April 2020. Numerous expert groups have coalesced around a common general roadmap for addressing COVID-19 pandemic: (1) reduce the spread of disease through social distancing, (2) gradually ease restrictions while monitoring for resurgence and healthcare overcapacity, and (3) eventually move to pharmaceutical interventions. However, the responsibility for navigating the COVID-19 response falls largely on state and local officials, who require data at the community-level to make decisions such as resource allocation, addressing vulnerable populations, and enhancing/relaxing social distancing measures. While multiple data streams are available now to track relevant indicators such as disease incidence, personal mobility, and health, it remains challenging to integrate disparate datasets and model predictions, while effectively communicating the complexities of big data to both decision-makers and the general public.
COVID-19 Pandemic Vulnerability Index (PVI) Dashboard is a web-based, GIS-enabled, decision-support dashboard that integrates multiple data streams relevant to COVID-19 response (see graphic). Importantly, COVID-19 PVI works at the county level, where the nation’s health departments mostly reside, giving each a:
• Vulnerability Index used for country-wide prioritization ranking of future infections, and
• Vulnerability Scorecard which integrates multiple prospective indicator scores related to infection status, mitigation efforts, and pandemic-related vulnerabilities.
Furthermore, the dashboard enables simultaneous visualization of the geospatial distribution of both overall scores and the score components. This dashboard will help decision-makers to identify current and potential future county-level “hot spots,” to understand the both dynamic and baseline factors that affect pandemic spread/severity, and to determine appropriate resource allocations and mitigation efforts.
The COVID-19 PVI dashboard was adapted from an existing tool ToxPi*GIS, and is being developed by an interdisciplinary team from Texas A&M University and North Carolina State University. The team seeks input from state and local officials and other experts to refine both the underlying indicators/data sources as well as dashboard functionality so as to best support COVID-19 decision-making. Underlying data and methods are fully transparent and can be quickly amended/updated based on additional information and feedback.
Ivan Rusyn, MD, PhD (email@example.com) Texas A&M University (College Station, TX)
Weihsueh Chiu, PhD (firstname.lastname@example.org) Texas A&M University (College Station, TX)
David Reif, PhD (email@example.com) North Carolina State University (Raleigh, NC)
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