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Predictability in process-based ensemble forecast of influenza

Pei, Sen; Cane, Mark A.; Shaman, Jeffrey L.

Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks.

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Title
PLoS Computational Biology
DOI
https://doi.org/10.1371/journal.pcbi.1006783

More About This Work

Academic Units
Environmental Health Sciences
Lamont-Doherty Earth Observatory
Published Here
March 5, 2019
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