Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes

We aim to understand the spatial inequality in Coronavirus disease 2019 (COVID-19) positivity rates across New York City (NYC) ZIP codes. Applying Bayesian spatial negative binomial models to a ZIP-code level dataset (N = 177) as of May 31st, 2020, we find that (1) the racial/ethnic minority groups...

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Veröffentlicht in:	Health & place 2021-05, Vol.69, p.102574-102574, Article 102574
Hauptverfasser:	Yang, Tse-Chuan, Kim, Seulki, Zhao, Yunhan, Choi, Seung-won Emily
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayes Theorem Bayesian analysis Bayesian spatial modeling Coronaviruses COVID-19 COVID-19 - epidemiology Ethnic groups Ethnicity - statistics & numerical data Geography Health status Health Status Disparities Household size Humans Inequality Life Sciences & Biomedicine Mathematical models Minority & ethnic groups Minority groups New York City New York City - epidemiology Public, Environmental & Occupational Health Queens Residence Characteristics - statistics & numerical data Science & Technology Socioeconomic Factors Spatial Analysis Spatial inequality Teleworking - statistics & numerical data Viral diseases
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