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

  • Morozova, Olga1
  • Li, Zehang Richard2
  • Crawford, Forrest W3, 4, 5, 6
  • 1 Program in Public Health and Department of Family, Population and Preventive Medicine, Stony Brook University (SUNY), NY, USA.
  • 2 Department of Statisitcs, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • 3 Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
  • 4 Department of Statistics & Data Science, Yale University, New Haven, CT, USA.
  • 5 Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.
  • 6 Yale School of Management, New Haven, CT, USA.
Published Article
medRxiv : the preprint server for health sciences
Publication Date
Apr 23, 2021
DOI: 10.1101/2020.06.12.20126391
PMID: 32587978


To support public health policymakers in Connecticut, we developed a 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, as well as estimates of important features of disease transmission, public behavior, healthcare response, and clinical progression of disease. In this paper, we describe a transmission model developed to meet the changing requirements of public health policymakers and officials in Connecticut from March 2020 to February 2021. We outline the model design, implementation and calibration, and describe how projections and estimates were used to support decision-making in Connecticut throughout the first year of the pandemic. We calibrated this model to data on deaths and hospitalizations, 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 time-varying 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 describe methodology for producing projections of epidemic evolution under uncertain future scenarios, as well as analytical tools for estimating epidemic features that are difficult to measure directly, such as cumulative incidence and the effects of non-pharmaceutical interventions. 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 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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