COVID-19 spatial distribution and its correlation with social-economic factors in Mzuzu city.

Abstract

Geospatial analytics could be used to approach an understanding of distribution and risk factors that have impacted COVID-19 spread at a ward level. Despite this, the current and available reported COVID-19 data in Mzuzu City is aggregated at the district level and not spatially correlated with other risk factors because the data has not been downscaled to locations to pinpoint COVID-19 hot spots. Therefore, this study aimed to analyse the spatial distribution of COVID-19 cases and their correlation with Social-economic factors in Mzuzu City. Firstly, spatial autocorrelation was used to provide insights into the spread and patterns of COVID-19 within the city. Secondly, the spatial association analysis was performed to assess Social-economic indicators which possibly influence the incidence of COVID-19. Finally, a proximity analysis was performed to determine the accessibility of COVID-19 vaccination sites and the feasibility of COVID-19 control measures in densely populated areas. The study has revealed that the spatial distribution of COVID-19 incidence was a clustered pattern, and it was highly distributed in populated areas of Lubinga ward and Masasa ward. The autocorrelation analysis based on monthly reported cases indicated that the cases were not statistically significant showing no correlation but on Social-economic impacts the study found that 83 percent of the population experienced total or partial loss in economic activities during the COVID-19 pandemic. The findings of proximity analysis in this study have shown that effectively implementing social distancing in informal settlements in Mzuzu City would be difficult. Despite this outcome, the study concluded that it was possible to vaccinate the entire population bearing that the residents fall within the 8km buffer of one or more Health Centers. The study recommends the use of spatial analytics and downscaling of data to easily analyse and prioritize hot spots during health emergencies such as COVID-19.