Abstract and Introduction
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.
Mathematical transmission models are useful tools for predicting the magnitude, duration, and severity of the severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic. However, widely used national-level models might not capture regional heterogeneity. The coronavirus disease (COVID-19) outbreak in Colorado, USA, has been the subject of numerous discrepant projections from the Institute for Health Metrics and Evaluation and other modeling groups, which might have structural and data source explanations, highlighting the need for ensuring that models are fit to local epidemiologic data.[2–4]
We report on our experience using a locally tailored model to inform policy in Colorado. Social distancing policies, intended to decrease contact rates, have been cornerstone public health tools for pandemic control, and these strategies have been adopted to control SARS-CoV-2 globally.[2,5] Until recently, evidence of the effects of social distancing has come primarily from studies of the consequences of school and transit closures on influenza transmission.[3,4,6] Early 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. However, 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.
One of the defining characteristics of the COVID-19 pandemic is the need for rapid response in the face of imperfect and incomplete information. Mathematical models of infectious disease transmission can be used in real-time to estimate parameters, such as the effective reproductive number (Re) and the efficacy of current and future intervention measures, providing time-sensitive data to policy-makers. 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.
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 Re after emergence of SARS-CoV-2 and the implementation of statewide social distancing policies in Colorado. We supplemented model estimates with an analysis of mobility by using mobile-device location data. Estimates were generated in near real time, at multiple time-points, with a rapidly evolving understanding of SARS-CoV-2. At each time point, we generated projections of the possible course of the outbreak under future social distancing scenarios. Findings were regularly provided to key Colorado decision-makers. We present estimates generated at multiple time points to document how our model, estimates and projections evolved over time. Although our analysis is specific to Colorado, our experience highlights the need for locally calibrated transmission models to inform public health preparedness and policymaking, along with ongoing analyses of the impact of policies to slow the spread of SARS-CoV-2.
Emerging Infectious Diseases. 2021;27(9):2312-2322. © 2021 Centers for Disease Control and Prevention (CDC)