Controlling the Outbreak of COVID-19: A Noncooperative Game Perspective
Anupam Kumar Bairagi, Mehedi Masud, Do Hyeon Kim, Md. Shirajum Munir, Abdullah Al Nahid, Sarder Fakhrul Abedin, Kazi Masudul Alam, Sujit Biswas, Sultan S Alsham… 展开作者
Anupam Kumar Bairagi, Mehedi Masud, Do Hyeon Kim, Md. Shirajum Munir, Abdullah Al Nahid, Sarder Fakhrul Abedin, Kazi Masudul Alam, Sujit Biswas, Sultan S Alshamrani, Zhu Han, Choong Seon Hong
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Accepted article by IEEE Access. DOI: 10.1109/ACCESS.2020.3040821
COVID-19 is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective preventive measures to control this pandemic. Therefore, in this paper, an optimization problem is formulated that accommodates both isolation and social distancing features of the individuals. To promote social distancing, we solve the formulated problem by applying a noncooperative game that can provide an incentive for maintaining social distancing to prevent the spread of COVID-19. Furthermore, the sustainability of the lockdown policy is interpreted with the help of our proposed game-theoretic incentive model for maintaining social distancing where there exists a Nash equilibrium. Finally, we perform an extensive numerical analysis that shows the effectiveness of the proposed approach in terms of achieving the desired social-distancing to prevent the outbreak of the COVID-19 in a noncooperative environment. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation from 25% to 100% for all considered scenarios. The numerical results also demonstrate that in a particular percentage of home isolation, the individual incentive decreases with an increasing number of individuals.
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数据分析和可视化对于探索和交流医学研究中的发现至关重要，特别是在流行病学监测中。来自众包网站COVID-19 India Tracker、2011年人口普查和Google Mobility报告的COVID-19诊断病例和死亡率数据已用于开发针对印度COVID-19疫情的实时的【形】分析和监测系统。我们开发了一个仪表板应用程序，用于数据可视化和分析几个指标，以使用数据科学技术跟踪SARS-CoV-2流行。以互动和方便用户的方式，对地区一级的基本流行病监测工具进行了估计，其中包括时间趋势、流行病曲线、增长率、倍增时间和有效繁殖数等关键流行病参数。这表明了数据科学方法和流行病学技术在公共卫生决策中的应用，同时解决了及时可靠的决策辅助工具的差距。
