Real-Time Spatiotemporal Analysis of Microepidemics of Influenza and COVID-19 Based on Hospital Network Data: Colocalization of Neighborhood-Level Hotspots.
Objectives. To identify spatiotemporal patterns of epidemic spread at the community level. Methods. We extracted influenza cases reported between 2016 and 2019 and COVID-19 cases reported in March and April 2020 from a hospital network in Rhode Island. We performed a spatiotemporal hotspot analysis...
| Publicado en: | American Journal of Public Health Vol. 110; no. 12; pp. 1817 - 1825 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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American Public Health Association
Dec2020
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=146968082&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 146968082 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00900036 APH jtl: American Journal of Public Health issn: 00900036 maglogo: N pubinfo: dt: Dec2020 vid: 110 iid: 12 pid: 44 pub: American Public Health Association artinfo: ui: 146968082 10.2105/AJPH.2020.305911 ppf: 1817 ppct: 8 formats: tig: atl: Real-Time Spatiotemporal Analysis of Microepidemics of Influenza and COVID-19 Based on Hospital Network Data: Colocalization of Neighborhood-Level Hotspots. aug: au: Mylona, Evangelia K. Shehadeh, Fadi Kalligeros, Markos Benitez, Gregorio Chan, Philip A. Mylonakis, Eleftherios affil: At the time of the study, all authors were with the Infectious Diseases Division, Warren Alpert Medical School of Brown University, Providence, RI. Philip A. Chan was also with the Rhode Island Department of Health Division of Preparedness, Response, Infectious Disease, and Emergency Medical Services, Providence. su: Rhode Island Influenza COVID-19 pandemic Epidemics Neighborhoods sug: subj: Influenza COVID-19 pandemic Epidemics Neighborhoods Rhode Island ab: Objectives. To identify spatiotemporal patterns of epidemic spread at the community level. Methods. We extracted influenza cases reported between 2016 and 2019 and COVID-19 cases reported in March and April 2020 from a hospital network in Rhode Island. We performed a spatiotemporal hotspot analysis to simulate a real-time surveillance scenario. Results. We analyzed 6527 laboratory-confirmed influenza cases and identified microepidemics in more than 1100 neighborhoods, and more than half of the neighborhoods that had hotspots in a season became hotspots in the next season. We used data from 731 COVID-19 cases, and we found that a neighborhood was 1.90 times more likely to become a COVID-19 hotspot if it had been an influenza hotspot in 2018 to 2019. Conclusions. The use of readily available hospital data allows the real-time identification of spatiotemporal trends and hotspots of microepidemics. Public Health Implications. As local governments move to reopen the economy and ease physical distancing, the use of historic influenza hotspots could guide early prevention interventions, while the real-time identification of hotspots would enable the implementation of interventions that focus on small-area containment and mitigation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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