Spatial Statistical Analysis and Risk Factor Identification of Covid-19 in China
Bibliographic Data
| ID | 15329035 |
|---|---|
| Authors | Jinyang Liu (0009-0004-2047-3400, School of Mathematics, Harbin Institute of Technology, Harbin, China and School of Statistics, Chengdu University of Information Technology, Chengdu, China, corresponding author), Boping Tian (0000-0001-5267-6764, Harbin Institute of Technology) |
| Year | 2023 |
| Volume | 47 |
| Issue | 4 |
| Pages | 681-705 |
| Publication date | 2023-08-30 |
| Open Access | No |
| Type | ARTICLE |
| Venue | American Journal of Health Behavior (JOURNAL) |
| Journal identifiers | ISSN: 1087-3244 • E-ISSN: 1945-7359 |
| Publisher | PNG Publications (PUBLISHER • US) |
| DOI | 10.5993/ajhb.47.4.5 |
| OpenAlex | W4388003179 |
| Language | EN |
| References cited | 22 |
Objectives: In this paper, we discuss the spatial aggregation and evolution of COVID-19 in China and identify the risk factors affecting the spread of the disease. The aim is to provide insights that can be used to implement timely and effective interventions in the face of similar infectious diseases in the future and to ensure the safety of people around the world. Methods: We used spatial statistics and measurement methods to analyze the spatial aggregation and evolution of COVID-19 in China. We carried out spatial visualization mapping and spatial statistical analysis on the data of the epidemic. Various risk factors of COVID-19 spread at the provincial level in China were comprehensively discussed by combining geographic detector and spatial Dubin model. Results: The analysis revealed the spatial aggregation and evolution patterns of COVID-19 in China and the risk factors affecting the spread of the disease, including population density, transportation network, and climate factors. The geographic detector and spatial Dubin model were effective in identifying the risk factors, and the results provide valuable insights for implementing timely and effective interventions. Conclusions: We emphasize the importance of timely and effective interventions in the face of infectious diseases such as COVID-19. Our results can raise awareness of prevention and control and respond to potential outbreaks of similar infectious diseases in the future. The study provides a deep understanding of COVID-19 and its spatial patterns, and the insights gained can safeguard both lives and property worldwide
China · Coronavirus disease 2019 (COVID-19 · Disease · Environmental health · Geography · Identification (biology · Infectious disease (medical specialty · Outbreak · Population · Psychological intervention · Remote sensing · Risk analysis (engineering · Spatial analysis · Spatial ecology · Spatial epidemiology · Computer Science · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Medicine · Zoonotic diseases and public health · Ecology · Epidemiology
The Covid-19 pandemic
Spatial epidemic dynamics of the Covid-19 outbreak in China
The effect of human mobility and control measures on the Covid-19 epidemic in China
GIS-based spatial modeling of Covid-19 incidence rate in the continental United States
Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China
The association between ambient temperature and mortality of the coronavirus disease 2019 (Covid-19) in Wuhan, China
| Highly cited | No |
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