{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T11:42:54Z","timestamp":1775734974365,"version":"3.50.1"},"reference-count":23,"publisher":"BMJ","issue":"10","license":[{"start":{"date-parts":[[2021,10,3]],"date-time":"2021-10-03T00:00:00Z","timestamp":1633219200000},"content-version":"unspecified","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UKRI\/DHSC","award":["COV0036"],"award-info":[{"award-number":["COV0036"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2021,9,15]]},"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>A geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>All hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Outcome measures<\/jats:title>\n                    <jats:p>Predictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Peak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%\u201320% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Local demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2021-050574","type":"journal-article","created":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T17:05:24Z","timestamp":1633367124000},"page":"e050574","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":7,"title":["Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK"],"prefix":"10.1136","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8972-769X","authenticated-orcid":false,"given":"Samuel I","family":"Watson","sequence":"first","affiliation":[{"name":"Institute of Applied Health Research, University of Birmingham, Birmingham, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter J","family":"Diggle","sequence":"additional","affiliation":[{"name":"Faculty of Health and Medicine, Lancaster University, Lancaster, UK"},{"name":"Epidemiology and Population Health, University of Liverpool, Liverpool, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael G","family":"Chipeta","sequence":"additional","affiliation":[{"name":"Big Data Institute, University of Oxford, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard J","family":"Lilford","sequence":"additional","affiliation":[{"name":"Institute of Applied Health Research, University of Birmingham, Birmingham, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2021,10,4]]},"reference":[{"key":"2025121300064576000_11.10.e050574.1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2007658117"},{"key":"2025121300064576000_11.10.e050574.2","doi-asserted-by":"crossref","first-page":"e37","DOI":"10.1016\/S1473-3099(20)30479-5","article-title":"Highly targeted spatiotemporal interventions against cholera epidemics, 2000-19: a scoping review","volume":"21","author":"Ratnayake","year":"2021","journal-title":"Lancet Infect Dis"},{"key":"2025121300064576000_11.10.e050574.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.140033"},{"key":"2025121300064576000_11.10.e050574.4","doi-asserted-by":"crossref","unstructured":"Huang H , Wang Y , Wang Z , et al . 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