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 COVID-19 confirmed cases and deaths across municipalities in Mexico. It focuses on the spread dynamics between Phase I (from March 23th to May 31st, 2020) and II (from June 1st to August 22th, 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 Space-Time Scan Statistics (SaTScan) revealed fast spread among municipalities. During Phase I, clusters of infections and deaths were mainly located at the center of the country, while 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, prevalence of obesity and diabetes, and concentration of fine particulate matter (PM 2.5) are strongly positively associated to confirmed cases and deaths regardless of lockdown.

Effect of meteorological factors on the COVID-19 cases: a case study related to three major cities of the Kingdom of Saudi Arabia

“…Also, the other model selection criteria, such as AIC and BIC, were in favor of the ZINB model. This finding is consistent with early research findings; for example, Benita and Gasca-Sanchez (2021) strongly favored ZINB regression to model COVID-19 cases. To estimate the relationship between voter turnout and COVID-19 incidence, Flanders et al (2020) employed ZINB regression.…”

What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

“…Gini index of household income is often employed to investigate whether regions with rising income inequalities experience larger number of cases. The evidence seems to favour the idea that areas with greater spread of income inequality tend to experience a more rapid COVID-19 surge ( Benita & Gasca-Sanchez, 2021 ; Wang, et al, 2021 ; Jannot, et al, 2021 ).…”

“…Although Kendall τ and Spearman ρ offer some advantages by relaxing the normal distribution assumption, they do not account for the possible presence of temporal trends, which can strongly affect the correlation value and yield artefactual associations. More sophisticated methods such as spatial ( Andersen, et al, 2021 ; Maroko, Nash, & Pavilonis, 2020 ; Parvin, et al, 2021 ) or generalized linear models for count data, including Logistic ( Kwok, et al, 2021 ; Birhanu, Ayana, Bayu, Mohammed, & Dessie, 2021 ), Poisson ( Sugg, et al, 2021 ; Morrissey, Spooner, Salter, & Shaddick, 2021 ) and Negative Binomial ( Benita & Gasca-Sanchez, 2021 ; Strully, Yang, & Liu, 2021 ), have been also applied. Machine learning models and classification algorithms figure from amongst the most popular approaches for predicting COVID-19 occurrence using socioeconomic inputs ( Phiri, et al, 2021 ; Li, et al, 2021 ).…”

Testing the Role of Socioeconomic Levels and Government Policies in Spreading Coronavirus Disease 2019 (COVID-19) in ASEAN

“…Another study conducted by [7] In addition, the findings also show that income inequality, the presence of inherited diseases such as fatness and diabetes, and the concentration of fine particulate matter (PM 2.5) are positively associated with confirmed cases and deaths regardless of lockdown.…”