Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model. (23rd March 2022)

• Record Type: Journal Article
• Authors: Keller, Joshua P.; Zhou, Tianjian; Kaplan, Andee; Anderson, G. Brooke; Zhou, Wen
• Is Part Of: Statistics in medicine. Volume 41:Number 15(2022)
• Page Start: 2745
• Page End: 2767
• Publication Date: 2022-03-23
• Subjects: Bayesian inference -- compartmental model -- infectious disease -- mobility -- multiplicative process model -- time‐varying coefficient; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9382
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : The spread of COVID‐19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population‐level dynamics of COVID‐19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID‐19, we have developed a novel Bayesian time‐varying coefficient state‐space model for infectious disease transmission. The foundation of this model is a time‐varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID‐19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID‐19.