{"title":"A GPS Data-Based Index to Determine the Level of Adherence to COVID-19 Lockdown Policies in India.","authors":"Harish Puppala, Amarnath Bheemaraju, Rishi Asthana","doi":"10.1007/s41666-020-00086-0","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9000,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-020-00086-0","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Healthcare Informatics Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s41666-020-00086-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/6/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 1
Abstract
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.
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