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A scenario modeling pipeline for COVID-19 emergency planning
Joseph Chadi Lemaitre; Kyra H Grantz; Joshua Kaminsky; Hannah R Meredith; Shaun A Truelove; Stephen A Lauer; Lindsay T Keegan; Sam Shah; Josh Wills; Kathryn Kaminsky; Javier Perez-Saez; Justin Lessler; Elizabeth C Lee.
Affiliation
  • Joseph Chadi Lemaitre; EPFL
  • Kyra H Grantz; Johns Hopkins Bloomberg School of Public Health
  • Joshua Kaminsky; Johns Hopkins Bloomberg School of Public Health
  • Hannah R Meredith; Johns Hopkins Bloomberg School of Public Health
  • Shaun A Truelove; Johns Hopkins Bloomberg School of Public Health
  • Stephen A Lauer; Johns Hopkins Bloomberg School of Public Health
  • Lindsay T Keegan; University of Utah
  • Sam Shah; Unaffiliated
  • Josh Wills; Unaffiliated
  • Kathryn Kaminsky; Unaffiliated
  • Javier Perez-Saez; Johns Hopkins University
  • Justin Lessler; Johns Hopkins Bloomberg School of Public Health
  • Elizabeth C Lee; Johns Hopkins Bloomberg School of Public Health
Preprint in English | medRxiv | ID: ppmedrxiv-20127894
Journal article
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ABSTRACT
Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.
License
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Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document type: Preprint
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