A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico.

spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.

Spatial epidemiological study of the distribution, clustering, and risk factors associated with early COVID-19 mortality in Mexico.

COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems worldwide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions, including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases, including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Thus, in the present study, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico. Firstly, to evaluate how mortality risk from COVID-19 among tested individuals (MRt) is geographically distributed and secondly, to analyze the association of spatial predictors of MRt across different states in Mexico, controlling for the severity of the disease. Among health-related variables, diabetes and obesity were positively associated with COVID-19 fatality. When analyzing Mexico as a whole, we identified that both the percentages of external and internal migration had positive associations with early COVID-19 mortality risk with external migration having the second-highest positive association. As an indirect measure of urbanicity, population density, and overcrowding in households, the physicians-to-population ratio has the highest positive association with MRt. In contrast, the percentage of individuals in the age group between 10 to 39 years had a negative association with MRt. Geographically, Quintana Roo, Baja California, Chihuahua, and Tabasco (until April 2020) had higher MRt and standardized mortality ratios, suggesting that risks in these states were above what was nationally expected. Additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risk varied by zone.

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level.

The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19th to March 18th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran's I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.