The main factors influencing COVID-19 spread and deaths in Mexico: A comparison between phases I and II

Francisco Benita, Francisco Gasca-Sanchez

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This article investigates the geographical spread of confirmed COVID-19 cases and deaths across municipalities in Mexico. It focuses on the spread dynamics and containment of the virus between Phase I (from March 23 to May 31, 2020) and Phase II (from June 1 to August 22, 2020) of the social distancing measures. It also examines municipal-level factors associated with cumulative COVID-19 cases and deaths to understand the spatial determinants of the pandemic. The analysis of the geographic pattern of the pandemic via spatial scan statistics revealed a fast spread among municipalities. During Phase I, clusters of infections and deaths were mainly located at the country's center, whereas in Phase II, these clusters dispersed to the rest of the country. The regression results from the zero-inflated negative binomial regression analysis suggested that income inequality, the prevalence of obesity and diabetes, and concentration of fine particulate matter (PM 2.5) are strongly positively associated with confirmed cases and deaths regardless of lockdown.

Original languageEnglish
Article number102523
Pages (from-to)102523
JournalApplied Geography
Volume134
DOIs
Publication statusPublished - Sep 2021

Bibliographical note

Funding Information:
Finally, it is vital for government to continue with the important efforts of testing procedures, tracking of individuals, social distancing measures, and plans for hospital reconversion and immediate expansion to mitigate the dynamics of disease spread. Furthermore, sudden outbreaks can be brought under control with quick and decisive action supported by Geographic Information Systems and Big Data technologies. Data acquisition methods, and integration of data sources from various organizations can be used to develop COVID-19 geo-located prediction systems based on real-time operational data assimilation and parameter estimation (Li et al., 2020; Zhou et al., 2020). This study, therefore, may be useful for decision-making on health policy at the municipal-level as it not only allows the prompt detection of hotspots of transmission and death but also unveils its relationship with socio-economic, health, climate, and mobility factors.

Publisher Copyright:
© 2021 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Forestry
  • Geography, Planning and Development
  • Environmental Science(all)
  • Tourism, Leisure and Hospitality Management

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