A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States.

ABSTRACT: To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data a

SUBMITTER: Drake JM 

PROVIDER: S-EPMC10659176 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature