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

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.

Comparing COVID-19 in the antipodes: Insights from pandemic containment strategies on both sides of the Pacific

“…The comparative approach is also hindered by methodological complexities such as scale of analysis and territorial contexts Suffice to say, a single variable can affect any proposed analysis framework. Notwithstanding these challenges, existing studies argue how the spread of the pandemic in cities is influenced by factors such as the urban environment/morphology, management and governance of the city, urban design and transport, and the socio-economic characteristics of the population ( Sharifi and Khavarian-Garmsir, 2020 , Benita and Gasca-Sanchez, 2021 ).…”

“…While it is well known that the density benefits mobility and accessibility, particularly in encouraging the active use of public transport ( Choi et al, 2020 ), there is inconsistent evidence regarding the effect of population density on the spread of the virus. On the one hand, early studies have indicated that high-density areas can explain the COVID-19 infection rate in, for example, countries in the Americas such as the United States ( Andersen et al, 2021 ), Brazil (Viezzer et al, 2021) or Mexico ( Benita & Gasca-Sanchez, 2021 ). On the other hand, studies conducted in Asia have presented counterexamples of densely populated areas in which the number of infections caused by COVID-19 is no different from that of cities with low urban density, particularly in China ( Chen et al, 2021 ) and Hong Kong ( Kan et al, 2021 ).…”

Factors that most expose countries to COVID-19: a composite indicators-based approach

“…Forati and Ghose ( 2021 ) found a strong spatial relationship between activity on social networks and the spread of Covid-19 in the United States. Benita and Gasca-Sanchez ( 2021 ) showed positive correlations between income inequality, the prevalence of obesity, diabetes, and the concentration of fine particles with the cases and deaths by COVID-19 in Mexico. Paez et al ( 2020 ) showed that the incidence of COVID-19 in Spain is negatively related to temperature and humidity and positively related to Gross Domestic Product per capita and the presence of mass transport systems.…”

COVID-19 Pandemic Effect on Energy Consumption in State Universities: Michoacan, Mexico Case Study

“…In Mexico, the SARS-CoV-2 virus arrived between January and February 2020 [6,7]. The first case of COVID-19 was detected in Mexico on 27 February 2020 [8]; and in a large part of the national territory, the strategies for confinement and closure of ordinary activities began in March of the same year [9].…”