Influenza-like Illness Surveillance Data Research Articles

Prediction of Influenza-Like Illness Incidence in Cheras, Malaysia based on Environmental Data using the Generalised Additive Model

Influenza-like illness (ILI) accounts for 500,000 fatalities worldwide yearly. Environmental factors contribute to spread of respiratory infections. This study describes the epidemiological trends of ILI in Cheras, Kuala Lumpur, Malaysia and seeks scientific evidence on ILI activity and environmental factors. Cheras Health District collected ILI surveillance data and the nearest weather station which provided environmental data on mean temperature, humidity, wind speed, cumulative rainfall, particulate matter (PM)10 and PM2.5 levels. A total of 51,245 ILI cases were reported from 1st December 2021 to 30th April 2023. The non-linear relationship between ILI incidence and environmental variables was modelled using the Generalised Additive Model (GAM). The lag time for mean temperature, mean humidity, cumulative rainfall, mean wind speed, PM 10 level, and PM 2.5 level was 2, 3, 3, 3, 0, and 3 days, respectively. The model with the optimal lag better describes ILI case variance (R2=0.5, explained deviance=58%) than the model without lag selection (R2=0.5, explained deviance=57.2%). The Lag Model indicates a significant p-value for PM10 but no significant concurvity between predictor variables. Thus, the final model (R2=0.5, explained deviance=59.2%) has k=15. Higher rainfall, relative humidity, colder temperature, and decreased wind speed increased ILI incidence. PM2.5 and PM10 also contributes to ILI.

Long Short-term Memory-Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation.

The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. The model's input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models' LSTM layers for the latter to feed a dense layer. The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics.

Leveraging Google Search Data and Artificial Intelligence Methods for Provincial-level Influenza Forecasting: A South African Case Study

This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delays in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for a finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions.

Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States.

Detection of SARS-CoV-2 infections to date has relied heavily on RT-PCR testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or sub-clinical infections have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with COVID-19 case counts across states. If 1/3 of patients infected with SARS-CoV-2 in the US sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the US during the three-week period from March 8 to March 28, 2020. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to have been doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID-19 epidemic in the US characterized by rapid spread across the US with over 80% infected patients remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases.

Unmasking the Actual COVID-19 Case Count.

This report presents a novel approach to estimate the total number of COVID-19 cases in the United States, including undocumented infections, by combining the Centers for Disease Control and Prevention’s influenza-like illness surveillance data with aggregated prescription data. We estimated that the cumulative number of COVID-19 cases in the United States by 4 April 2020 was > 2.5 million.

TDEFSI

Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal forecasts for ILI is crucial for local preparedness and optimal interventions. We present T heory-guided D eep Learning-based E pidemic F orecasting with S ynthetic I nformation (TDEFSI), 1 an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks. TDEFSI yields accurate high-resolution spatiotemporal forecasts using low-resolution time-series data. During the training phase, TDEFSI uses high-resolution simulations of epidemics that explicitly model spatial and social heterogeneity inherent in urban regions as one component of training data. We train a two-branch recurrent neural network model to take both within-season and between-season low-resolution observations as features and output high-resolution detailed forecasts. The resulting forecasts are not just driven by observed data but also capture the intricate social, demographic, and geographic attributes of specific urban regions and mathematical theories of disease propagation over networks. We focus on forecasting the incidence of ILI and evaluate TDEFSI’s performance using synthetic and real-world testing datasets at the state and county levels in the USA. The results show that, at the state level, our method achieves comparable/better performance than several state-of-the-art methods. At the county level, TDEFSI outperforms the other methods. The proposed method can be applied to other infectious diseases as well.

Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model.

Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious disease proportions. Using U.S. influenza-like illness surveillance data over 18 seasons, we assessed probabilistic forecasts of this new beta autoregressive model with proper scoring rules. Other readily available forecasting tools were used for comparison, including Prophet, (S)ARIMA and kernel conditional density estimation (KCDE). Short-term flu activity was equally well predicted up to four weeks ahead by the beta model with four autoregressive lags and by KCDE; however, the beta model runs much faster. Non-dynamic Prophet scored worst. Relative performance differed for seasonal peak prediction. Prophet produced the best peak intensity forecasts in seasons with standard epidemic curves; otherwise, KCDE outperformed all other methods. Peak timing was best predicted by SARIMA, KCDE or the beta model, depending on the season. The best overall performance when predicting peak timing and intensity was achieved by KCDE. Only KCDE and naive historical forecasts consistently outperformed the equal-bin reference approach for all test seasons. We conclude that the endemic-epidemic beta model is a performant and easy-to-implement tool to forecast flu activity a few weeks ahead. Real-time forecasting of the seasonal peak, however, should consider outputs of multiple models simultaneously, weighing their usefulness as the season progresses.

Assessing the utility of a smart thermometer and mobile application as a surveillance tool for influenza and influenza-like illness.

Kinsa Inc. sells Food and Drug Administration-cleared smart thermometers, which synchronize with a mobile application, and may aid influenza forecasting efforts. We compare smart thermometer and mobile application data to regional influenza and influenza-like illness surveillance data from the California Department of Public Health. We evaluated the correlation between the regional California surveillance data and smart thermometer data, tested the hypothesis that smart thermometer readings and symptom reports provide regionally specific predictions, and determined whether smart thermometer and mobile application improved disease forecasts. Smart thermometer readings are highly correlated with regional surveillance data, are more predictive of surveillance data for their own region and season than for other times and places, and improve predictions of influenza, but not predictions of influenza-like illness. These results are consistent with the hypothesis that smart thermometer readings and symptom reports reflect underlying disease transmission in California. Data from such cloud-based devices could supplement syndromic influenza surveillance data.

The influence of statutory holidays on the proportion of the outpatient and emergency visits for influenza-like illness

Objective: To analyze the reasons for the fluctuations in the percentage of outpatient or emergency visits for influenza-like illness (ILI) during the Spring Festival and National Day in 2014-2018 surveillance season. Methods: ILI surveillance data was collected during the period of Spring Festival and National Day in mainland China, and downloaded from Chinese Influenza Surveillance Information System, during the 2014-2018 surveillance season. Results: There was no significant difference noticed in the number of ILI reports in the festival week with weeks before or after in both the southern and northern provinces. The number of outpatient visits was much less than that of the week before and after, but the number of emergency visits was statistically significantly increased. Conclusion: In the holiday peak of ILI%, the major causes was the impact of holiday-off at sentinel hospitals, resulting in a large reduction in the number of outpatient visits in the consulting room during the festivals.

Linking temperature and humidity to influenza-like illness: A long-term time series study in the Netherlands, 1970–2016

Introduction Climatic parameters affect the survival and transmissibility of respiratory viruses causing influenza-like illness (ILI), like influenza, respiratory syncytial virus and rhinovirus. In temperate countries, ILI activity is inversely associated with humidity and temperature (which affect both the onset and size of ILI epidemics), and we hypothesized that changes in the average values of these climatic parameters (which have occurred in Europe in recent decades) may explain the declining trends in ILI incidence rates observed during the same period. Here, we tested this hypothesis by linking ILI and climatic time series in the Netherlands for the period 1970–2016. Methods Influenza-like illness surveillance data for the period 1970–2016 were obtained from the Netherlands Institute for Health Services Research (NIVEL), which relies on a network of 60 sentinel general practitioners in 40 practices that are distributed throughout the Netherlands (including urban and rural areas) and cover approximately 0.7% of the Dutch population. Data on daily average absolute humidity (g/m3) and temperature for the weather station of De Bilt (which is located in the middle of the country) for the period 1970–2016 were obtained from the Royal Netherlands Meteorological Institute. Linear regression models were used to assess whether the season average ILI incidence rate changed over time, and whether there was any change over time in daily average absolute humidity and temperature. We modelled the association between climate and ILI activity through negative binomial regression models with the weekly ILI count as dependent variable, absolute humidity (or temperature) with up to three lagged weeks as independent variables, and covariates accounting for contagiousness of ILI-causing agents and presence of immunity. Model-based predictions were made using a leave-one-out cross-validation procedure. The study was funded by Sanofi-Pasteur. Results The season average ILI incidence rate declined significantly (P Conclusions Changes in absolute humidity and temperature were able to satisfactorily explain the long-term trends of ILI incidence rate in the Netherlands during 1970–2016. Temperate countries should monitor climate-related change in infectious diseases epidemiology so that measures to mitigate their impact can be planned and implemented.

Validation of Syndromic ILI Data for Use in CDC’s ILINet Surveillance, Pennsylvania

Objective: Discuss use of syndromic surveillance as a source for the state’s ILI/Influenza surveillanceDiscuss reliability of syndromic data and methods to address problems caused by data outliers and inconsistencies.Introduction: ILINet is a CDC program that has been used for years for influenza-like illness (ILI) surveillance, using a network of outpatient providers who volunteer to track and report weekly the number of visits due to ILI and the total number of visits to their practice. Pennsylvania has a network of 95 providers and urgent care clinics that submit data to ILINet. However, ongoing challenges in recruiting and retaining providers, and inconsistent weekly reporting are barriers to receiving accurate, representative, and timely ILI surveillance data year-round. Syndromic surveillance data have been used to enhance outpatient ILI surveillance in a number of jurisdictions, including Pennsylvania. At present, 156 hospitals, or 90% of all Pennsylvania hospitals with emergency departments (EDs), send chief complaint and other information on their ED visits to the Department of Health’s (PADOH) syndromic surveillance system. PADOH evaluated the consistency and reliability of ILI syndromic data as compared to ILINet data, to confirm that syndromic data were suitable for use in ILINet.Methods: Pennsylvania ILINet data from the past 6 influenza seasons (2011-2012 to 2016-2017, or 314 weeks of data) were downloaded from the CDC’s ILINet website. The statewide weekly percent of visits due to ILI in ILINet was used as the standard for comparisons. For syndromic surveillance, PADOH uses the Epicenter platform hosted by Health Monitoring Systems (HMS); visit-level data are also stored in SAS datasets at PADOH, and HMS forwards a subset of data to the National Syndromic Surveillance System Program. Using syndromic data from the same time period, the proportion of weeks with no syndromic data available was calculated for each facility. A state-developed ILI algorithm (very similar to the 2016 algorithm developed by the ISDS Syndrome Definitions Workgroup) was applied to ED visit chief complaint data to identify visits likely to be due to ILI. The algorithm flags the ER visit as ILI if chief complaint has any combinations of words for flu or fever plus either cough and sore throat or fever and both cough or sore throat . The percent of ED visits due to ILI per the syndromic algorithm (ILIsyn) was calculated for each week by hospital and state-wide. Facility ILIsyn trends were compared to the State level percent ILI data from ILINet by visually examining plots and by calculating Pearson correlation coefficients. Facilities that had >=15 weeks where ILIsyn differed from percent ILI in ILINet by more than 5% were considered to be poorly correlated.Results: A total of 156 hospitals were evaluated in the study. Twenty of the hospitals were excluded because they did not have syndromic data for at least 50% of the weeks in the study period, and an additional 20 were excluded because they had not agreed to have data forwarded to CDC. Of the remaining 116 facilities, individual facility correlation coefficients between ILIsyn and ILINet trends ranged from 0.03 to 0.82 (examples are in Figure 1). Twenty-four hospitals (20.7%) were determined to be poorly correlated. When data from the remaining 92 hospitals were combined, the state ILINet and state-wide ILIsyn trends were strongly correlated statistically and graphically (r=0.82, p <0.0001, Figure 2). Syndromic data from these 92 facilities were deemed acceptable for inclusion in ILINet. Conclusions: Syndromic surveillance data are a valuable source for ILI surveillance. However, evaluation at the hospital-specific level revealed that useful information is not obtained from all facilities. This project demonstrated that validation of data at the facility level is crucial to obtaining reliable and meaningful information. More work is needed to understand which factors distinguish well-correlated from poorly-correlated facilities, and how to improve the quality of information obtained from poorly-correlated facilities.

Effects of school breaks on influenza-like illness incidence in a temperate Chinese region: an ecological study from 2008 to 2015

ObjectiveTo assess the effects of winter/summer school breaks on occurrences of influenza-like illness (ILI).MethodsWe jointly analysed ILI surveillance data with the timing of school breaks in a temperate district in...

Seasonality of Influenza and Respiratory Syncytial Viruses and the Effect of Climate Factors in Subtropical-Tropical Asia Using Influenza-Like Illness Surveillance Data, 2010 -2012.

IntroductionThe seasonality of influenza and respiratory syncytial virus (RSV) is well known, and many analyses have been conducted in temperate countries; however, this is still not well understood in tropical countries. Previous studies suggest that climate factors are involved in the seasonality of these viruses. However, the extent of the effect of each climate variable is yet to be defined.Materials and MethodsWe investigated the pattern of seasonality and the effect of climate variables on influenza and RSV at three sites of different latitudes: the Eastern Visayas region and Baguio City in the Philippines, and Okinawa Prefecture in Japan. Wavelet analysis and the dynamic linear regression model were applied. Climate variables used in the analysis included mean temperature, relative and specific humidity, precipitation, and number of rainy days. The Akaike Information Criterion estimated in each model was used to test the improvement of fit in comparison with the baseline model.ResultsAt all three study sites, annual seasonal peaks were observed in influenza A and RSV; peaks were unclear for influenza B. Ranges of climate variables at the two Philippine sites were narrower and mean variables were significantly different among the three sites. Whereas all climate variables except the number of rainy days improved model fit to the local trend model, their contributions were modest. Mean temperature and specific humidity were positively associated with influenza and RSV at the Philippine sites and negatively associated with influenza A in Okinawa. Precipitation also improved model fit for influenza and RSV at both Philippine sites, except for the influenza A model in the Eastern Visayas.ConclusionsAnnual seasonal peaks were observed for influenza A and RSV but were less clear for influenza B at all three study sites. Including additional data from subsequent more years would help to ascertain these findings. Annual amplitude and variation in climate variables are more important than their absolute values for determining their effect on the seasonality of influenza and RSV.

Are School Absences Correlated with Influenza Surveillance Data in England? Results from Decipher My Data—A Research Project Conducted through Scientific Engagement with Schools

BackgroundSchool aged children are a key link in the transmission of influenza. Most cases have little or no interaction with health services and are therefore missed by the majority of existing surveillance systems. As part of a public engagement with science project, this study aimed to establish a web-based system for the collection of routine school absence data and determine if school absence prevalence was correlated with established surveillance measures for circulating influenza.MethodsWe collected data for two influenza seasons (2011/12 and 2012/13). The primary outcome was daily school absence prevalence (weighted to make it nationally representative) for children aged 11 to 16. School absence prevalence was triangulated graphically and through univariable linear regression to Royal College of General Practitioners (RCGP) influenza like illness (ILI) episode incidence rate, national microbiological surveillance data on the proportion of samples positive for influenza (A+B) and with Rhinovirus, RSV and laboratory confirmed cases of Norovirus.Results27 schools submitted data over two respiratory seasons. During the first season, levels of influenza measured by school absence prevalence and established surveillance were low. In the 2012/13 season, a peak of school absence prevalence occurred in week 51, and week 1 in RCGP ILI surveillance data. Linear regression showed a strong association between the school absence prevalence and RCGP ILI (All ages, and 5–14 year olds), laboratory confirmed cases of influenza A & B, and weak evidence for a linear association with Rhinovirus and Norovirus.InterpretationThis study provides initial evidence for using routine school illness absence prevalence as a novel tool for influenza surveillance. The network of web-based data collection platforms we established through active engagement provides an innovative model of conducting scientific research and could be used for a wide range of infectious disease studies in the future.

Age-related differences in the accuracy of web query-based predictions of influenza-like illness.

BackgroundWeb queries are now widely used for modeling, nowcasting and forecasting influenza-like illness (ILI). However, given that ILI attack rates vary significantly across ages, in terms of both magnitude and timing, little is known about whether the association between ILI morbidity and ILI-related queries is comparable across different age-groups. The present study aimed to investigate features of the association between ILI morbidity and ILI-related query volume from the perspective of age.MethodsSince Google Flu Trends is unavailable in Italy, Google Trends was used to identify entry terms that correlated highly with official ILI surveillance data. All-age and age-class-specific modeling was performed by means of linear models with generalized least-square estimation. Hold-out validation was used to quantify prediction accuracy. For purposes of comparison, predictions generated by exponential smoothing were computed.ResultsFive search terms showed high correlation coefficients of > .6. In comparison with exponential smoothing, the all-age query-based model correctly predicted the peak time and yielded a higher correlation coefficient with observed ILI morbidity (.978 vs. .929). However, query-based prediction of ILI morbidity was associated with a greater error. Age-class-specific query-based models varied significantly in terms of prediction accuracy. In the 0–4 and 25–44-year age-groups, these did well and outperformed exponential smoothing predictions; in the 15–24 and ≥ 65-year age-classes, however, the query-based models were inaccurate and highly overestimated peak height. In all but one age-class, peak timing predicted by the query-based models coincided with observed timing.ConclusionsThe accuracy of web query-based models in predicting ILI morbidity rates could differ among ages. Greater age-specific detail may be useful in flu query-based studies in order to account for age-specific features of the epidemiology of ILI.

Rates of influenza-like illness and winter school breaks, Chile, 2004-2010.

To determine effects of school breaks on influenza virus transmission in the Southern Hemisphere, we analyzed 2004–2010 influenza-like–illness surveillance data from Chile. Winter breaks were significantly associated with a two-thirds temporary incidence reduction among schoolchildren, which supports use of school closure to temporarily reduce illness, especially among schoolchildren, in the Southern Hemisphere.

Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China

BackgroundInfluenza 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.MethodsDynamic 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.ResultsBoth 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.ConclusionsForecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen.

A Practical Method for Surveillance of Novel H1N1 Influenza Using Automated Hospital Data

We report a surveillance method for influenza that is based on automated hospital laboratory and pharmacy data. During the 2009 H1N1 influenza pandemic, this method was objective, easy to perform, and utilized readily available automated hospital data. This surveillance method produced results that correlated strongly with influenza-like illness surveillance data.

Two waves of pandemic influenza A(H1N1)2009 in Wales – the possible impact of media coverage on consultation rates, April – December 2009

In the United Kingdom, the influenza A(H1N1) 2009 pandemic had a distinct two-wave pattern of general practice consultations for influenza-like illness (ILI). We describe the epidemiology of the influenza pandemic in Wales between April and December 2009 using integrated data from a number of independent sources: GP surveillance, community virology surveillance, hospital admissions and deaths, and media enquiries monitoring. The first wave peaked in late July at 100 consultations per 100,000 general practice population and attracted intensive media coverage. The positivity rate for the A(H1N1)2009 influenza did not exceed 25% and only 44 hospitalisations and one death were recorded. By contrast, the second wave peaked in late October and although characterised by lower ILI consultation rates (65 consultations per 100,000 general practice population) and low profile media activity, was associated with much higher positivity rates for pandemic influenza A(H1N1)2009 (60%) and substantially more hospital admissions (n=379) and deaths (n=26). The large number of ILI-related consultations during the first wave in Wales probably reflected the intensive media activity rather than influenza virus circulating in the community. Data from community surveillance schemes may therefore have considerably overestimated the true incidence of influenza. This has implications for the future interpretation of ILI surveillance data and their use in policy making, and underlines the importance of using integrated epidemiological, virological and hospital surveillance data to monitor influenza activity.

Influenza-Like Illness in a Community Surrounding a School-Based Outbreak of 2009 Pandemic Influenza A (H1N1) Virus-Chicago, Illinois, 2009

In April 2009, following the first school closure due to 2009 pandemic influenza A (H1N1) (pH1N1) in Chicago, Illinois, area hospitals were inundated with patients presenting with influenza-like illness (ILI). The extent of disease spread into the surrounding community was unclear. We performed a household survey to estimate the ILI attack rate among community residents and compared reported ILI with confirmed pH1N1 cases and ILI surveillance data (ie, hospital ILI visits, influenza testing, and school absenteeism). The estimated ILI attack rate was 4.6% (95% confidence interval, 2.8%-7.4%), with cases distributed throughout the 5-week study period. In contrast, 36 (84%) of 43 confirmed pH1N1 cases were identified the week of the school closure. Trends in surveillance data peaked during the same week and rapidly decreased to near baseline. Public awareness and health care practices impact standard ILI surveillance data. Community-based surveys are a valuable tool to help assess the burden of ILI in a community.