Author(s)

Emmanuel Chinkaka^1,2*, Kyle F. Davis¹, Dawnwell Chiwanda², Billy Kachingwe², Stanley Gusala², Richard Mvula², Francis Chauluka³, Julie Michelle Klinger¹

¹Department of Geography and Spatial Sciences, University of Delaware, Newark, USA.
²Department of Earth Sciences, Malawi University of Science and Technology, Limbe, Malawi.
³Department of Water Resources Management, Malawi University of Science and Technology, Limbe, Malawi.

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.

Malawi, Geospatial, Spatial Dependency, Coronavirus, Vulnerability, Spatial Variability, Prevalence, MGWR, GIS

Chinkaka, E. , Davis, K. , Chiwanda, D. , Kachingwe, B. , Gusala, S. , Mvula, R. , Chauluka, F. and Klinger, J. (2023) Geospatial Coronavirus Vulnerability Regression Modelling for Malawi Based on Cumulative Spatial Data from April 2020 to May 2021. Journal of Geographic Information System, 15, 110-121. doi: 10.4236/jgis.2023.151007.