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

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 and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March–December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.Author summary: Due to the time-varying nature of numerous drivers of disease transmission, flexible, semi-parametric models of disease transmission are necessary to faithfully represent the complex, hidden dynamic processes that drive epidemics of emerging pathogens like SARS-CoV-2. Adequate models are essential to guide policy decisions. We present a data-driven semi-parametric model of SARS-CoV-2 transmission that embeds a latent process within a mechanistic compartmental model. The latent process sub-model captures temporal variation in precautionary behaviors that cannot be easily measured. We show that temporal variation in transmission strength is best explained by mobility early in the pandemic and by this latent process later in the pandemic. The model is flexible and incorporates known biological parameters and disease transmission processes.