bioRxiv Science⌕ Search

Biology subjects

Ben-Nun, M.

Publications and source records attributed to Ben-Nun, M..

2 recordsLinked to original sources
bioRxiv2018

National and Regional Influenza-Like-Illness Forecasts for the USA

Health planners use forecasts of key metrics associated with influenza-like-illness (ILI); near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. We implemented a metapopulation model framework with 32 model variants. Variants differed from each other in their assumptions about: the force-of-infection (FOI); use of uninformative priors; the use of discounted historical data for not-yet-observed time points; and the treatment of regions as either independent or coupled. Individual model variants were chosen subjectively as the basis for our weekly forecasts; however, a subset of coupled models were only available part way through the season. Most frequently, during the 2016-17 season, we chose; FOI variants with both school vacations and humidity terms; uninformative priors; the inclusion of discounted historical data for not-yet-observed time points; and coupled regions (when available). Our near-term weekly forecasts substantially over-estimated incidence early in the season when coupled models were not available. However, our forecast accuracy improved in absolute terms and relative to other teams once coupled solutions were available. In retrospective analysis, we found that the 2016-17 season was not typical: on average, coupled models performed better when fit without historically augmented data. Also, we tested a simple ensemble model for the 2016-17 season and found that it underperformed our subjective choice for all forecast targets. In this study, we were able to improve accuracy during a prospective forecasting exercise by coupling dynamics between regions. Although reduction of forecast subjectivity should be a long-term goal, some degree of human intervention is likely to improve forecast accuracy in the medium-term in parallel with the systematic consideration of more sophisticated ensemble approaches.\n\nAuthor summaryIt is estimated that there are between 3 and 5 million worldwide annual seasonal cases of severe influenza illness, and between 290 000 and 650 000 respiratory deaths [1]. Influenza-like-illness (ILI) describes a set of symptoms and is a practical way for health-care workers to easily estimate likely influenza cases. The Centers for Disease Control (CDC) collects and disseminates ILI information, and has, for the last several years, run a forecasting challenge (the CDC Flu Challenge) for modelers to predict near-term weekly incidence, week of season onset, week of peak, and intensity of peak. We have developed a modeling framework that accounts for a range of mechanisms thought to be important for influenza transmission, such as climatic conditions, school vacations, and coupling between different regions. In this study we describe our forecast procedure for the 2016-17 season and highlight which features of our models resulted in better or worse forecasts. Most notably, we found that when the dynamics of different regions are coupled together, the forecast accuracy improves. We also found that the most accurate forecasts required some level of forecaster interaction, that is, the procedure could not be completely automated without a reduction in accuracy.

epidemiology↗
bioRxiv2017

Identifying factors that may improve mechanistic forecasting models for influenza

Influenza causes substantial morbidity and mortality and places strain on healthcare systems, some of which could be mitigated by accurate forecasting. Specific humidity and school vacations have both been shown independently to affect the transmission dynamics of influenza at large spatial scales. Here, we compare the ability of five compartmental transmission models, which include these two processes, to explain influenza-like-illness (ILI) incidence data for five United States counties for which school vacations and specific humidity data were available over a span of four seasons. We used the models in two different ways. First we fitted all available data at the same time and assessed model performance using standard measures of parsimony and goodness-of-fit. Then we conducted a retrospective forecasting study in which we attempted to predict incidence beyond a given week by fitting to data available up to that week. In general, when fitting the data using the whole season, we found that either specific humidity, school closures, or a combined model incorporating both effects captured the variability in incidence better than a fully constrained SIR-like model. Moreover, where these factors play a role, the timing of the variations suggests a causal relationship. When school vacations and specific humidity were important, the model-estimated parameters were broadly consistent. Retrospective forecasting simulations were consistent with the explanatory use of the models, with both specific humidity and school vacations giving more accurate forecasts than a simple SIR-like model in some populations and for some seasons. Our results suggest that influenza forecast models should test for the importance of different factors such as school vacations and specific humidity on a population-by-population and year-by-year basis.\n\nAuthor summaryUnderstanding the underlying factors that contribute to the transmission of influenza is crucial for developing models with predictive capabilities. In this study, we address two key effects: humidity and school vacations. We show that both can play an important role, depending on the location of the population as well as the timing of the school vacations. We then demonstrate how such mechanistic models can be used to forecast an influenza season as it unfolds, including estimates of the uncertainty of the predictions at each week of the forecast. To make better influenza forecasts, models need to test whether factors such as school vacations and specific humidity are important for a given season and population.

epidemiology↗