Pei S, Cane MA, Shaman J. Predictability in process-based ensemble forecast of influenza. PLoS Comput Biol. 2019 Feb 28;15(2):e1006783
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.
See Also:
Latest articles in those days:
- Phylogeography and gene pool analysis of highly pathogenic avian influenza H5N1 viruses reported in India from 2006 to 2021 4 hours ago
- Analysis of a diffusive epidemic model with a zero-infection zone 5 hours ago
- Quick detection of H5N1 avian influenza virus by surface enhanced Raman scattering(SERS) using aptamer capture 5 hours ago
- The critical role of RAGE in severe influenza infection: A target for control of inflammatory response in the disease 6 hours ago
- Human infection caused by avian influenza A (H10N5) virus 6 hours ago
[Go Top] [Close Window]