Estimating the Impact of Statewide Policies to Reduce Spread of Severe Acute Respiratory Syndrome Coronavirus 2 in Real Time, Colorado, USA.

Andrea G Buchwald,David Bortz,Olivia Zarella,Jimi Adams,Rachel Herlihy,Jonathan Samet,Kathryn Colborn,Debashis Ghosh,Elizabeth J Carlton,Meghan Buran,Jude Bayham

doi:10.3201/eid2709.204167

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated rapid local public health response, but studies examining the impact of social distancing policies on SARS-CoV-2 transmission have struggled to capture regional-level dynamics. We developed a susceptible-exposed-infected-recovered transmission model, parameterized to Colorado, USA‒specific data, to estimate the impact of coronavirus disease‒related policy measures on mobility and SARS-CoV-2 transmission in real time. During March‒June 2020, we estimated unknown parameter values and generated scenario-based projections of future clinical care needs. Early coronavirus disease policy measures, including a stay-at-home order, were accompanied by substantial decreases in mobility and reduced the effective reproductive number well below 1. When some restrictions were eased in late April, mobility increased to near baseline levels, but transmission remained low (effective reproductive number <1) through early June. Over time, our model parameters were adjusted to more closely reflect reality in Colorado, leading to modest changes in estimates of intervention effects and more conservative long-term projections.

Highlights

Mathematical transmission models are useful tools for predicting the magnitude, duration, and severity of the severe acute respiratory coronavirus
We developed a compartmental susceptible-exposed-infected-recovered (SEIR) model calibrated to statewide COVID-19 case and hospitalization data to estimate changes in the contact rate and the reproduction number (Re) after emergence of SARS-CoV-2 and the implementation of statewide social distancing policies in Colorado
Estimating Efficacy of Social Distancing and Other Transmission-Reducing Interventions On April 3, we generated a preliminary estimate of social distancing during phase 1, which equated to a ≈45% decrease in the contact rate (Table 2; Figure 2), and Re decreased from 5.3 to 2.4 (Figure 3)

Summary

Introduction

Mathematical transmission models are useful tools for predicting the magnitude, duration, and severity of the severe acute respiratory coronavirus. Evidence suggests that social distancing policies can suppress transmission of SARS-CoV-2 [7,8], and recent evidence suggests a strong correlation between mobility and transmission reduction [9]. These studies largely focused on periods when social distancing policies were in place, leaving critical questions unanswered regarding how long populations will comply with such measures and what happens when policies are relaxed. We describe development of such a model, in close collaboration with the Colorado Department of Health and Environment and the Governor’s office, to gauge the current and future effects of early policies to decrease social contacts and, later, the gradual relaxing of stay-at-home orders

Methods

Results

Conclusion