Spatial Assessment of COVID-19 First-Wave Mortality Risk in the Global South.
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...
| Publicado en: | Professional Geographer Vol. 74; no. 3; pp. 440 - 459 |
|---|---|
| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
2022
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=157355948&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 157355948 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00330124 PGG jtl: Professional Geographer issn: 00330124 maglogo: Y pubinfo: dt: 2022 vid: 74 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 157355948 10.1080/00330124.2021.2009888 ppf: 440 ppct: 19 formats: tig: atl: Spatial Assessment of COVID-19 First-Wave Mortality Risk in the Global South. aug: au: Mansour, Shawky Abulibdeh, Ammar Alahmadi, Mohammed Ramadan, Elnazir affil: Alexandria University, Egypt, and Sultan Qaboos University, Oman Qatar University, Qatar King Abdulaziz City for Science and Technology, Saudi Arabia Sultan Qaboos University, Oman su: COVID-19 Developing countries Geographic information systems Standard deviations Low-income countries Artificial neural networks Fish mortality sug: subj: COVID-19 Developing countries Geographic information systems Standard deviations Low-income countries Artificial neural networks Fish mortality keyword: ANN COVID-19 mortality GIS modeling Global South sociodemographic determinants determinantes sociodemográficos modelización con SIG mortalidad por COVID-19 Sur Global 人工神经网络 发展中国家 地理信息系统建模 新冠病毒死亡率 社会人口决定因素。 ANN COVID-19 mortality GIS modeling Global South sociodemographic determinants determinantes sociodemográficos modelización con SIG mortalidad por COVID-19 Sur Global 人工神经网络 发展中国家 地理信息系统建模 新冠病毒死亡率 社会人口决定因素。 ab: 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 (R). 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|