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dc.contributor.authorMichalak, Michał-
dc.contributor.authorCordes, Jack-
dc.contributor.authorKulawik, Agnieszka-
dc.contributor.authorSitek, Sławomir-
dc.contributor.authorPytel, Sławomir-
dc.contributor.authorZuzańska-Żyśko, Elżbieta-
dc.contributor.authorWieczorek, Radosław-
dc.identifier.citation"Geospatial Health", Vol. 17, iss. s1, 2022, s. 1-13pl_PL
dc.description.abstractSpatiotemporal modelling of infectious diseases such as coronavirus disease 2019 (COVID-19) involves using a variety of epidemiological metrics such as regional proportion of cases and/or regional positivity rates. Although observing changes of these indices over time is critical to estimate the regional disease burden, the dynamical properties of these measures, as well as crossrelationships, are usually not systematically given or explained. Here we provide a spatiotemporal framework composed of six commonly used and newly constructed epidemiological metrics and conduct a case study evaluation. We introduce a refined risk estimate that is biased neither by variation in population size nor by the spatial heterogeneity of testing. In particular, the proposed methodology would be useful for unbiased identification of time periods with elevated COVID-19 risk without sensitivity to spatial heterogeneity of neither population nor testing coverage.We offer a case study in Poland that shows improvement over the bias of currently used methods. Our results also provide insights regarding regional prioritisation of testing and the consequences of potential synchronisation of epidemics between regions. The approach should apply to other infectious diseases and other geographical areas.pl_PL
dc.rightsUznanie autorstwa-Użycie niekomercyjne 3.0 Polska*
dc.subjectrelative riskpl_PL
dc.subjectunbiased metricspl_PL
dc.titleReducing bias in risk indices for COVID-19pl_PL
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Uznanie autorstwa - użycie niekomercyjne 3.0 Polska Creative Commons License Creative Commons