Abstract
Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area.
Highlights
Influenza continues to be one of the most important human infectious diseases; responsible for thousands of deaths in the United States each year
This study extends the application of the individual-based epidemiology model to forecasting of the epidemic infection curve
Study Objective In this study, we focus on the event that the epidemic cannot be classified to any of the cases in the library (Figure 1)
Summary
Influenza continues to be one of the most important human infectious diseases; responsible for thousands of deaths in the United States each year. Individual-based epidemiology models are useful in evaluating the possible effectiveness and economic impact of different response strategies [2,3,4,5,6]. We seek to forecast the time at which the epidemic peaks, the number of infected individuals at the peak and the cumulative infected counts. These measures provide a summary of the epidemic curve and are important to public health officials. An accurate forecast of these measures at a regional level would enable local public health officials to evaluate intervention strategies and make educated decisions during an influenza epidemic [8,9,10]
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