The pandemic caused by the novel Coronavirus SARS-CoV2 has been responsible for life threatening health complications, and extreme pressure on healthcare systems. While preventive and definite curative medical interventions are yet to arrive, Non-Pharmaceutical Interventions (NPIs) like physical isolation, quarantine and drastic social measures imposed by governing agencies are effective in arresting the spread of infections in a population. In densely populated countries like India, lockdown interventions are partially effective due to social and administrative complexities. Using detailed demographic data, we present an agent based model to imitate the behavior of the population and its mobility features, even under intervention. We demonstrate the effectiveness of contact tracing policies and how our model efficiently relates to empirical findings on testing efficiency. We also present various lockdown intervention strategies for mitigation - using the bare number of infections, the effective reproduction rate, as well as using reinforcement learning. Our analysis can help assess the socio-economic consequences of such interventions, and provide useful ideas and insights to policy makers for better decision making.

中文翻译：

---

封锁干预措施对控制印度 COVID-19 流行的影响

由新型冠状病毒 SARS-CoV2 引起的大流行已造成危及生命的健康并发症，并对医疗保健系统造成极大压力。虽然预防性和明确的治疗性医疗干预措施尚未出台，但管理机构实施的物理隔离、检疫和严厉的社会措施等非药物干预措施 (NPI) 可有效阻止感染在人群中的传播。在印度等人口稠密的国家，由于社会和行政复杂性，封锁干预措施部分有效。使用详细的人口统计数据，我们提出了一个基于代理的模型来模仿人口的行为及其流动性特征，即使在干预下也是如此。我们展示了联系人追踪政策的有效性，以及我们的模型如何有效地与测试效率的实证结果相关联。我们还提出了各种用于缓解的锁定干预策略——使用感染人数、有效繁殖率以及使用强化学习。我们的分析可以帮助评估此类干预措施的社会经济后果，并为决策者提供有用的想法和见解，以做出更好的决策。