Jain Kirti, Bhatnagar Vasudha, Kaur Sharanjit
Department of Computer Science, University of Delhi, Delhi, 110007 India.
Acharya Narendra Dev College, University of Delhi, Delhi, 110019 India.
Netw Model Anal Health Inform Bioinform. 2023;12(1):14. doi: 10.1007/s13721-022-00402-1. Epub 2023 Jan 10.
Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in and spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables.
基于网络的模型因其内在能力，能够对当代人类高度连通世界中互动的异质性进行建模，所以适合用于理解疫情动态。我们提出了一个框架，通过在地理区域中融入人口统计信息，创建一个模拟该区域人口社会接触网络的线框模型。该框架生成了一个具有小世界拓扑结构的模块化网络，它能够适应密度变化，并模拟二维和三维空间中的人际互动。当加载了塑造人类连通模式的适当经济、社会和城市数据时，这个网络就成为城市规划者、人口统计学家和社会科学家的有力决策工具。我们使用合成网络在可控环境中进行实验，并使用SEIR模型的一个变体来研究分区、密度变化和人口流动对疫情变量的影响。我们的结果表明，这些人口因素对社会接触模式具有显著影响，表现为不同的疫情动态。随后，我们通过利用现有普查数据创建相应的替代社会接触网络，对印度三个邦进行了新冠疫情的实际案例研究。该案例研究证实，融入人口统计信息的模块化接触网络能够减少疫情变量估计中的误差。
