Robust testing and tracing are key to fighting the menace of coronavirus disease 2019 (COVID-19). This outbreak has progressed with tremendous impact on human life, society and economy. In this paper, we propose an age-structured SIQR model to track the progression of the pandemic in India, Italy and USA, taking into account the different age structures of these countries. We have made predictions about the disease dynamics, identified the most infected age groups and analysed the effectiveness of social distancing measures taken in the early stages of infection. The basic reproductive ratio \({\mathfrak {R}}_0\) has been numerically calculated for each country. We propose a strategy of age-targeted testing, with increased testing in the most proportionally infected age groups. We observe a marked flattening of the infection curve upon simulating increased testing in the 15–40 year age groups in India. Thus, we conclude that social distancing and widespread testing are effective methods of control, with emphasis on testing and identifying the hot spots of highly infected populations. It has also been suggested that a complete lockdown, followed by lockdowns in selected regions, is more effective than the reverse.

强大的检测和追踪是对抗 2019 年冠状病毒病 (COVID-19) 威胁的关键。这次疫情的发展对人类生活、社会和经济产生了巨大影响。在本文中，考虑到这些国家不同的年龄结构，我们提出了一个年龄结构的 SIQR 模型来跟踪印度、意大利和美国大流行的进展。我们已经对疾病动态做出了预测，确定了受感染最多的年龄组，并分析了在感染早期采取的社会隔离措施的有效性。基本繁殖率\({\mathfrak {R}}_0\)已针对每个国家进行了数值计算。我们提出了一种针对年龄的检测策略，在感染比例最高的年龄组中增加检测。在模拟印度 15-40 岁年龄组增加检测后，我们观察到感染曲线明显变平。因此，我们得出结论，社交距离和广泛检测是有效的控制方法，重点是检测和识别高度感染人群的热点。也有人建议，完全封锁，然后在选定地区封锁，比反过来更有效。