We explore a standard epidemiological model, known as the SIRD model, to study the COVID-19 infection in India, and a few other countries around the world. We use (a) the stable cumulative infection of various countries and (b) the number of infection versus the tests carried out to evaluate the model. The time-dependent infection rate is set in the model to obtain the best fit with the available data. The model is simulated aiming to project the probable features of the infection in India, various Indian states, and other countries. India imposed an early lockdown to contain the infection that can be treated by its healthcare system. We find that with the current infection rate and containment measures, the total active infection in India would be maximum at the end of June or beginning of July 2020. With proper containment measures in the infected zones and social distancing, the infection is expected to fall considerably from August. If the containment measures are relaxed before the arrival of the peak infection, more people from the susceptible population will fall sick as the infection is expected to see a threefold rise at the peak. If the relaxation is given a month after the peak infection, a second peak with a moderate infection will follow. However, a gradual relaxation of the lockdown started well ahead of the peak infection, leads to a nearly twofold increase of the peak infection with no second peak. The model is further extended to incorporate the infection arising from the population showing no symptoms. The preliminary finding suggests that random testing needs to be carried out within the asymptomatic population to contain the spread of the disease. Our model provides a semi-quantitative overview of the progression of COVID-19 in India, with model projections reasonably replicating the current progress. The projection of the model is highly sensitive to the choice of the parameters and the available data.

我们探索了一种称为 SIRD 模型的标准流行病学模型，以研究印度和世界其他一些国家的 COVID-19 感染情况。我们使用（a）各个国家的稳定累积感染和（b）感染数量与进行的测试来评估模型。模型中设置了时间相关的感染率，以获得与可用数据的最佳拟合。该模型是模拟的，旨在预测印度、印度各邦和其他国家感染的可能特征。印度实施了早期封锁，以遏制其医疗系统可以治疗的感染。我们发现，按照目前的感染率和遏制措施，印度的总活跃感染将在 2020 年 6 月底或 7 月初达到最大值。通过在感染区采取适当的遏制措施并保持社交距离，预计感染率将从 8 月开始大幅下降。如果在感染高峰到来之前放松防控措施，预计感染高峰期将有更多易感人群患病。如果在感染高峰后一个月给予放松，那么将出现第二个中度感染高峰。然而，在感染高峰之前就开始逐步放松封锁，导致感染高峰增加了近两倍，没有第二个高峰。该模型被进一步扩展以包含由没有症状的人群引起的感染。初步发现表明，需要在无症状人群中进行随机检测，以遏制疾病的传播。我们的模型提供了印度 COVID-19 进展的半定量概述，模型预测合理地复制了当前进展。模型的投影对参数的选择和可用数据高度敏感。