J. Reis

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

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.

Simulation of four respiratory viruses and inference of epidemiological parameters

While influenza has been simulated extensively to better understand its behavior and predict future outbreaks, most other respiratory viruses have seldom been simulated. In this study, we provide an overview of four common respiratory viral infections: respiratory syncytial virus (RSV), respiratory adenovirus, rhinovirus and parainfluenza, present specimen data collected 2004–2014, and simulate outbreaks in 19 overlapping regions in the United States. Pairing a compartmental model and data assimilation methods, we infer key epidemiological parameters governing transmission: the basic reproductive number R0 and length of infection D. RSV had been previously simulated, and our mean estimate of D and R0 of 5.2 days and 2.8, respectively, are within published clinical and modeling estimates. Among the four virus groupings, mean estimates of R0 range from 2.3 to 3.0, with a lower and upper quartile range of 2.0–2.8 and 2.6–3.2, respectively. As rapid PCR testing becomes more common, estimates of the observed virulence and duration of infection for these viruses could inform decision making by clinicians and officials for managing patient treatment and response.