New Jersey COVID-19 municipal dataset

Yuqi Wang; Sarah R Allred; Emily A Greenfield; Aayush Yadav; Ryan Pletcher; George Arthur; Sachin Saxena; Trista Harig; Emily Rankin; Benjamin Rudolph; Ummulkhayer Sameha; Shwetal Sharma; Shibin Yan

doi:10.1016/j.dib.2021.107426

New Jersey COVID-19 municipal dataset

Data Brief. 2021 Oct:38:107426. doi: 10.1016/j.dib.2021.107426. Epub 2021 Sep 24.

Authors

Yuqi Wang¹, Sarah R Allred^{2

3}, Emily A Greenfield¹, Aayush Yadav^{2

4}, Ryan Pletcher², George Arthur^{2

5}, Sachin Saxena^{2

4}, Trista Harig^{2

3}, Emily Rankin³, Benjamin Rudolph^{2

3}, Ummulkhayer Sameha², Shwetal Sharma^{2

3}, Shibin Yan^{2

6}

Affiliations

¹ School of Social Work, Rutgers University, New Brunswick, USA.
² Senator Walter Rand Institute for Public Affairs, Rutgers University, Camden, USA.
³ Department of Psychology, Rutgers University, Camden, USA.
⁴ Department of Computer Science, Rutgers University, Camden, USA.
⁵ Department of Mathematics, Rutgers University, Camden, USA.
⁶ Department of Public Affairs, Rutgers University, Camden, USA.

Abstract

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.

Keywords: Geospatial analysis; Pandemic; Public health; SARS-CoV-2, Case rate.