ArticleScientific reports2021
One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The tale of two assumptions: incorporating healthcare-seeking behaviour in epidemic forecasting.BMC infectious diseases · 2025Article
- The utility of infectious disease modelling in informing decisions for outbreak response: A scoping review.PLOS global public health · 2025Article
- Challenges of COVID-19 Case Forecasting in the US, 2020-2021.PLoS computational biology · 2024Article
- Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study.Infectious Disease Modelling · 2023Article
- Semi-parametric modeling of SARS-CoV-2 transmission using tests, cases, deaths, and seroprevalence data.ArXiv · 2023Article
- Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes.BMC public health · 2023Article
- Lessons Learned from the Lessons Learned in Public Health during the First Years of COVID-19 Pandemic.International journal of environmental research and public health · 2023Review
- Using simulation modelling and systems science to help contain COVID-19: A systematic review.Systems research and behavioral science · 2022Article
- SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021.JAMA network open · 2022Observational
- Scaling SARS-CoV-2 wastewater concentrations to population estimates of infection.Scientific reports · 2022Article
- Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges.Medical review (2021) · 2022Review
- Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data.Science advances · 2022Article
- SARS-CoV-2 attack rate and population immunity in southern New England, March 2020 - May 2021.medRxiv : the preprint server for health sciences · 2021Article
- COVID-19 Testing and Case Rates and Social Contact Among Residential College Students in Connecticut During the 2020-2021 Academic Year.JAMA network open · 2021Article
- Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data.medRxiv : the preprint server for health sciences · 2021Article
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3 authors.
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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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.