Abstract
Introduction: The ability to predict the growth and decline of infectious disease incidence has advanced considerably in recent years. In particular, accurate forecasts of influenza epidemiology have been developed using a number of approaches.Methods: Within our own group we produce weekly operational real-time forecasts of influenza at the municipal and state level in the U.S. These forecasts are generated using ensemble simulations depicting local influenza transmission dynamics, which have been optimized prior to forecast with observations of influenza incidence and data assimilation methods. The expected accuracy of a given forecast can be inferred in real-time through quantification of the agreement (e.g. the variance) among the ensemble of simulations.Results: Here we show that forecast expected accuracy can be further discriminated with the additional consideration of the streak or persistence of the forecast—the number of consecutive weeks the forecast has converged to the same outcome.Discussion: The findings indicate that the use of both the streak and ensemble agreement provides a more detailed and informative assessment of forecast expected accuracy.
Highlights
The ability to predict the growth and decline of infectious disease incidence has advanced considerably in recent years
Estimates of influenza incidence were generated by multiplying Google Flu Trend weekly estimates of municipal influenza-like illness (ILI)[13] with census division regional weekly laboratory-confirmed influenza positive proportions as compiled by the U.S Centers for Disease Control and Prevention (CDC) from National Respiratory and Enteric Virus Surveillance System (NREVSS) and U.S.-based World Health Organization (WHO) Collaborating Laboratories[14]
Specific humidity data were compiled from the National Land Data Assimilation System (NLDAS) project-2 dataset
Summary
The ability to predict the growth and decline of infectious disease incidence has advanced considerably in recent years. Recent applications of resampling and inference methodologies in the field of infectious disease modeling have led to the realization of real-time forecast by a number of research groups[1,2,3,4,5,6,7,8,9] These approaches utilize, in varying combinations, statistical, dynamical or combined methods to predict future disease incidence. The central idea is that if the ensemble of simulations can represent the outbreak as far observed, model-simulated forecasts will be more likely to represent future epidemic trajectories[3,8,11,12] These forecasts have been carried out at the municipal and state scale in the U.S.10
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