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The Vida Decision Support System: an integrated modeling framework to inform and monitor regional COVID-19 responses
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  • Jack Reid,
  • Katlyn Turner,
  • Seamus Lombardo,
  • Mulan Jiang,
  • David Lagomasino,
  • Mohammad Jalali,
  • Eric Ashcroft,
  • Danielle Wood
Jack Reid
Massachusetts Institute of Technology,Massachusetts Institute of Technology

Corresponding Author:jackreid@mit.edu

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Katlyn Turner
Massachusetts Institute of Technology
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Seamus Lombardo
Massachusetts Institute of Technology
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Mulan Jiang
Massachusetts Institute of Technology
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David Lagomasino
East Carolina University
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Mohammad Jalali
Harvard University
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Eric Ashcroft
Blue Raster LLC
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Danielle Wood
Massachusetts Institute of Technology
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Abstract

The COVID-19 pandemic has had a diverse range of both direct and indirect impacts on health (both physical and mental), the economy, and the environment. The relevant data sources used to inform pandemic-related decisions have been similarly diverse, though decision-makers have primarily relied upon data sets from non-satellite sources such as traditional public health data. As we move from initial crisis response to more long-term management, there is both an interest and a need for considering a wider diversity of data sources and impacts. It is difficult for any person to absorb and respond strategically to the broad sets of data that are relevant to the issues regarding COVID management. To address this, the authors propose a five part, integrated data visualization and modeling framework entitled the Vida Decision Support System. The goal of Vida is to create an accessible and openly available online platform that can be customized by the leadership team for a city or region and bring together knowledge from several areas of expertise. The five components of Vida, each of which serve to model a specific domain, include Public Health, Environment, Socio-economic Impacts, Public Policy, and Technology. This framework is currently being designed and evaluated with collaborators in Angola, Brazil, Chile, Indonesia, Mexico and the United States. The environmental data comes from sources such as in-situ sensors and both civil and commercial earth observation instruments (Landsat, VIIRS, Planet Labs’ PlanetScope, etc.) to track factors such as water quality, forest extent and health, air quality, human mobility, and nighttime urban lighting. Similarly, socioeconomic data derives from both in-situ sources, such as local statistical agencies, and from satellite products, such as those hosted by NASA’s Socioeconomic Data and Applications Center. The authors discuss the value provided by this framework to each of the collaborators, the process used to apply the framework to each local context, and future possibilities for Vida. Even though Vida was first developed and applied in response to COVID-19, it has applications in other public health contexts where policy, environment, and socio-economic impacts are closely tied.