{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T06:25:17Z","timestamp":1790922317168,"version":"4.1.0"},"reference-count":35,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 110-2121-M-032-001"],"award-info":[{"award-number":["MOST 110-2121-M-032-001"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Purpose<\/jats:title>\n<jats:p>Research on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Analyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups\u2014High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk\u2014and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-level factors raising the COVID-19 infection risk. The comparisons of various model fit diagnostics indicate that the spatial models better capture the associations between COVID-19 risk and other factors.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>The key findings include (1) High- and Moderate-Risk counties are clustered in the Black Belt, the coastal areas, and Great Lakes regions. (2) Fragile labor markets (e.g., high percentages of unemployed and essential workers) and high housing inequality are associated with higher risks. (3) The Monte Carlo tests suggest that the associations between covariates and COVID-19 risk are spatially non-stationary. For example, counties in the northeastern region and Mississippi Valley experience a stronger impact of essential workers on COVID-19 risk than those in other regions, whereas the association between income ratio and COVID-19 risk is stronger in Texas and Louisiana.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The COVID-19 infection risk levels differ greatly across the US and their associations with structural inequality and sociodemographic composition are spatially non-stationary, suggesting that the same stimulus may not lead to the same change in COVID-19 risk. Potential interventions to lower COVID-19 risk should adopt a place-based perspective.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0265673","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T17:30:31Z","timestamp":1649266231000},"page":"e0265673","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0116-9257","authenticated-orcid":true,"given":"Vivian Yi-Ju","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiwoong","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4871-450X","authenticated-orcid":true,"given":"Feinuo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tse-Chuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"pone.0265673.ref001","volume-title":"U.S. and World Population Clock","author":"US Census Bureau","year":"2021"},{"key":"pone.0265673.ref002","author":"CDC","year":"2021","journal-title":"COVID Date Tracker: Centers for Disease Control and Prevention"},{"key":"pone.0265673.ref003","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.annepidem.2020.08.016","article-title":"Regional variation in COVID-19 disparities: connections with immigrant and Latinx communities in US counties.","volume":"53","author":"K Strully","year":"2021","journal-title":"Annals of epidemiology."},{"key":"pone.0265673.ref004","volume-title":"Covid-19: Black people and other minorities are hardest hit in US.","author":"O. 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