figshare
Browse
rsif20180174_si_001.docx (544.11 kB)

Additional descriptions of methods, supporting tables and plots from Evaluation of mechanistic and statistical methods in forecasting influenza-like illness

Download (544.11 kB)
journal contribution
posted on 2018-07-10, 14:47 authored by Sasikiran Kandula, Teresa Yamana, Sen Pei, Wan Yang, Haruka Morita, Jeffrey Shaman
A variety of mechanistic and statistical methods to forecast seasonal influenza have been proposed and are in use; however, the effects of various data issues and design choices (statistical versus mechanistic methods, for example) on the accuracy of these approaches have not been thoroughly assessed. Here, we compare the accuracy of three forecasting approaches—a mechanistic method, a weighted average of two statistical methods and a super-ensemble of eight statistical and mechanistic models—in predicting seven outbreak characteristics of seasonal influenza during the 2016–2017 season at the national and 10 regional levels in the USA. For each of these approaches, we report the effects of real time under- and over-reporting in surveillance systems, use of non-surveillance proxies of influenza activity and manual override of model predictions on forecast quality. Our results suggest that a meta-ensemble of statistical and mechanistic methods has better overall accuracy than the individual methods. Supplementing surveillance data with proxy estimates generally improves the quality of forecasts and transient reporting errors degrade the performance of all three approaches considerably. The improvement in quality from ad hoc and post-forecast changes suggest that domain experts continue to possess information that is not being sufficiently captured by current forecasting approaches.

History

Usage metrics

    Journal of the Royal Society Interface

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC