Open AccessReducing bias in risk indices for COVID-19
Author(s) -
Michał Michalak,
Jack Cordes,
Agnieszka Kulawik,
Sławomir Sitek,
Sławomir Pytel,
Elżbieta Zuzańska-Żyśko,
Radosław Wieczorek
Publication year - 2022
Publication title -
geospatial health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.545
H-Index - 36
eISSN - 1970-7096
pISSN - 1827-1987
DOI - 10.4081/gh.2022.1013
Subject(s) - covid-19 , identification (biology) , spatial epidemiology , population , spatial heterogeneity , econometrics , risk assessment , population size , statistics , epidemiology , computer science , geography , disease , infectious disease (medical specialty) , environmental health , biology , medicine , mathematics , ecology , outbreak , virology , computer security , pathology
Spatiotemporal 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.