Differential contagiousness of respiratory disease across the United States

Abstract

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, denoted R_0. The value of R_0 gives the expected number of new cases generated by an infectious person in a wholly susceptible population and depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we estimated region-specific R_0 values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. Our estimates range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases.

Impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 variants Alpha and Delta on Coronavirus Disease 2019 transmission dynamics in four metropolitan areas of the United States

“…In earlier work, we demonstrated that new COVID-19 case detection over various periods in 2020 can be faithfully reproduced for 280 (out of the 384) metropolitan statistical areas (MSAs) in the US and all 50 states by region-specific parameterizations of a compartmental model that accounts for time-varying non-pharmaceutical interventions [5–7]. However, in 2021, the model lost its ability to capture disease transmission dynamics, presumably because of the impacts of vaccination and the emergence of more transmissible SARS-CoV-2 variants, namely, Alpha and Delta.…”

“…We determined the intervals of the step functions Y θ ( t ), P τ ( t ), and Λ τ ( t ) (i.e., θ and τ ) using a model-selection procedure described in the Appendix. Simulations and Bayesian inferences were performed as previously described [5–7] and in the Appendix. Files needed to reproduce inferences using the software package PyBioNetFit [28] are available online (https://github.com/lanl/PyBNF/tree/master/examples/Vax_and_Variants).…”

“…In 2020, Coronavirus Disease 2019 (COVID-19) transmission dynamics were significantly influenced by non-pharmaceutical interventions [1–7]. In 2021, other factors arose with significant impacts on disease transmission, namely, vaccination [8–9] and emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants [10–11].…”

“…In earlier work, with the aid of a compartmental model, we quantified the impact of non-pharmaceutical interventions on COVID-19 transmission dynamics [5–7]. We found that the multiple surges in disease incidence seen in 2020 [14] could be explained by changes in protective disease-avoiding behaviors, which we will refer to collectively as social distancing.…”

Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah)

“…Each phase is associated with three parameters: an onset time, a sum of rate constants that defines a timescale for transition to a setpoint level of adoption of disease-avoiding behaviors, and the setpoint (the fraction of the regional population adopting disease-avoiding behaviors). We have previously shown that this model is able to reproduce the dynamics of regional COVID-19 epidemics in 2020 in 280 of 384 MSAs in the US [8], including the 15 most populous metropolitan areas [7], and in all 50 states [9]. We adopted a Bayesian inference approach enabled by Markov chain Monte Carlo (MCMC) sampling to obtain samples of region-specific parameter posteriors.…”