{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:11Z","timestamp":1772138051424,"version":"3.50.1"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Partners in Health"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Amidst the continuing spread of coronavirus disease-19 (COVID-19), real-time data analysis and visualization remain critical the general public to track the pandemic\u2019s impact and to inform policy making by officials. Multiple metrics permit the evaluation of the spread, infection and mortality of infectious diseases. For example, numbers of new cases and deaths provide easily interpretable measures of absolute impact within a given population and time frame, while the effective reproduction rate provides an epidemiological measure of the rate of spread. By evaluating multiple metrics concurrently, users can leverage complementary insights into the impact and current state of the pandemic when formulating prevention and safety plans for oneself and others. We describe COVID-19 Spread Mapper, a unified framework for estimating and quantifying the uncertainty in the smoothed daily effective reproduction number, case rate and death rate in a region using log-linear models. We apply this framework to characterize COVID-19 impact at multiple geographic resolutions, including by US county and state as well as by country, demonstrating the variation across resolutions and the need for harmonized efforts to control the pandemic. We provide an open-source online dashboard for real-time analysis and visualization of multiple key metrics, which are critical to evaluate the impact of COVID-19 and make informed policy decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Our model and tool are publicly available as implemented in R and hosted at https:\/\/metrics.covid19-analysis.org\/. The source code is freely available from https:\/\/github.com\/lin-lab\/COVID19-Rt and https:\/\/github.com\/lin-lab\/COVID19-Viz.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac129","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T07:09:21Z","timestamp":1646204961000},"page":"2661-2663","source":"Crossref","is-referenced-by-count":7,"title":["COVID-19 Spread Mapper: a multi-resolution, unified framework and open-source tool"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1319-9333","authenticated-orcid":false,"given":"Andy","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Harvard TH Chan School of Public Health , Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7869-5223","authenticated-orcid":false,"given":"Sheila M","family":"Gaynor","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard TH Chan School of Public Health , Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rounak","family":"Dey","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard TH Chan School of Public Health , Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6423-0444","authenticated-orcid":false,"given":"Haoyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard TH Chan School of Public Health , Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corbin","family":"Quick","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard TH Chan School of Public Health , Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7067-7752","authenticated-orcid":false,"given":"Xihong","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard TH Chan School of Public Health , Boston, MA 02115, USA"},{"name":"Department of Statistics, Harvard University , Cambridge, MA 02138, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"2023041402561508500_","doi-asserted-by":"crossref","first-page":"112","DOI":"10.12688\/wellcomeopenres.16006.1","article-title":"Estimating the time-varying reproduction number of SARS-CoV-2 using national and subnational case counts","volume":"5","author":"Abbott","year":"2020","journal-title":"Wellcome Open Res"},{"key":"2023041402561508500_","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1007\/s11831-020-09472-8","article-title":"The number of confirmed cases of COVID-19 by using machine learning: methods and challenges","volume":"28","author":"Ahmad","year":"2021","journal-title":"Arch. 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