Superensemble forecast of respiratory syncytial virus outbreaks at national, regional, and state levels in the United States

Abstract

Respiratory syncytial virus (RSV) infections peak during the winter months in the United States, yet the timing, intensity, and onset of these outbreaks vary each year. An RSV vaccine is on the cusp of being released; precise models and accurate forecasts of RSV epidemics may prove vital for planning where and when the vaccine should be deployed. Accurate forecasts with sufficient spatial and temporal resolution could also be used to support the prevention or treatment of RSV infections. Previously, we developed and validated an RSV forecast system at the regional scale in the United States. This model-inference system had considerable forecast skill, relative to the historical expectance, for outbreak peak intensity, total outbreak size, and onset, but only marginal skill for predicting the timing of the outbreak peak. Here, we use a superensemble approach to combine three forecasting methods for RSV prediction in the US at three different spatial resolutions: national, regional, and state. At the regional and state levels, we find a substantial improvement of forecast skill, relative to historical expectance, for peak intensity, timing, and onset outbreak up to two months in advance of the predicted outbreak peak. Moreover, due to the greater variability of RSV outbreaks at finer spatial scales, we find that improvement of forecast skill at the state level exceeds that at the regional and national levels. Such finer scale superensemble forecasts may be more relevant for effecting local-scale interventions, particularly in communities with a high burden of RSV infection.

Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

“…Although the ensemble performed well, there are many avenues for further improvement. The ensemble forecast weighted the models equally, but weighting based on historical performance may improve forecast skill (McAndrew and Reich 2019; Reich, McGowan, et al 2019; Viboud et al 2018; E. L. Ray and Reich 2018; Yamana, Kandula, and Shaman 2016; Reis et al 2019; Brooks et al 2018). However, any weighting method will need to be dynamic and allow for the incorporation of new models, potential changes in existing models, and the different sets of locations forecasted by each model.…”

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019

“…During an Ebola outbreak, real-time forecasting has the potential to support decision-making and allocation of resources, but highly accurate forecasts have proven difficult for Ebola [8,9] as well as other diseases [10][11][12][13]. Highly accurate forecasts of small, noisy outbreaks may be a fundamentally elusive ideal [14].…”

Real-time projections of epidemic transmission and estimation of vaccination impact during an Ebola virus disease outbreak in Northeastern Democratic Republic of Congo

“…Previous work has found that probabilistic forecasts can have relatively high accuracy within a few weeks, though they become less useful as time horizons grow longer 8 , and short-term forecasts may provide useful information for response organizations. 3 In this paper we apply a suite of independent methods of real-time forecasting to the Eastern DRC outbreak, to generate both short-term and long-term projections of future case counts as of the time of writing. We validate short-term projections by scoring projections derived from case count reports obtained earlier in the outbreak against subsequent known counts.…”