The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic

Abstract

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data‐driven knowledge discovery and more sophisticated spatial analysis central to the COVID‐19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county‐level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high‐quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user‐centered design to ground the direction and design of Atlas application development.

Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

“…Additional community-level health factors were identified from theoretical evidence and literature on neighborhood characteristics and COVID-19 mortality 25 , 26 using a participatory design approach. 14 These factors included income inequality, uninsured rate, primary care physicians, preventable hospital stays, severe housing problems rate, and access to broadband internet. Preventable hospital stays were measured as the rate of hospital stays for ambulatory care–sensitive conditions per 100 000 Medicare enrollees.…”

“…The Modifiable Areal Unit Problem in geography highlights issues of different findings produced by varied levels of aggregation, 13 suggesting that state-level estimates can mask heterogeneous population patterns and make community-level impacts difficult to evaluate. 14 Initial explorations of CDC data underscore this challenge. Figure 1 illustrates provisional CDC data aggregated by the US Department of Health and Human Services (HHS) JAMA Network Open | Diversity, Equity, and Inclusion region, 15 the most granular level at which both race-and age-disaggregated data are currently available.…”

Availability of best practices for opioid use disorder in jails and related training and resource needs: findings from a national interview study of jails in heavily impacted counties in the U.S.

“…io/ opioidpolicy-scan/. This public use data set includes data from the Bureau of Justice Statistics, Centers for Disease Control and Prevention, the Current Population Survey, and SAMHSA (Kolak et al, 2021).…”

Towards a Methodology for Building Dynamic Urgent Applications on Continuum Computing Platforms

“…They provide holistic ecosystems comprising computing resources as well as data, software, networking along with coordination and user support. While integrating sophisticated data analytics for emerging applications deployed at the edge of the network presents additional architectural and technical challenges, CIs have a real potential to address today's global challenges, such as pandemic management [1] or wildfire management [2].…”

“…This methodology was inspired by reports of Utah's air pollution as a result of the wildfires in California [9]. The SAGE platform, a cutting-edge CI aimed at AI-driven research at the edge 1 , is equipped with sensors that could help classify, forecast, and connect these occurrences to better alerts and responses.…”