Retrospective Parameter Estimation and Forecast of Respiratory Syncytial Virus in the United States

Julia Reis and Jeffrey Shaman

PLOS Computational Biology, 2016, vol. 12, issue 10, 1-15

Abstract: Recent studies have shown that systems combining mathematical modeling and Bayesian inference methods can be used to generate real-time forecasts of future infectious disease incidence. Here we develop such a system to study and forecast respiratory syncytial virus (RSV). RSV is the most common cause of acute lower respiratory infection and bronchiolitis. Advanced warning of the epidemic timing and volume of RSV patient surges has the potential to reduce well-documented delays of treatment in emergency departments. We use a susceptible-infectious-recovered (SIR) model in conjunction with an ensemble adjustment Kalman filter (EAKF) and ten years of regional U.S. specimen data provided by the Centers for Disease Control and Prevention. The data and EAKF are used to optimize the SIR model and i) estimate critical epidemiological parameters over the course of each outbreak and ii) generate retrospective forecasts. The basic reproductive number, R0, is estimated at 3.0 (standard deviation 0.6) across all seasons and locations. The peak magnitude of RSV outbreaks is forecast with nearly 70% accuracy (i.e. nearly 70% of forecasts within 25% of the actual peak), four weeks before the predicted peak. This work represents a first step in the development of a real-time RSV prediction system.Author Summary: Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory infection and bronchiolitis. Prompt treatment of RSV is necessary to prevent damage to lung tissue, complications from prolonged oxygen deprivation, and the potential development of reactive airway disorders. Another respiratory disease, influenza, has been simulated and forecast with high accuracy, using an epidemiological model and data assimilation methods. Here, we adapt such a model-filter system to simulate and forecast RSV epidemics in the United States. We find that the timing and volume of RSV epidemics can be forecast with high accuracy. Advance warning of the epidemic timing and volume of RSV patients has the potential to help medical centers prepare for a surge in infected patients and thus reduce delays to treatment in emergency departments.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005133

DOI: 10.1371/journal.pcbi.1005133

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