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

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