Geospatial Coronavirus Vulnerability Regression Modelling for Malawi Based on Cumulative Spatial Data from April 2020 to May 2021

Abstract

In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi's districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease's cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.

Geospatial Variability of Cholera Cases in Malawi Based on Climatic and Socioeconomic Influences

“…Wolf et al, [49] argues that MGWR achieves higher accuracy in analyzing location-based relationships of spatially referenced data compared to GWR, whose implementation assumes that all relations in the analysis have a constant scale. This assumption of constant spatial scale in GWR is inappropriate since most spatial variables have properties with multiple complex processes and diverse spatial scales [15]. Therefore, in this study, we used MGWR since it takes into consideration the variations of spatial scales of input variable relations in order to rectify the fixed scale issues that are caused by GWR in modeling Cholera cases variability [45].…”