Forecasting Influenza Epidemics in Hong Kong

Wan Yang, Benjamin J Cowling, Eric H Y Lau and Jeffrey Shaman

PLOS Computational Biology, 2015, vol. 11, issue 7, 1-17

Abstract: Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.Author Summary: Influenza causes high levels of morbidity, mortality, and economic burden. Accurate forecasts of epidemic timing and magnitude would provide public health sectors valuable advance information in support of the planning and deployment of intervention measures. Such forecast systems have been developed for temperate regions with seasonal winter epidemics (e.g., U.S. cities). In subtropical and tropical regions, however, influenza epidemics can occur throughout the year with varying epidemic intensity; this irregularity makes the generation of accurate forecasts more challenging. For this study we develop alternative forecast systems that are more adept at handling erratic non-seasonal epidemics, using state-of-the-art Bayesian inference methods in conjunction with an epidemiological model. Here we present these forecast systems and apply them to Hong Kong. During 1998–2013, Hong Kong saw 44 influenza epidemics caused by either the A(H1N1), A(H3N2), or B strain, and 19 aggregate epidemics caused by one or more of these influenza strains. The forecast systems are able to forecast both the peak timing and peak magnitude of these epidemics, including the 2009 pandemic. The results suggest that routine forecast of influenza epidemics in other subtropical and tropical regions is possible, as well as forecast of other infectious diseases sharing similar irregular transmission dynamics.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004383

DOI: 10.1371/journal.pcbi.1004383

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