Geospatial modelling on the spread and dynamics of 154 day outbreak of the novel coronavirus (COVID-19) pandemic in Bangladesh towards vulnerability zoning and management approaches
Geospatial modelling on the spread and dynamics of 154 day outbreak of the novel coronavirus (COVID-19) pandemic in Bangladesh towards vulnerability zoning and management approaches
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DOI：
10.1007/s40808-020-00962-z
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发表时间：
2020-09-09
影响因子：
3
通讯作者：
Islam, Md. Nazrul
中科院分区：
文献类型：
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作者：
Rahman, Md. Rejaur;Islam, A. H. M. Hedayutul;Islam, Md. Nazrul
新型COVID-19是一种全球传播的大流行病，受到全球关注。由于目前尚无有效药物，为最大限度地减少和控制COVID-19的传播，全球均采用非药物干预措施（NPI）。然而，为了通过有效的管理战略和规划实施必要的非传染性疾病，关于大流行病的性质、规模、传播模式、热点、潜在风险因素、脆弱性和风险水平的时空信息是重要的。因此，本研究试图利用154天实时流行病学数据系列，深入评估和分析孟加拉国COVID-19疫情和时空传播动态。分析了区级数据，以便利用地理信息系统进行地理空间分析和建模。热点分析采用Getis-Ord Gi* 统计，另一方面，基于层次分析法的加权和法（AHP-WSM）用于COVID-19脆弱性分区建模。在孟加拉国，COVID-19疫情仍处于暴露水平。疾病传播率高（20.37%），病例倍增时间为11天（研究期间的最后一周）。病死率较低（1.3%），治愈率约为57.50%。地理空间分析显示，该疾病从中部地区传播，达卡是暴露最严重的地区，其次是查托格拉姆、纳拉扬甘吉、库米拉和博格拉。中部的单一强聚集模式主要向东南部扩散，被确定为病例和死亡分布的主要热点。此外，还查明了疾病传播与加速疾病传播的选定因素之间的潜在联系。中部、东部和东南部为高度脆弱区，西部、西南部、西北部和东北部为中度脆弱区。脆弱分区工作使得有可能确定需要通过适当管理和行动计划紧急干预的不同程度的脆弱地区，因此，预计将制定综合管理战略。因此，本研究将为了解COVID-19的时空调查和脆弱区域划分提供有用的指导，并有助于制定有效的管理行动计划，以减少和控制疾病传播和影响。通过适当调整一些具有地方相关性的因素，本文得出的COVID-19脆弱性分区可适用于其他地区，一般可用于任何其他传染病。该方法适用于区域范围，但决定因素的大规模数据的可用性也可以应用于小区域，因此可以制定管理策略。
The novel COVID-19 is a worldwide transmitted pandemic and has received global attention. Since there is no effective medication yet, to minimize and control the transmission of the COVID-19, non-pharmaceutical interventions (NPIs) are followed globally. However, for the implementation of needful NPIs through effective management strategies and planning, space-time-based information on the nature, magnitude, pattern of transmission, hotspots, the potential risk factors, vulnerability, and risk level of the pandemic are important. Hence, this study was an attempt to in-depth assess and analyze the COVID-19 outbreak and transmission dynamics through space and time in Bangladesh using 154 day real-time epidemiological data series. District-level data were analyzed for the geospatial analysis and modelling using GIS. Getis-Ord Gi* statistics was applied for the hotspot analysis, and on the other hand, the analytical hierarchy process-based weighted sum method (AHP-WSM) was used for the modelling of vulnerability zoning of COVID-19. In Bangladesh, the status of the pandemic COVID-19 still is in exposure level. Disease transmitted at a high rate (20.37%), and doubling time of the cases were 11 days (latest week of the study period). The fatality rate was comparatively low (1.3%), and the recovery rate was about 57.50%. Geospatial analysis exhibitsthe disease propagates from the central parts, and Dhaka was the most exposed district followed by Chattogram, Narayanganj, Cumilla, and Bogra. A single strong clustering pattern in the central part, which spread out mainly to the south-eastern part, was identified as a prime hotspot in both the cases and deaths distributions. Additionally, potential linkages between the transmission of disease and the selected factors that gear up the spreading of the disease were identified. The central, eastern, and south-eastern parts were recognized as high vulnerable zone, and conversely, the western, south-western, north-western, and north-eastern parts as medium vulnerable zone. The vulnerable zoning exercise made it possible to identify vulnerable areas with the different magnitude that require urgent intervention through proper management and action plan, and accordingly, comprehensive management strategies were anticipated. Thus, this study will be a useful guide towards understanding the space-time-based investigations and vulnerable area delineation of the COVID-19 and assist to formulate an effective management action plan to reduce and control the disease propagation and impacts. By appropriate adjustment of some factors with local relevance, COVID-19 vulnerability zoning derived here can be applied to other regions, and generally can be used for any other infectious disease. This method was applied at a regional scale, but the availability of larger scale data of the determining factors could be applied in small areas too, and accordingly, management strategies can be formulated.