Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective
Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective
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DOI：
10.1007/s10668-020-00849-0
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发表时间：
2020-07-08
影响因子：
4.9
通讯作者：
Kumar, Rakesh
中科院分区：
文献类型：
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作者：
Bherwani, Hemant;Anjum, Saima;Kumar, Rakesh
COVID-19起源于中国武汉，正在全球迅速蔓延。据报道，与其前身相比，这种称为严重急性呼吸道综合征冠状病毒2（SARS-CoV-2）的新型冠状病毒株的传播率很高。正在实施药物和疫苗临床试验、社会距离、使用个人防护设备等方面的主要战略，以控制传播。目前的研究集中在印度政府采取的封锁和社交距离政策，并使用贝叶斯概率模型（BPM）评估其有效性。通过上述方法进行的变点分析（CPA）表明，在病例呈指数级上升之前实施封锁的州能够以更好、更有效的方式控制疾病的传播。对作为联邦领土的马哈拉施特拉邦、古吉拉特邦、中央邦、拉贾斯坦邦、泰米尔纳德邦、西孟加拉、北方邦和德里等邦进行了分析。据报告，古吉拉特邦和中央邦的Delta值最高，为9.6周，而最低值为4.7周，显然是受影响最严重的马哈拉施特拉邦。所有的状态都指示Delta的显著相关性（p < 0.05，tstat > tcritical），即，CPA和封锁时间与单位人口病例数（CPP）和单位面积病例数（CPUA）的差异，而delta与单位人口病例数密度（CPD）呈弱相关（p < 0.1和tstat < tcritical）。对于CPP和CPUA两者，tstat > tcritical指示显著相关性，而Pearson相关性指示负方向。根据业务流程管理的输入，从地理信息系统的Voronoi方法研究了确定高风险地区方面的进一步分析。所有州都遵循上述高人口、高病例情景的模式，风险区的边界可以通过在其中构建的泰森多边形（TP）来识别。该研究的结果有助于为印度制定战略和政策驱动的应对措施，以应对COVID-19大流行。
Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of Delta (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p < 0.05, tstat > tcritical) for Delta, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p < 0.1 and tstat < tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat > tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.