Flu Forecast

Influenza, or the flu, is a significant public health burden in the U.S. that annually causes between 3,000 and 49,000 deaths. Predictions of influenza, if reliable, would provide public health officials valuable advanced warning that could aid efforts to reduce the burden of this disease. For instance, medical resources, including vaccines and antivirals, can be distributed to areas in need well in advance of peak influenza incidence. Recent applications of statistical filtering methods to epidemiological models have shown that accurate and reliable influenza forecast is possible; however, many filtering methods exist, and the performance of any filter may be application dependent. In this study, we use a single epidemiological modeling framework to test the performance of six state-of-the-art filters for modeling and forecasting influenza. Three of the filters are particle filters, commonly used in scientific, engineering, and economic disciplines; the other three filters are ensemble filters, frequently used in geophysical disciplines, such as numerical weather prediction. We use each of the six filters to retrospectively model and forecast seasonal influenza activity during 2003-2012 for 115 cities in the U.S. We compare the performance of the six filters and propose potential strategies for improving real-time influenza prediction.

Yang W, Karspeck A, Shaman J. 2014 Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics. PLoS Comput Biol 10: e1003583

Unlike the U.S., where flu season arrives regularly in winter, influenza epidemics in subtropical and tropical regions occur throughout the year. This irregularity creates challenges for the forecast system as applied to U.S. cities. In this work, we develop alternative forecast systems that are more adept at handling erratic non-seasonal epidemics, using either the ensemble adjustment Kalman filter or a particle filter with space reprobing, in conjunction with a susceptible-infected-recovered model. We present these forecast systems and apply them to Hong Kong.

The forecast systems are able to forecast both the peak timing and peak magnitude for 44 epidemics during 1998-2013, as caused by individual influenza strains, as well as 19 aggregate epidemics, as caused by all strains. Forecast accuracy is comparable to that achieved for U.S. cities. For peak timing (peak magnitude) forecast accuracy increases up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B, 1-3 weeks before the predicted peak. These findings indicate that these forecasts provide lead times adequate for planning intervention measures. In addition, the forecasts of peak magnitude can be used to inform the scale of response. For instance, the amount of antivirals and vaccines needed could be assessed based on the predicted peak magnitude. Altogether, our 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.

Yang W, Cowling BJ, Lau EHY, Shaman J. 2015. Forecasting influenza epidemics in Hong Kong. PLoS Computational Biology 11: e1004383.

Recently developed influenza forecast systems have the potential to aid public health planning for and mitigation of the burden of this disease. However, current forecasts are often generated at spatial scales (e.g. national level) that are coarser than the scales at which public health measures and interventions are implemented (e.g. community level). In this study, we build and test influenza forecast systems at county and community levels, which either include spatial connectivity among locations or are run in isolation. We test these four flu forecast systems (i.e. 2 models × 2 spatial scales) using data collected from 2008 to 2013, including the 2009 pandemic, for the five boroughs (corresponding to county level) and 42 neighborhoods (corresponding to community level) in New York City. We compare the performance of the four forecast systems in predicting the onset, duration, and intensity of flu outbreaks and found that the performance varied by spatial scale (borough vs. neighborhood), season (non-pandemic vs. pandemic) and metric (onset, duration, and intensity). In general, the inclusion of spatial network connectivity in the forecast model improves forecast accuracy at the borough scale but degrades accuracy at the neighborhood scale.

Yang W, Olson DR, Shaman J (2016) Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City. PLoS Comput Biol 12(11): e1005201. doi:10.1371/journal. pcbi.1005201.