One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut

  1. Olga Morozova
  2. Zehang Richard Li
  3. Forrest W. Crawford

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Abstract

To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.

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  1. Version published to 10.1038/s41598-021-99590-5
  2. Version published to 10.1101/2020.06.12.20126391v3 on medRxiv
  3. Version published to 10.1101/2020.06.12.20126391v2 on medRxiv
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    NIH rigor criteria are not applicable to paper type.

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  5. Version published to 10.1101/2020.06.12.20126391v1 on medRxiv