ArticlePloS one2020
Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Joint spatiotemporal evaluation of multiple healthcare resources: hospitals, hospital beds and physicians across 365 Chinese cities over 22 years.Frontiers in public health · 2025Article
- Revealing spatiotemporal inequalities, hotspots, and determinants in healthcare resource distribution: insights from hospital beds panel data in 2308 Chinese counties.BMC public health · 2024Article
- Article
- Relative bed allocation for COVID-19 patients, EHR investments, and COVID-19 mortality outcomes.PloS one · 2023Article
- Spatial Analysis on Supply and Demand of Adult Surgical Masks in Taipei Metropolitan Areas in the Early Phase of the COVID-19 Pandemic.International journal of environmental research and public health · 2022Article
- Spatiotemporal data mining: a survey on challenges and open problems.Artificial intelligence review · 2022Article
- The impact of social distancing on community case count in the United States: Testing the efficacy of protection motivation theory during early stages of the COVID-19 pandemic.Risk, hazards & crisis in public policy · 2021Article
- The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic.Transactions in GIS : TG · 2021Article
- The use of post-anaesthesia care units as a supply of ICU beds while maintaining scheduled surgery: A cross-sectional web-based feasibility survey in France.Journal of clinical anesthesia · 2021Article
- Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study.Journal of medical Internet research · 2021Article
- COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records.IEEE access : practical innovations, open solutions · 2021Article
- Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods.Frontiers in public health · 2020Article
- Assessing the Disruption Impact on Healthcare Delivery.Journal of primary care & community healthArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) was first identified in December 2019 in Wuhan, China as an infectious disease, and has quickly resulted in an ongoing pandemic. A data-driven approach was developed to estimate medical resource deficiencies due to medical burdens at county level during the COVID-19 pandemic. The study duration was mainly from February 15, 2020 to May 1, 2020 in the U.S. Multiple data sources were used to extract local population, hospital beds, critical care staff, COVID-19 confirmed case numbers, and hospitalization data at county level. We estimated the average length of stay from hospitalization data at state level, and calculated the hospitalized rate at both state and county level. Then, we developed two medical resource deficiency indices that measured the local medical burden based on the number of accumulated active confirmed cases normalized by local maximum potential medical resources, and the number of hospitalized patients that can be supported per ICU bed per critical care staff, respectively. Data on medical resources, and the two medical resource deficiency indices are illustrated in a dynamic spatiotemporal visualization platform based on ArcGIS Pro Dashboards. Our results provided new insights into the U.S. pandemic preparedness and local dynamics relating to medical burdens in response to the COVID-19 pandemic.
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