (obtained using real-time reverse transcription-polymerase chain reaction; rRT-PCR) have been used as an alternative to infectivity assays. We investigated the degree to which measuring both infectious (via TCID50) and total (via rRT-PCR) viral load allows within-host model parameters to be estimated with greater consistency and reduced uncertainty, compared with fitting to TCID50 data alone. We applied our models to viral load data from an experimental ferret infection study. Best-fit parameter estimates for the “dual-measurement” model are similar to those from the TCID50-only model, with greater consistency in best-fit estimates across different experiments, as well as reduced uncertainty in some parameter estimates. Our results also highlight how variation in TCID50 assay sensitivity and calibration may hinder model interpretation, as some parameter estimates systematically vary with known uncontrolled variations in the assay. Our techniques may aid in drawing stronger quantitative inferences from in vivo studies of influenza virus dynamics. [ABSTRACT FROM AUTHOR]

We construct a stochastic SIR model for influenza spreading on a D-dimensional lattice, which represents the dynamic contact network of individuals. An age distributed population is placed on the lattice and moves on it. The displacement from a site to a nearest neighbor empty site, allows individuals to change the number and identities of their contacts. The dynamics on the lattice is governed by an attractive interaction between individuals belonging to the same age-class. The parameters, which regulate the pattern dynamics, are fixed fitting the data on the age-dependent daily contact numbers, furnished by the Polymod survey. A simple SIR transmission model with a nearest neighbors interaction and some very basic adaptive mobility restrictions complete the model. The model is validated against the age-distributed Italian epidemiological data for the influenza A(H1N1) during the season, with sensible predictions for the epidemiological parameters. For an appropriate topology of the lattice, we find that, whenever the accordance between the contact patterns of the model and the Polymod data is satisfactory, there is a good agreement between the numerical and the experimental epidemiological data. This result shows how rich is the information encoded in the average contact patterns of individuals, with respect to the analysis of the epidemic spreading of an infectious disease. [ABSTRACT FROM AUTHOR]

Background: During the 2011/2012 winter influenza season in the Republic of Korea, influenza A (H3N2) was the predominant virus in the first peak period of influenza activity during the second half of January 2012. On the other hand, influenza B was the predominant virus in the second peak period of influenza activity during the second half of March 2012. The objectives of this study were to compare the clinical and epidemiological characteristics of patients with laboratory-confirmed influenza A or influenza B. Methodology/Principal Findings: We analyzed data from 2,129 adult patients with influenza-like illnesses who visited the emergency rooms of seven university hospitals in Korea from October 2011 to May 2012. Of 850 patients with laboratory-confirmed influenza, 656 (77.2%) had influenza A (H3N2), and 194 (22.8%) influenza B. Age, and the frequencies of cardiovascular disorders, diabetes, hypertension were significantly higher in patients with influenza A (H3N2) (P<0.05). The frequencies of leukopenia or thrombocytopenia in patients with influenza B at initial presentation were statistically higher than those in patients with influenza A (H3N2) (P<0.05). The rate of hospitalization, and length of hospital stay were statistically higher in patients with influenza A (H3N2) (P<0.05), and of the 79 hospitalized patients, the frequency of diabetes, hypertension, cases having at least one of the comorbid conditions, and the proportion of elderly were significantly higher in patients with influenza A (H3N2) (P<0.05). Conclusions: The proportion of males to females and elderly population were significantly higher for influenza A (H3N2) patients group compared with influenza B group. Hypertension, diabetes, chronic lung diseases, cardiovascular disorders, and neuromuscular diseases were independently associated with hospitalization due to influenza. Physicians should assess and treat the underlying comorbid conditions as well as influenza viral infections for the appropriate management of patients with influenza. [ABSTRACT FROM AUTHOR]

Background: Influenza is vaccine-preventable; however, the burden of severe influenza in India remains unknown. We conducted a population-based study to estimate the incidence of laboratory confirmed influenza-associated hospitalizations in a rural community in western India. Methods: We conducted active surveillance for hospitalized patients with acute medical illnesses or acute chronic disease exacerbations in Pune during pandemic and post pandemic periods (May 2009–April 2011). Nasal and throat swabs were tested for influenza viruses. A community health utilization survey estimated the proportion of residents hospitalized with respiratory illness at non-study facilities and was used to adjust incidence estimates from facility-based surveillance. Results: Among 9,426 hospitalizations, 3,391 (36%) patients were enrolled; 665 of 3,179 (20.9%) tested positive for influenza. Of 665 influenza positives, 340 (51%) were pandemic A(H1N1)pdm09 and 327 (49%) were seasonal, including A/H3 (16%), A/H1 (3%) and influenza B (30%). The proportion of patients with influenza peaked during August 2009 (39%) and 2010 (42%). The adjusted annual incidence of influenza hospitalizations was 46.8/10,000 during pandemic and 40.5/10,000 during post-pandemic period with comparable incidence of A(H1N1)pdm09 during both periods (18.8 and 20.3, respectively). The incidence of both pH1N1 and seasonal hospitalized influenza disease was highest in the 5–29 year olds. Conclusions: We document the previously unrecognized burden of influenza hospitalization in a rural community following the emergence of influenza A(H1N1)pdm09 viruses in India. During peak periods of influenza activity circulation i.e during the monsoon period, 20% of all hospital admissions in the community had influenza positivity. These findings can inform development of influenza prevention and control strategies in India. [ABSTRACT FROM AUTHOR]

Objective: To assess 2009 A(H1N1) seroconversion rates and their determinants within an unvaccinated population in Vientiane Capital, Laos. Methods: CoPanFlu Laos, a general population cohort of 807 households and 4,072 participants was established in March 2010. Sociodemographic data, epidemiological data, and capillary blood samples were collected from all the household members in March, and again in October 2010, in order to assess the level of antibodies to 2009 A(H1N1) with the haemagglutination inhibition assay. 2009 A(H1N1) seroconversion was defined as a fourfold or greater increase in titre between inclusion and follow-up. Determinants for pandemic influenza infection were studied using the generalized estimating equations model, taking household clustering into account. Results: Between March and November 2010, 3,524 paired sera were tested. Prior to the pandemic, our cohort was almost completely vaccine-naive for seasonal influenza. The overall seroconversion rate among nonvaccinated individuals (n = 2,810) was 14.3% (95%CI [13.0, 15.6]), with the highest rate for participants under 20 yo (19.8%, 95%CI [17.4, 22.4]) and the lowest rate for participants over 60 yo (6.5%, 95%CI [3.7, 10.4]). Participants with lower baseline titres had significantly higher infection rates, with a dose-effect relationship. Odds ratios (ORs) ranged from 76.5 (95%CI [27.1, 215.8]), for those with a titre at inclusion of 1∶10, to 8.1 (95%CI [3.3, 20.4]), for those with a titre of 1∶40. Having another household member with a titre ≥1∶80 was associated with a higher likelihood of immunity (OR = 3.3, 95%CI [2.8, 3.9]). Conclusion: The determinants and age distribution for seroconversion within a vaccine-naive population were similar to those found in developed countries. This pandemic was characterized by strong epidemiological determinants, regardless of geographical zone and level of development. Moreover, we detected pre-existing cross-reacting antibodies in participants over 60 yo, which could not have originated from former multiple vaccination as has been suggested elsewhere. [ABSTRACT FROM AUTHOR]

The CoPanFlu-France cohort of households was set up in 2009 to study the risk factors for infection by the pandemic influenza virus (H1N1pdm) in the French general population. The authors developed an integrative data-driven approach to identify individual, collective and environmental factors associated with the post-seasonal serological H1N1pdm geometric mean titer, and derived a nested case-control analysis to identify risk factors for infection during the first season. This analysis included 1377 subjects (601 households). The GMT for the general population was 47.1 (95% confidence interval (CI): 45.1, 49.2). According to a multivariable analysis, pandemic vaccination, seasonal vaccination in 2009, recent history of influenza-like illness, asthma, chronic obstructive pulmonary disease, social contacts at school and use of public transports by the local population were associated with a higher GMT, whereas history of smoking was associated with a lower GMT. Additionally, young age at inclusion and risk perception of exposure to the virus at work were identified as possible risk factors, whereas presence of an air humidifier in the living room was a possible protective factor. These findings will be interpreted in light of the longitudinal analyses of this ongoing cohort. [ABSTRACT FROM AUTHOR]

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over “swine flu,” as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication. [ABSTRACT FROM AUTHOR]

Background: Pigs play a key epidemiologic role in the ecology of influenza A viruses (IAVs) emerging from animal hosts and transmitted to humans. Between 2008 and 2010, we investigated the health risk of occupational exposure to swine influenza viruses (SIVs) in Italy, during the emergence and spread of the 2009 H1N1 pandemic (H1N1pdm) virus. Methodology/Principal Findings: Serum samples from 123 swine workers (SWs) and 379 control subjects (Cs), not exposed to pig herds, were tested by haemagglutination inhibition (HI) assay against selected SIVs belonging to H1N1 (swH1N1), H1N2 (swH1N2) and H3N2 (swH3N2) subtypes circulating in the study area. Potential cross-reactivity between swine and human IAVs was evaluated by testing sera against recent, pandemic and seasonal, human influenza viruses (H1N1 and H3N2 antigenic subtypes). Samples tested against swH1N1 and H1N1pdm viruses were categorized into sera collected before (n. 84 SWs; n. 234 Cs) and after (n. 39 SWs; n. 145 Cs) the pandemic peak. HI-antibody titers ≥10 were considered positive. In both pre-pandemic and post-pandemic peak subperiods, SWs showed significantly higher swH1N1 seroprevalences when compared with Cs (52.4% vs. 4.7% and 59% vs. 9.7%, respectively). Comparable HI results were obtained against H1N1pdm antigen (58.3% vs. 7.7% and 59% vs. 31.7%, respectively). No differences were found between HI seroreactivity detected in SWs and Cs against swH1N2 (33.3% vs. 40.4%) and swH3N2 (51.2 vs. 55.4%) viruses. These findings indicate the occurrence of swH1N1 transmission from pigs to Italian SWs. Conclusion/Significance: A significant increase of H1N1pdm seroprevalences occurred in the post-pandemic peak subperiod in the Cs (p<0.001) whereas SWs showed no differences between the two subperiods, suggesting a possible occurrence of cross-protective immunity related to previous swH1N1 infections. These data underline the importance of risk assessment and occupational health surveillance activities aimed at early detection and control of SIVs with pandemic potential in humans. [ABSTRACT FROM AUTHOR]

The role of the host immune response in determining the severity and duration of an influenza infection is still unclear. In order to identify severity factors and more accurately predict the course of an influenza infection within a human host, an understanding of the impact of host factors on the infection process is required. Despite the lack of sufficiently diverse experimental data describing the time course of the various immune response components, published mathematical models were constructed from limited human or animal data using various strategies and simplifying assumptions. To assess the validity of these models, we assemble previously published experimental data of the dynamics and role of cytotoxic T lymphocytes, antibodies, and interferon and determined qualitative key features of their effect that should be captured by mathematical models. We test these existing models by confronting them with experimental data and find that no single model agrees completely with the variety of influenza viral kinetics responses observed experimentally when various immune response components are suppressed. Our analysis highlights the strong and weak points of each mathematical model and highlights areas where additional experimental data could elucidate specific mechanisms, constrain model design, and complete our understanding of the immune response to influenza. [ABSTRACT FROM AUTHOR]

Background: Influenza A virus is one of world’s major uncontrolled pathogen, causing seasonal epidemic as well as global pandemic. This was evidenced by recent emergence and continued prevalent 2009 swine origin pandemic H1N1 Influenza A virus, provoking first true pandemic in the past 40 years. In the course of its evolution, the virus acquired many mutations and multiple unidentified molecular determinants are likely responsible for the ability of the 2009 H1N1 virus to cause increased disease severity in humans. Availability of limited data on complete genome hampers the continuous monitoring of this type of events. Outbreaks with considerable morbidity and mortality have been reported from all parts of the country. Methods/Results: Considering a large number of clinical cases of infection complete genome based sequence characterization of Indian H1N1pdm virus and their phylogenetic analysis with respect to circulating global viruses was undertaken, to reveal the phylodynamic pattern of H1N1pdm virus in India from 2009–2011. The Clade VII was observed as a major circulating clade in phylogenetic analysis. Selection pressure analysis revealed 18 positively selected sites in major surface proteins of H1N1pdm virus. Conclusions: This study clearly revealed that clade VII has been identified as recent circulating clade in India as well globally. Few clade VII specific well identified markers undergone positive selection during virus evolution. Continuous monitoring of the H1N1pdm virus is warranted to track of the virus evolution and further transmission. This study will serve as a baseline data for future surveillance and also for development of suitable therapeutics. [ABSTRACT FROM AUTHOR]

Background: We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Methods: Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004–2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. Results: A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Conclusions: Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases. [ABSTRACT FROM AUTHOR]

Background: Influenza A (H1N1)pdm09 (2009 H1N1) re-circulated as the predominant virus from January through February 2011 in China. National surveillance of 2009 H1N1 as a notifiable disease was maintained to monitor potential changes in disease severity from the previous season. Methodology/Principal Findings: To describe the characteristics of hospitalized cases with 2009 H1N1 infection and analyze risk factors for severe illness during the 2010–2011winter season in China, we obtained surveillance data from hospitalized cases with 2009 H1N1 infection from November 2010 through May 2011, and reviewed medical records from 701 hospitalized cases. Age-standardized risk ratios were used to compare the age distribution of patients that were hospitalized and died due to 2009 H1N1 between the 2010–2011winter season to those during the 2009–2010 pandemic period. During the 2010–2011 winter season, children less than 5 years of age had the highest relative risk of hospitalization and death, followed by adults aged 65 years or older. Additionally, the relative risk of hospitalized cases aged 5–14 and 15–24 years was lower compared to children less than 5 years of age. During the winter season of 2010–2011, the proportions of adults aged 25 years or older for hospitalization and death were significantly higher than those during the 2009–2010 pandemic period. Being male, having a chronic medical condition, delayed hospital admission (≥3 days from onset) or delayed initiation of antiviral treatment (≥5 days from onset) were associated with severe illness among non-pregnant patients ≥2 years of age. Conclusions/Significance: We observed a change in high risk groups for hospitalization for 2009 H1N1 during the winter months immediately following the pandemic period compared to the high risk groups identified during the pandemic period. Our nationally notifiable disease surveillance system enabled us to understand the evolving epidemiology of 2009 H1N1 infection after the pandemic period. [ABSTRACT FROM AUTHOR]

Background: There is limited information on influenza and respiratory syncytial virus (RSV) seasonal patterns in tropical areas, although there is renewed interest in understanding the seasonal drivers of respiratory viruses. Methods: We review geographic variations in seasonality of laboratory-confirmed influenza and RSV epidemics in 137 global locations based on literature review and electronic sources. We assessed peak timing and epidemic duration and explored their association with geography and study settings. We fitted time series model to weekly national data available from the WHO influenza surveillance system (FluNet) to further characterize seasonal parameters. Results: Influenza and RSV activity consistently peaked during winter months in temperate locales, while there was greater diversity in the tropics. Several temperate locations experienced semi-annual influenza activity with peaks occurring in winter and summer. Semi-annual activity was relatively common in tropical areas of Southeast Asia for both viruses. Biennial cycles of RSV activity were identified in Northern Europe. Both viruses exhibited weak latitudinal gradients in the timing of epidemics by hemisphere, with peak timing occurring later in the calendar year with increasing latitude (P<0.03). Time series model applied to influenza data from 85 countries confirmed the presence of latitudinal gradients in timing, duration, seasonal amplitude, and between-year variability of epidemics. Overall, 80% of tropical locations experienced distinct RSV seasons lasting 6 months or less, while the percentage was 50% for influenza. Conclusion: Our review combining literature and electronic data sources suggests that a large fraction of tropical locations experience focused seasons of respiratory virus activity in individual years. Information on seasonal patterns remains limited in large undersampled regions, included Africa and Central America. Future studies should attempt to link the observed latitudinal gradients in seasonality of viral epidemics with climatic and population factors, and explore regional differences in disease transmission dynamics and attack rates. [ABSTRACT FROM AUTHOR]