Understanding the Geography of COVID-19 Case Fatality Rates in China: A Spatial Autoregressive Probit-Log Linear Hurdle Analysis

Frontiers in Public Health

article

Hanchen Yu¹  Xin Lao²  Hengyu Gu³  Zhihao Zhao²  Honghao He⁴ 

[1] Center for Geographic Analysis, Harvard University;School of Economics and Management, China University of Geosciences;Department of Geography and Resource Management, The Chinese University of Hong Kong;School of Software and Microelectronics, Peking University

关键词: COVID-19;    case fatality rate;    spatial autocorrelation;    spatial heterogeneity;    hurdle model;   

DOI  :  10.3389/fpubh.2022.751768

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

This study employs a spatial autoregressive probit-log linear (SAP-Log) hurdle model to investigate the influencing factors on the probability of death and case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) at the city level in China. The results demonstrate that the probability of death from COVID-19 and the CFR level are 2 different processes with different determinants. The number of confirmed cases and the number of doctors are closely associated with the death probability and CFR, and there exist differences in the CFR and its determinants between cities within Hubei Province and outside Hubei Province. The spatial probit model also presents positive spatial autocorrelation in death probabilities. It is worth noting that the medical resource sharing among cities and enjoyment of free medical treatment services of citizens makes China different from other countries. This study contributes to the growing literature on determinants of CFR with COVID-19 and has significant practical implications.

【 授权许可】

CC BY