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flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic.
Lemaitre, Joseph C; Loo, Sara L; Kaminsky, Joshua; Lee, Elizabeth C; McKee, Clifton; Smith, Claire; Jung, Sung-Mok; Sato, Koji; Carcelen, Erica; Hill, Alison; Lessler, Justin; Truelove, Shaun.
Afiliação
  • Lemaitre JC; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: jo.lemaitresamra@gmail.com.
  • Loo SL; Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA.
  • Kaminsky J; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Lee EC; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • McKee C; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Smith C; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Jung SM; Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Sato K; Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA.
  • Carcelen E; Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA.
  • Hill A; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
  • Lessler J; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Carolina Population Center, University of North Carolina at Chapel
  • Truelove S; Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Epidemics ; 47: 100753, 2024 Jun.
Article em En | MEDLINE | ID: mdl-38492544
ABSTRACT
The COVID-19 pandemic led to an unprecedented demand for projections of disease burden and healthcare utilization under scenarios ranging from unmitigated spread to strict social distancing policies. In response, members of the Johns Hopkins Infectious Disease Dynamics Group developed flepiMoP (formerly called the COVID Scenario Modeling Pipeline), a comprehensive open-source software pipeline designed for creating and simulating compartmental models of infectious disease transmission and inferring parameters through these models. The framework has been used extensively to produce short-term forecasts and longer-term scenario projections of COVID-19 at the state and county level in the US, for COVID-19 in other countries at various geographic scales, and more recently for seasonal influenza. In this paper, we highlight how the flepiMoP has evolved throughout the COVID-19 pandemic to address changing epidemiological dynamics, new interventions, and shifts in policy-relevant model outputs. As the framework has reached a mature state, we provide a detailed overview of flepiMoP's key features and remaining limitations, thereby distributing flepiMoP and its documentation as a flexible and powerful tool for researchers and public health professionals to rapidly build and deploy large-scale complex infectious disease models for any pathogen and demographic setup.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / SARS-CoV-2 / COVID-19 Limite: Humans Idioma: En Revista: Epidemics / Epidemics (Online) Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / SARS-CoV-2 / COVID-19 Limite: Humans Idioma: En Revista: Epidemics / Epidemics (Online) Ano de publicação: 2024 Tipo de documento: Article