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

Research Output:
Contribution to journal
Article
Peer-review

Open access

Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well
  • SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

Publication metrics

Metrics

SciVal
FWCI
1.98
SciVal
Author count
2
SciVal
Paper percentile
87
SciVal
Citations
33
Scopus
Citations

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.

Publication Information

Output type

Research Output:
Contribution to journal
Article
Peer-review

Original language

English

Article number

102523

Pages from-to (Number of pages)

Pages 102523

Journal (Volume, Issue Number)

Applied Geography (Volume 134)

Publication milestones

  • Published - 09/2021

Publication status

Published - 09/2021

ISSN

0143-6228

Publication IDs

  • Scopus: 85111474384
  • PubMed: 34334843

Funding Details

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.
FundersFunding numbers
Geographic Information Systems and Big Data technologies
-