Journal Article

Predictability in process-based ensemble forecast of influenza

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

2019

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Analysis / Cities and towns / Communicable diseases / Computer and Information Sciences / Computer Simulation / Data assimilation / Disease Outbreaks / Disease transmission / Distribution / Dynamical systems / Earth Sciences / Ecology and Environmental Sciences / Environmental health / Epidemics / Forecasting - methods / Forecasts and trends / Growth patterns / Health sciences / Humans / Humidity / Illnesses / Incidence / Infectious diseases / Influenza / Influenza, Human - epidemiology / Laboratories / Mathematical models / Medicine and Health Sciences / Methods / Model accuracy / Models, Statistical / Models, Theoretical / Nonlinear systems / Outbreaks / Pandemics / Parameter estimation / Physical Sciences / Population / Public health / Respiratory syncytial virus / Retrospective Studies / Seasons / Staphylococcus infections / Stochasticity / Variation / Vector analysis / Viruses / Weather forecasting / West Nile virus