covid19census: U.S. and Italy COVID-19 metrics and other epidemiological data.

Titlecovid19census: U.S. and Italy COVID-19 metrics and other epidemiological data.
Publication TypeJournal Article
Year of Publication2021
AuthorsZanettini C, Omar M, Dinalankara W, Imada ELuidy, Colantuoni E, Parmigiani G, Marchionni L
JournalDatabase (Oxford)
Date Published2021 05 15
KeywordsAlgorithms, Comorbidity, COVID-19, Databases, Factual, Datasets as Topic, Demography, Diabetes Mellitus, Health Surveys, Humans, Italy, Models, Theoretical, Pandemics, SARS-CoV-2, Software, United States

Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic in 2020, there has been a tremendous accumulation of data capturing different statistics including the number of tests, confirmed cases and deaths. This data wealth offers a great opportunity for researchers to model the effect of certain variables on COVID-19 morbidity and mortality and to get a better understanding of the disease at the epidemiological level. However, in order to draw any reliable and unbiased estimate, models also need to take into account other variables and metrics available from a plurality of official and unofficial heterogenous resources. In this study, we introduce covid19census, an R package that extracts from many different repositories and combines together COVID-19 metrics and other demographic, environment- and health-related variables of the USA and Italy at the county and regional levels, respectively. The package is equipped with a number of user-friendly functions that dynamically extract the data over different timepoints and contains a detailed description of the included variables. To demonstrate the utility of this tool, we used it to extract and combine different county-level data from the USA, which we subsequently used to model the effect of diabetes on COVID-19 mortality at the county level, taking into account other variables that may influence such effects. In conclusion, it was observed that the 'covid19census' package allows to easily extract area-level data from both the USA and Italy using few functions. These comprehensive data can be used to provide reliable estimates of the effect of certain variables on COVID-19 outcomes. Database URL:

Alternate JournalDatabase (Oxford)
PubMed ID33991092
PubMed Central IDPMC8122363
Grant ListP30 CA006973 / CA / NCI NIH HHS / United States
Related Faculty: 
Luigi Marchionni, M.D., Ph.D.


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