New Jersey COVID-19 municipal dataset

• Published in: Data in brief Vol. 38; p. 107426
• Main Authors: Wang, Yuqi, Allred, Sarah R., Greenfield, Emily A., Yadav, Aayush, Pletcher, Ryan, Arthur, George, Saxena, Sachin, Harig, Trista, Rankin, Emily, Rudolph, Benjamin, Sameha, Ummulkhayer, Sharma, Shwetal, Yan, Shibin
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01-10-2021; Elsevier
• Subjects: Data; Geospatial analysis; Pandemic; Public health; SARS-CoV-2, Case rate; Pandemic; Geospatial analysis; SARS-CoV-2, Case rate; Public health
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2021.107426

Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.