Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 10 1 2024 09 28 33 52 EN Mustafa Shebani Aboalyem School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor, Pulau Pinang, Malaysia. Mohd Tahir Ismail School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor, Pulau Pinang, Malaysia. 2023 11 25 2024 09 14    Introduction: This study uses advanced geospatial modeling to analyze key determinants of COVID-19 death cases across the Middle East and North Africa (MENA) region, aiming to reveal spatial patterns and inform targeted interventions for enhanced public health response. Methods: This study employs GIS and geostatistical models, including OLS, SLM, SEM, GWR, and MGWR, to analyze spatial and demographic determinants of COVID-19 mortality across MENA. By examining socioeconomic, medical, and demographic factors, it identifies key drivers and explores spatially non-stationary relationships impacting death rates. Results: The study found that hospital bed allocation, unemployment rate, and vaccination doses positively correlate with COVID-19 death cases in MENA, likely due to better reporting and healthcare access. The OLS model (R² = 0.7346) highlighted spatial autocorrelation, prompting the use of SLM and SEM, which confirmed predictor significance. GWR (R² = 0.8140) and MGWR (R² = 0.8187) revealed spatially non-stationary relationships, with hospital beds impacting the northwest (GWR) and southwest (MGWR). Unemployment was significant in the northeast (Iran, Turkey) and northwest (Morocco), while vaccination doses were notably influential in Iran and Somalia. Conclusion: This study emphasizes the significant roles of healthcare capacity, socioeconomic factors, and vaccination coverage in influencing COVID-19 mortality across MENA. It highlights the vulnerability of healthcare systems in developing countries and underscores the need for targeted resource allocation. Using spatial models like GWR and MGWR, the research reveals regional variations, especially in the northwest, advocating for tailored, region-specific interventions. By integrating GIS and geostatistical models, this analysis lays a foundation for future research on COVID-19 dynamics, providing crucial insights to inform policy measures for better public health crisis management.    Introduction: This study uses advanced geospatial modeling to analyze key determinants of COVID-19 death cases across the Middle East and North Africa (MENA) region, aiming to reveal spatial patterns and inform targeted interventions for enhanced public health response. Methods: This study employs GIS and geostatistical models, including OLS, SLM, SEM, GWR, and MGWR, to analyze spatial and demographic determinants of COVID-19 mortality across MENA. By examining socioeconomic, medical, and demographic factors, it identifies key drivers and explores spatially non-stationary relationships impacting death rates. Results: The study found that hospital bed allocation, unemployment rate, and vaccination doses positively correlate with COVID-19 death cases in MENA, likely due to better reporting and healthcare access. The OLS model (R² = 0.7346) highlighted spatial autocorrelation, prompting the use of SLM and SEM, which confirmed predictor significance. GWR (R² = 0.8140) and MGWR (R² = 0.8187) revealed spatially non-stationary relationships, with hospital beds impacting the northwest (GWR) and southwest (MGWR). Unemployment was significant in the northeast (Iran, Turkey) and northwest (Morocco), while vaccination doses were notably influential in Iran and Somalia. Conclusion: This study emphasizes the significant roles of healthcare capacity, socioeconomic factors, and vaccination coverage in influencing COVID-19 mortality across MENA. It highlights the vulnerability of healthcare systems in developing countries and underscores the need for targeted resource allocation. Using spatial models like GWR and MGWR, the research reveals regional variations, especially in the northwest, advocating for tailored, region-specific interventions. By integrating GIS and geostatistical models, this analysis lays a foundation for future research on COVID-19 dynamics, providing crucial insights to inform policy measures for better public health crisis management. https://jbe.tums.ac.ir/index.php/jbe/article/view/1339