Abstract
For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.
Highlights
For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error
Our findings demonstrate that diagnosis of the error growth structure in a nonlinear epidemiological model provides valuable information, which can be used to reduce the inaccuracy of forecasts generated with that model
Rather than solely relying on state space estimation, the sensitive state variable S and parameter R0max are instead updated by counteracting the structural errors dynamically diagnosed through the breeding method
Summary
For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models. We have generated operational real-time influenza forecasts for 5 years[16], using a model-inference system combining a humidity-modulated susceptible-infected-recovered-susceptible (SIRS) model and statistical filtering techniques[4, 5, 17, 18] This system has produced reliable forecasts of influenza peak timing with leads of up to 9 weeks[5]. In the long-term evolution of dynamical systems in which extremely complex behaviors (e.g., chaos) may occur, it has been found that both the error growth caused by the Normalized error in S a1
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