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Evaluating the impact of different social networks on the spread of respiratory diseases has been limited by a lack of detailed data on transmission outside the household setting as well as appropriate statistical methods. Here, from data collected during a H1N1 pandemic (pdm) influenza outbreak that started in an elementary school and spread in a semirural community in Pennsylvania, we quantify how transmission of influenza is affected by social networks. We set up a transmission model for which parameters are estimated from the data via Markov chain Monte Carlo sampling. Sitting next to a case or being the playmate of a case did not significantly increase the risk of infection; but the structuring of the school into classes and grades strongly affected spread. There was evidence that boys were more likely to transmit influenza to other boys than to girls (and vice versa), which mimicked the observed assortative mixing among playmates. We also investigated the presence of abnormally high transmission occurring on specific days of the outbreak. Late closure of the school (i.e., when 27% of students already had symptoms) had no significant impact on spread. School-aged individuals (6-18 y) facilitated the introduction and spread of influenza in households, but only about one in five cases aged >18 y was infected by a school-aged household member. This analysis shows the extent to which clearly defined social networks affect influenza transmission, revealing strong between-place interactions with back-and-forth waves of transmission between the school, the community, and the household.here is a large body of theoretical literature on how social networks and population structures may affect the spread of communicable diseases and hence influence the design of optimal control strategies (1-8). Such work often makes use of detailed data on populations (e.g., demographics in households, schools, and workplaces; mobility and land-use data; contact surveys; or time-use data) but then makes assumptions about how transmission rates change with the type of interaction (e.g., as a function of the setting and the spatial or social distance between individuals, etc.).

The formulation of accurate clinical case definitions is an integral part of an effective process of public health surveillance. Although such definitions should, ideally, be based on a standardized and fixed collection of defining criteria, they often require revision to reflect new knowledge of the condition involved and improvements in diagnostic testing. Optimal case definitions also need to have a balance of sensitivity and specificity that reflects their intended use. After the 2009–2010 H1N1 influenza pandemic, the World Health Organization (WHO) initiated a technical consultation on global influenza surveillance. This prompted improvements in the sensitivity and specificity of the case definition for influenza – i.e. a respiratory disease that lacks uniquely defining symptomology. The revision process not only modified the definition of influenza-like illness, to include a simplified list of the criteria shown to be most predictive of influenza infection, but also clarified the language used for the definition, to enhance interpretability. To capture severe cases of influenza that required hospitalization, a new case definition was also developed for severe acute respiratory infection in all age groups. The new definitions have been found to capture more cases without compromising specificity. Despite the challenge still posed in the clinical separation of influenza from other respiratory infections, the global use of the new WHO case definitions should help determine global trends in the characteristics and transmission of influenza viruses and the associated disease burden.

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BackgroundInfluenza‐associated illness results in increased morbidity and mortality in the Americas. These effects can be mitigated with an appropriately chosen and timed influenza vaccination campaign. To provide guidance in choosing the most suitable vaccine formulation and timing of administration, it is necessary to understand the timing of influenza seasonal epidemics.ObjectivesOur main objective was to determine whether influenza occurs in seasonal patterns in the American tropics and when these patterns occurred.MethodsPublicly available, monthly seasonal influenza data from the Pan American Health Organization and WHO, from countries in the American tropics, were obtained during 2002–2008 and 2011–2014 (excluding unseasonal pandemic activity during 2009–2010). For each country, we calculated the monthly proportion of samples that tested positive for influenza. We applied the monthly proportion data to a logistic regression model for each country.ResultsWe analyzed 2002–2008 and 2011–2014 influenza surveillance data from the American tropics and identified 13 (81%) of 16 countries with influenza epidemics that, on average, started during May and lasted 4 months.ConclusionsThe majority of countries in the American tropics have seasonal epidemics that start in May. Officials in these countries should consider the impact of vaccinating persons during April with the Southern Hemisphere formulation.

To determine the effects of school closure, we surveyed 214 households after a 1-week elementary school closure because of pandemic (H1N1) 2009. Students spent 77% of the closure days at home, 69% of students visited at least 1 other location, and 79% of households reported that adults missed no days of work to watch children.

BackgroundInfluenza disease is a vaccine-preventable cause of morbidity and mortality. The Pan American Health Organization (PAHO) region has invested in influenza vaccines, but few estimates of influenza burden exist to justify these investments. We estimated influenza-associated deaths for 35 PAHO countries during 2002–2008.MethodsAnnually, PAHO countries report registered deaths. We used respiratory and circulatory (R&C) codes from seven countries with distinct influenza seasonality and high-quality mortality data to estimate influenza-associated mortality rates by age group (0–64, 65–74, and ≥75 years) with a Serfling regression model or a negative binomial model. We calculated the percent of all R&C deaths attributable to influenza by age group in these countries (etiologic fraction) and applied it to the age-specific mortality in 13 countries with good mortality data but poorly defined seasonality. Lastly, we grouped the remaining 15 countries into WHO mortality strata and applied the age and mortality stratum-specific rate of influenza mortality calculated from the 20 countries. We summed each country’s estimate to arrive at an average total annual number and rate of influenza deaths in the Americas.ResultsFor the 35 PAHO countries, we estimated an annual mean influenza-associated mortality rate of 2·1/100 000 among <65-year olds, 31·9/100 000 among those 65–74 years, and 161·8/100 000 among those ≥75 years. We estimated that annually between 40 880 and 160 270 persons (mean, 85 100) die of influenza illness in the PAHO region.ConclusionInfluenza remains an important cause of mortality in the Americas.

ObjectivesOur objective was to estimate the incidence of influenza‐associated hospitalizations and in‐hospital deaths in Central American Region.Design and settingWe used hospital discharge records, influenza surveillance virology data, and population projections collected from Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua to estimate influenza‐associated hospitalizations and in‐hospital deaths. We performed a meta‐analysis of influenza‐associated hospitalizations and in‐hospital deaths.Main outcome measuresThe highest annual incidence was observed among children aged <5 years (136 influenza‐associated hospitalizations per 100 000 persons).ResultsAnnually, 7 625–11 289 influenza‐associated hospitalizations and 352–594 deaths occurred in the subregion.ConclusionsOur results suggest that a substantive number of persons are annually hospitalized because of influenza. Health officials should estimate how many illnesses could be averted through increased influenza vaccination.