flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic
Joseph C. Lemaitre,
Sara L. Loo,
Joshua Kaminsky,
Elizabeth C. Lee,
Clifton McKee,
Claire Smith,
Sung-mok Jung,
Koji Sato,
Erica Carcelen,
Alison Hill,
Justin Lessler,
Shaun Truelove
Affiliations
Joseph C. Lemaitre
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Corresponding author.
Sara L. Loo
Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA
Joshua Kaminsky
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Elizabeth C. Lee
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Clifton McKee
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Claire Smith
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Sung-mok Jung
Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Koji Sato
Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA
Erica Carcelen
Johns Hopkins University International Vaccine Access Center, Department of International Health, Baltimore, MD, USA
Alison Hill
Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
Justin Lessler
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 Hill, Chapel Hill, NC, USA
Shaun Truelove
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
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.
