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\begin{document}

\title{The Vida Decision Support System: an integrated modeling framework to
inform and monitor regional COVID-19 responses}



\author[1]{Jack Reid}%
\author[2]{Katlyn Turner}%
\author[2]{Seamus Lombardo}%
\author[2]{Mulan Jiang}%
\author[3]{David Lagomasino}%
\author[4]{Mohammad Jalali}%
\author[5]{Eric Ashcroft}%
\author[2]{Danielle Wood}%
\affil[1]{Massachusetts Institute of Technology,Massachusetts Institute of Technology}%
\affil[2]{Massachusetts Institute of Technology}%
\affil[3]{East Carolina University}%
\affil[4]{Harvard University}%
\affil[5]{Blue Raster LLC}%


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  \date{May 17, 2024}


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\begin{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.%
\end{abstract}%



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