Barat Souvik, Parchure Ritu, Darak Shrinivas, Kulkarni Vinay, Paranjape Aditya, Gajrani Monika, Yadav Abhishek, Kulkarni Vinay
Tata Consultancy Services Research, Pune, India.
Prayas Health Group, Pune, India.
Trans Indian Natl Acad Eng. 2021;6(2):323-353. doi: 10.1007/s41403-020-00197-5. Epub 2021 Jan 29.
The COVID-19 epidemic created, at the time of writing the paper, highly unusual and uncertain socio-economic conditions. The world economy was severely impacted and business-as-usual activities severely disrupted. The situation presented the necessity to make a trade-off between individual health and safety on one hand and socio-economic progress on the other. Based on the current understanding of the epidemiological characteristics of COVID-19, a broad set of control measures has emerged along dimensions such as restricting people's movements, high-volume testing, contract tracing, use of face masks, and enforcement of social-distancing. However, these interventions have their own limitations and varying level of efficacy depending on factors such as the population density and the socio-economic characteristics of the area. To help tailor the intervention, we develop a configurable, fine-grained agent-based simulation model that serves as a virtual representation, i.e., a digital twin of a diverse and heterogeneous area such as a city. In this paper, to illustrate our techniques, we focus our attention on the Indian city of Pune in the western state of Maharashtra. We use the digital twin to simulate various what-if scenarios of interest to (1) predict the spread of the virus; (2) understand the effectiveness of candidate interventions; and (3) predict the consequences of introduction of interventions possibly leading to trade-offs between public health, citizen comfort, and economy. Our model is configured for the specific city of interest and used as an in-silico experimentation aid to predict the trajectory of active infections, mortality rate, load on hospital, and quarantine facility centers for the candidate interventions. The key contributions of this paper are: (1) a novel agent-based model that seamlessly captures people, place, and movement characteristics of the city, COVID-19 virus characteristics, and primitive set of candidate interventions, and (2) a simulation-driven approach to determine the exact intervention that needs to be applied under a given set of circumstances. Although the analysis presented in the paper is highly specific to COVID-19, our tools are generic enough to serve as a template for modeling the impact of future pandemics and formulating bespoke intervention strategies.
在撰写本文时，新冠疫情造成了极为特殊且不确定的社会经济状况。世界经济受到严重冲击，正常商业活动遭到严重扰乱。这种情况使得有必要在个人健康与安全和社会经济发展之间进行权衡。基于目前对新冠病毒流行病学特征的了解，出现了一系列广泛的控制措施，包括限制人员流动、大规模检测、接触者追踪、使用口罩以及实施社交距离等。然而，这些干预措施有其自身的局限性，且效果因地区人口密度和社会经济特征等因素而异。为了帮助定制干预措施，我们开发了一个可配置的、细粒度的基于智能体的模拟模型，该模型可作为一个虚拟表征，即一个城市等多样化和异质性区域的数字孪生模型。在本文中，为了说明我们的技术，我们将注意力集中在印度西部马哈拉施特拉邦的浦那市。我们使用数字孪生模型来模拟各种感兴趣的假设情景，以（1）预测病毒传播；（2）了解候选干预措施的有效性；（3）预测干预措施实施可能导致的公共卫生、公民舒适度和经济之间权衡的后果。我们的模型针对特定感兴趣的城市进行配置，并用作计算机模拟实验辅助工具，以预测候选干预措施下的活跃感染轨迹、死亡率、医院负荷以及隔离设施中心情况。本文的主要贡献在于：（1）一种新颖的基于智能体的模型，该模型能够无缝捕捉城市的人员、地点和移动特征、新冠病毒特征以及一组基本的候选干预措施；（2）一种模拟驱动的方法，用于确定在给定情况下需要应用的确切干预措施。尽管本文所呈现的分析高度特定于新冠疫情，但我们的工具具有足够的通用性，可作为模拟未来大流行影响和制定定制干预策略的模板。
