Bushira Kedir Mohammed, Ongala Jacob Otieno
Department of Civil and Environmental Engineering, Namibia University of Science and Technology (NUST), Windhoek, Namibia.
Department of Mathematics and Statistics, Namibia University of Science and Technology (NUST), Windhoek, Namibia.
Trans Indian Natl Acad Eng. 2021;6(2):377-394. doi: 10.1007/s41403-021-00209-y. Epub 2021 Feb 17.
The SARS-CoV-2 infections continue to increase in Namibia and globally. Assessing and mapping the COVID-19 risk zones and modeling the response of COVID-19 using different scenarios are very vital to help decision-makers to estimate the immediate number of resources needed and plan for future interventions of COVID-19 in the area of interest. This study is aimed to identify and map COVID-19 risk zones and to model future COVID-19 response of Namibia using geospatial technologies. Population density, current COVID-19 infections, and spatial interaction index were used as proxy data to identify the different COVID-19 risk zones of Namibia. COVID-19 Hospital Impact Model for Epidemics (CHIME) V1.1.5 tool was used to model future COVID-19 responses with mobility restrictions. Weights were assigned for each thematic layer and thematic layer classes using the Analytical Hierarchy Process (AHP) tool. Suitably ArcGIS overlay analysis was conducted to produce risk zones. Current COVID-19 infection and spatial mobility index were found to be the dominant and sensitive factors for risk zoning in Namibia. Six different COVID-19 risk zones were identified in the study area, namely highest, higher, high, low, lower, and lowest. Modeling result revealed that mobility reduction by 30% within the country had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases and delay to the peak number. The research output could help policy-makers to estimate the immediate number of resources needed and plan for future interventions of COVID-19 in Namibia, especially to assess the potential positive effects of mobility restriction.
纳米比亚和全球范围内,新型冠状病毒肺炎(SARS-CoV-2)感染病例持续增加。评估和绘制新冠疫情风险区域,并使用不同情景对新冠疫情应对情况进行建模,对于帮助决策者估算所需即时资源数量以及规划新冠疫情在相关区域的未来干预措施至关重要。本研究旨在利用地理空间技术识别和绘制新冠疫情风险区域,并对纳米比亚未来的新冠疫情应对情况进行建模。人口密度、当前新冠感染病例数和空间相互作用指数被用作代理数据,以识别纳米比亚不同的新冠疫情风险区域。使用新冠疫情医院影响模型(CHIME)V1.1.5工具对实施流动限制后的未来新冠疫情应对情况进行建模。利用层次分析法(AHP)工具为每个专题图层和专题图层类别分配权重。通过适当的ArcGIS叠加分析生成风险区域。研究发现,当前的新冠感染病例数和空间流动指数是纳米比亚风险分区的主导和敏感因素。研究区域内确定了六个不同的新冠疫情风险区域,即最高、较高、高、低、较低和最低。建模结果显示,国内流动减少30%对控制新冠疫情传播有显著效果:病例峰值数量趋于平缓且峰值出现延迟。研究成果可帮助政策制定者估算所需即时资源数量,并规划纳米比亚未来的新冠疫情干预措施,特别是评估流动限制的潜在积极影响。