The coronavirus disease (COVID-19) that appeared in 2019 gave rise to a major global health crisis that is still topping global health, socioeconomic, and intervention program agendas. Although the outbreak of COVID-19 has had substantial and devastating impacts on developed countries, the countries of the Global South share a higher proportion of the epidemic’s effects as shown particularly in morbidity and mortality rates in low-income countries. Modeling the effects of underlying factors and disease mortality is essential to plan effective control strategies for disease transmission and risks. The relationship between COVID-19 mortality rates and sociodemographic and health determinants can highlight various epidemic fatality risks. In this research, geographic information systems (GIS) and a multilayer perceptron (MLP) artificial neural network (ANN) were adopted to model and examine variations in COVID-19 mortality rates in the Global South. The model’s performance was tested using statistical measures of mean square error (MSE), root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R²). The findings indicated that the most important variables in explaining spatial mortality rate variations were the size of the elderly (sixty-five and older) population, accessibility to handwashing facilities, and hospital beds per 1,000 population. Mapping the explanatory variables and estimated mortality rates and determining the importance of each variable in explaining the spatial variation of COVID-19 death rates across countries of the Global South can shed light on how public health care and demographic structures can offer policymakers invaluable guidelines to planning effective intervention strategies.

2019 年出现的冠状病毒病 (COVID-19) 引发了一场重大的全球健康危机，该危机仍是全球健康、社会经济和干预计划议程的首位。尽管 COVID-19 的爆发对发达国家造成了巨大的破坏性影响，但全球南方国家在该流行病的影响中所占的比例更高，尤其是在低收入国家的发病率和死亡率方面。对潜在因素和疾病死亡率的影响进行建模对于规划疾病传播和风险的有效控制策略至关重要。COVID-19 死亡率与社会人口和健康决定因素之间的关系可以突出各种流行病死亡风险。在这项研究中，采用地理信息系统 (GIS) 和多层感知器 (MLP) 人工神经网络 (ANN) 来模拟和检查全球南方 COVID-19 死亡率的变化。使用均方误差 (MSE)、均方根误差 (RMSE)、平均偏差误差 (MBE) 和决定系数 (2 ⁾。研究结果表明，解释空间死亡率变化的最重要变量是老年人（65 岁及以上）人口的规模、洗手设施的可及性以及每 1,000 人的病床。绘制解释变量和估计死亡率，并确定每个变量在解释全球南方国家 COVID-19 死亡率空间变化方面的重要性，可以阐明公共卫生保健和人口结构如何为决策者提供宝贵的规划指导有效的干预策略。