Abstract
We present a framework for near real-time forecast of influenza epidemics using a simulation optimization approach. The method combines an individual-based model and a simple root finding optimization method for parameter estimation and forecasting. In this study, retrospective forecasts were generated for seasonal influenza epidemics using web-based estimates of influenza activity from Google Flu Trends for 2004-2005, 2007-2008 and 2012-2013 flu seasons. In some cases, the peak could be forecasted 5-6 weeks ahead. This study adds to existing resources for influenza forecasting and the proposed method can be used in conjunction with other approaches in an ensemble framework.
Highlights
In a paper published in 1986, Longini et al.[1] discussed the usefulness of developing approaches to infectious disease forecasting for minimizing the public health impacts of an epidemic
The 95% confidence intervals (CI) forecasts the epidemic peak between early to mid February, which agrees with the true peak week of 02/17/2008
Reliable forecasts of influenza events could influence the allocation of public health resources and control measures
Summary
In a paper published in 1986, Longini et al.[1] discussed the usefulness of developing approaches to infectious disease forecasting for minimizing the public health impacts of an epidemic. The computational model presented was developed by scientists in the Soviet Union for predicting the spatio-temporal spread of influenza between and within 126 cities and centers in the Soviet Union. The model was based on a system of integro-differential equations with partial derivatives, which were later translated to a set of difference equations for computer analysis. An extension of the model to a global scale was applied to forecasting the worldwide spread of the 1968-1969 Hong Kong influenza A (H3N2) pandemic. Longini et al.[1] concluded that the performance of the model was promising in the forecast of the temporal-geographic spread of influenza over the forecast period, which consisted of 425 days
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