The first mitigation response to the Covid-19 pandemic was to limit person-to-person interaction as much as possible. This was implemented by the temporary closing of many workplaces and people were required to follow social distancing. Networks are a great way to represent interactions among people and the temporary severing of these interactions. Here, we present a network model of human–human interactions that could be mediators of disease spread. The nodes of this network are individuals and different types of edges denote family cliques, workplace interactions, interactions arising from essential needs, and social interactions. Each individual can be in one of four states: susceptible, infected, immune, and dead. The network and the disease parameters are informed by the existing literature on Covid-19. Using this model, we simulate the spread of an infectious disease in the presence of various mitigation scenarios. For example, lockdown is implemented by deleting edges that denote non-essential interactions. We validate the simulation results with the real data by matching the basic and effective reproduction numbers during different phases of the spread. We also simulate different possibilities of the slow lifting of the lockdown by varying the transmission rate as facilities are slowly opened but people follow prevention measures like wearing masks etc. We make predictions on the probability and intensity of a second wave of infection in each of these scenarios.

针对 Covid-19 大流行的第一个缓解措施是尽可能限制人与人之间的互动。这是通过暂时关闭许多工作场所来实现的，并且要求人们保持社交距离。网络是表示人与人之间的互动以及暂时切断这些互动的好方法。在这里，我们提出了一个人与人互动的网络模型，它可能是疾病传播的媒介。该网络的节点是个人，不同类型的边表示家庭派系、工作场所互动、因基本需求而产生的互动以及社会互动。每个人都可能处于四种状态之一：易感、感染、免疫和死亡。该网络和疾病参数由有关 Covid-19 的现有文献提供。使用这个模型，我们模拟了在各种缓解场景下传染病的传播。例如，锁定是通过删除表示非必要交互的边来实现的。我们通过匹配传播不同阶段的基本繁殖数和有效繁殖数，用真实数据验证了模拟结果。我们还模拟了缓慢解除封锁的不同可能性，通过改变传播率，因为设施慢慢开放，但人们遵循戴口罩等预防措施。我们对每个方面第二波感染的概率和强度进行预测场景。