Spatiotemporal Characteristics and Risk Factors of the Covid-19 Pandemic in New York State
Implication of Future Policies
Bibliographic Data
| ID | 22034289 |
|---|---|
| Authors | Anran Zheng (0000-0002-2874-455X, Capital Normal University), Tao Wang (0000-0002-8367-8946, Capital Normal University, corresponding author), Xiaojuan Li (0000-0002-7510-6593, Capital Normal University) |
| Year | 2021 |
| Volume | 10 |
| Issue | 9 |
| Pages | 627 |
| Publication date | 2021-09-18 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10090627 |
| OpenAlex | W3201293508 |
| Language | EN |
| Citations received | 2 |
| References cited | 25 |
The Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Based on the county-level COVID-19 cases and environmental factors in the state from March to December 2020, this study investigates spatiotemporal clustering patterns using spatial autocorrelation and space-time scan analysis. Environmental factors influencing the COVID-19 spread were analyzed based on the Geodetector model. Infection clusters first appeared in southern New York State and then moved to the central western parts as the epidemic developed. The statistical results of space-time scan analysis are consistent with those of spatial autocorrelation analysis. The analysis results of Geodetector showed that both temperature and population density were strong indications of the monthly incidence of COVID-19, especially in March and April 2020. There is a trend of increasing interactions between various risk factors. This study explores the spatiotemporal pattern of COVID-19 in New York State over ten months and explains the relationship between the disease transmission and influencing factors
Disease · Environmental health · Geography · Pandemic · Population · Socioeconomic status · Sociology · Spatial analysis · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Demography · Medicine · Spatial and Panel Data Analysis
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| Citations per year | 0.5 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |