Abstract
BackgroundWe 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.MethodsForecast 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.ResultsA 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.ConclusionsInteger-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.
Highlights
Influenza is a substantial cause of morbidity and mortality with up to five million cases of severe illness and 500,000 deaths worldwide each year [1]
Increased patient volume caused by seasonal influenza is a contributor to emergency department (ED) crowding, which has been linked to delays in critical treatments and increased mortality [7,8,9,10]
Google Flu Trends (GFT) utilizes internet search queries to detect the presence of influenza like illness (ILI) on a national, regional, state and city level 7–10 days prior to the U.S Influenza Sentinel Provider Surveillance Network and was recently validated to show a strong correlation with ED influenza cases at a local level [12,13,14]
Summary
Influenza is a substantial cause of morbidity and mortality with up to five million cases of severe illness and 500,000 deaths worldwide each year [1]. An influenza pandemic presents a well recognized and serious threat to the United States healthcare infrastructure [3,6] Effective management of both seasonal and pandemic influenza requires early detection of the outbreak through timely and accurate surveillance linked with a rapid response to mitigate crowding. Numerous potential surveillance systems exist to identify influenza outbreaks Traditional surveillance such as The Centers for Disease Control and Prevention’s (CDC) US Influenza Sentinel Provider Surveillance Network relies on the collection of numerous indicators including clinical symptoms, virology laboratory results, hospital admissions and mortality statistics resulting in a several week lag in data reporting [11]. New digital surveillance sources, such as Google Flu Trends (GFT), offer the potential to identify influenza surges in real-time, optimizing timely outbreak detection and response. We evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy
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