基于GPS数据的指数,以确定印度对COVID-19封锁政策的遵守程度。

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2021-01-05 eCollection Date: 2021-06-01 DOI:10.1007/s41666-020-00086-0
Harish Puppala, Amarnath Bheemaraju, Rishi Asthana
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引用次数: 1

摘要

尽管实施了严格的封锁政策,但在过去几周,印度的COVID-19病例增长仍在迅速扩大。本研究引入了一种基于gps的工具,即锁定破坏指数(LBI),该工具有助于确定锁定期间的破坏活动程度。使用社区流动性报告对其进行评估。该指数的范围为0 ~ 100,表示遵守封锁政策的程度。0分表示平民严格遵守准则,100分表示完全违反准则。改进了理想解相似性排序偏好法(TOPSIS)来计算LBI。我们考虑了印度的15个州,冠状病毒的传播在这些州相对占主导地位。在封锁的第一阶段观察到明显的突破活动,在封锁的第三和第四阶段强度增加。比哈尔邦总体上占优势,LBI为75.28。与此同时,德里大多数人严格遵守了封锁政策,LBI得分为47.05,是最低的。尽管所有邦在第二阶段(L2.0)的封锁期间,与第一阶段(L1.0)相比,违规活动平均上升了3%,但德里和泰米尔纳德邦的违规活动呈下降趋势。自封锁L3.0的第三阶段开始以来,在分析所考虑的每个州都观察到违规活动显著增加。在L3.0和L4.0结束时,LBI相对于L1.0平均上升16.9%和27.6%。LBI与累计确诊病例的spearman秩正相关为0.88。这种相关性证明,违规行为可能是封锁期间新冠肺炎病例增加的原因之一。
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A GPS Data-Based Index to Determine the Level of Adherence to COVID-19 Lockdown Policies in India.

The growth of COVID-19 cases in India is scaling high over the past weeks despite stringent lockdown policies. This study introduces a GPS-based tool, i.e., lockdown breaching index (LBI), which helps to determine the extent of breaching activities during the lockdown period. It is evaluated using the community mobility reports. This index ranges between 0 and 100, which implies the extent of following the lockdown policies. A score of 0 indicates that civilians strictly adhered to the guidelines while a score of 100 points to complete violation. Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is modified to compute the LBI. We considered fifteen states of India, where the spread of coronavirus is relatively dominant. A significant breaching activity is observed during the first phase of lockdown, and the intensity increased in the third and fourth phases of lockdown. Overall breaching activities are dominant in Bihar with LBI of 75.28. At the same time, it is observed that the majority of the people in Delhi adhered to the lockdown policies strictly, as reflected with an LBI score of 47.05, which is the lowest. Though an average rise of 3% breaching activities during the second phase of lockdown (L2.0) with reference to the first phase of lockdown (L1.0) is noticed in all the states, a decreasing trend is noticed in Delhi and Tamil Nadu. Since the beginning of third phase of lockdown L3.0, a significant rise in breaching activities is observed in every state considered for the analysis. The average LBI rise of 16.9% and 27.6% relative to L1.0 is observed at the end of L3.0 and L4.0, respectively. A positive spearman rank correlation of 0.88 is noticed between LBI and the cumulative confirmed cases. This correlation serves as evidence and enlightens the fact that the breaching activities could be one of the possible reasons that contributed to the rise in COVID-19 cases throughout lockdown.

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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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