Geospatial modelling of COVID19 mortality in Oman using geographically weighted Poisson regression GWPR

• Published in: Scientific reports Vol. 15; no. 1; pp. 8138 - 17
• Main Authors: Mansour, Shawky, Alahmadi, Mohammed, Mahmoud, Ayman, Al-Shamli, Khalid, Alhabsi, Malaak, Ali, Waad
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.03.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/499; 692/699; Adult; Aged; COVID-19; COVID-19 - epidemiology; COVID-19 - mortality; Developing countries; Female; Geospatial modelling; GWPR; Health risks; Heterogeneity; Humanities and Social Sciences; Humans; LDCs; Male; Middle Aged; Mortality; multidisciplinary; Older people; Oman; Oman - epidemiology; Pandemics; Poisson Distribution; Population density; Public health; Respiratory diseases; Risk Factors; SARS-CoV-2 - isolation & purification; Science; Science (multidisciplinary); Spatial heterogeneity; Spatial Regression; Spatial variations; Oman; COVID-19 mortality; Spatial heterogeneity; Geospatial modelling; GWPR; Oman
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-92753-8

The year 2020 witnessed the arrival of the global COVID-19 pandemic, which became the most devastating public health disaster in the last decade. Understanding the underlying spatial variations of the consequences of the pandemic, particularly mortality, is crucial for plans and policies. Nevertheless, few studies have been conducted on the key determinants of COVID-19 mortality and how these might vary geographically across developing nations. Therefore, this research aims to address these gaps by adopting the Geographically Weighted Poisson Regression (GWPR) model to investigate spatial heterogeneity of COVID-19 mortality in Oman. The findings indicated that local GWPR performed better than global Ordinary Least Square (OLS) model, and the relationship between risk factors and mortality cases varied geographically at a subnational scale. The local parameter estimates of the model revealed that elderly populations, respiratory diseases, and population density were significant in predicting mortality cases. The elderly population variable was the most influential regressor, followed by respiratory diseases. The formulated policy recommendations will provide decision-makers and practitioners with key factors related to pandemic mortality so that future interventions and preventive measures can mitigate high fatality risks.