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A model for COVID-19 transmission in Connecticut.

Identifieur interne : 002239 ( Main/Corpus ); précédent : 002238; suivant : 002240

A model for COVID-19 transmission in Connecticut.

Auteurs : Olga Morozova ; Zehang Richard Li ; Forrest W. Crawford

Source :

medRxiv : the preprint server for health sciences ; 2020.

RBID : pubmed:32587978

Abstract

To support public health policymakers in Connecticut as they begin phased lifting of social distancing restrictions, we developed a county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. We calibrated this model to the local dynamics of deaths and hospitalizations and the exact timing of state interventions, including school closures and stay-at-home order. In this technical report, we describe the details of the model design, implementation and calibration, and show projections of epidemic development through the Summer of 2020 under different assumptions about the increase in contact rates following partial state reopening. Our model results are consistent with high effectiveness of state lockdown measures, but changes in human interaction patterns during the coming months are unknown. In addition, a lot of uncertainty remains with respect to several key epidemiological parameters and the effectiveness of increased testing and contact tracing capacity. As more information becomes available, we will update the projections presented in this report. Reports in this series are posted to https://crawford-lab.github.io/covid19_ct/.

DOI: 10.1101/2020.06.12.20126391
PubMed: 32587978
PubMed Central: PMC7310630

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<author><name sortKey="Crawford, Forrest W" sort="Crawford, Forrest W" uniqKey="Crawford F" first="Forrest W" last="Crawford">Forrest W. Crawford</name>
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<front><div type="abstract" xml:lang="en">To support public health policymakers in Connecticut as they begin phased lifting of social distancing restrictions, we developed a county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. We calibrated this model to the local dynamics of deaths and hospitalizations and the exact timing of state interventions, including school closures and stay-at-home order. In this technical report, we describe the details of the model design, implementation and calibration, and show projections of epidemic development through the Summer of 2020 under different assumptions about the increase in contact rates following partial state reopening. Our model results are consistent with high effectiveness of state lockdown measures, but changes in human interaction patterns during the coming months are unknown. In addition, a lot of uncertainty remains with respect to several key epidemiological parameters and the effectiveness of increased testing and contact tracing capacity. As more information becomes available, we will update the projections presented in this report. Reports in this series are posted to https://crawford-lab.github.io/covid19_ct/.</div>
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<Abstract><AbstractText>To support public health policymakers in Connecticut as they begin phased lifting of social distancing restrictions, we developed a county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. We calibrated this model to the local dynamics of deaths and hospitalizations and the exact timing of state interventions, including school closures and stay-at-home order. In this technical report, we describe the details of the model design, implementation and calibration, and show projections of epidemic development through the Summer of 2020 under different assumptions about the increase in contact rates following partial state reopening. Our model results are consistent with high effectiveness of state lockdown measures, but changes in human interaction patterns during the coming months are unknown. In addition, a lot of uncertainty remains with respect to several key epidemiological parameters and the effectiveness of increased testing and contact tracing capacity. As more information becomes available, we will update the projections presented in this report. Reports in this series are posted to https://crawford-lab.github.io/covid19_ct/.</AbstractText>
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A model for COVID-19 transmission in Connecticut.

A model for COVID-19 transmission in Connecticut.

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