Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic.
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-...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0240348 |
| _version_ | 1819040310420832256 |
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| author | Dexuan Sha Xin Miao Hai Lan Kathleen Stewart Shiyang Ruan Yifei Tian Yuyang Tian Chaowei Yang |
| author_facet | Dexuan Sha Xin Miao Hai Lan Kathleen Stewart Shiyang Ruan Yifei Tian Yuyang Tian Chaowei Yang |
| author_sort | Dexuan Sha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T09:07:04Z |
| format | Article |
| id | doaj.art-45dc330a22ea4d298c40b160bc64807a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T09:07:04Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-45dc330a22ea4d298c40b160bc64807a2022-12-21T19:09:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011510e024034810.1371/journal.pone.0240348Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic.Dexuan ShaXin MiaoHai LanKathleen StewartShiyang RuanYifei TianYuyang TianChaowei YangCoronavirus 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.https://doi.org/10.1371/journal.pone.0240348 |
| spellingShingle | Dexuan Sha Xin Miao Hai Lan Kathleen Stewart Shiyang Ruan Yifei Tian Yuyang Tian Chaowei Yang Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic. PLoS ONE |
| title | Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic. |
| title_full | Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic. |
| title_fullStr | Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic. |
| title_full_unstemmed | Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic. |
| title_short | Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic. |
| title_sort | spatiotemporal analysis of medical resource deficiencies in the u s under covid 19 pandemic |
| url | https://doi.org/10.1371/journal.pone.0240348 |
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