Abstract
BackgroundReal-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting.MethodsA discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions.ResultsThe quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds.ConclusionsReal-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance.
Highlights
Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied
The confidence intervals (CIs) overlapped with the CI in week 44, the 95% CI based on week 15 was broad, ranging from 0.88 to 1.40
The present study has proposed a method for real-time forecasting based on crudely reported weekly incidence data, accounting for demographic stochasticity and conditional measurement and employing a simple discrete time stochastic model
Summary
Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. Of the various diseases that have been studied, the transmission dynamics of influenza have attracted much scientific interest, and from the beginning of the 2009 pandemic, mathematical modeling has progressed our understanding of the epidemiological dynamics of influenza (H1N1-2009) [7]. Among the various applications of mathematical models to infectious disease epidemiology, future prediction is an area that has been understudied and methods for real-time and long-term prediction in large populations have yet to be sought [8,9,10]. The realtime estimation of model parameters has been proposed with, for example, the aim of assessing the effectiveness of certain control measures in real-time [17], as mentioned above, such studies tended to focus on parameter estimation and quantitative forecasting has been understudied. During the course of an epidemic, it may be important to forecast the future course of the epidemic in real-time
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