论文标题
武汉，多伦多和意大利新兴Covid-19流行病的数据驱动网络模型
A Data-Driven Network Model for the Emerging COVID-19 Epidemics in Wuhan, Toronto and Italy
论文作者
论文摘要
2019年持续的冠状病毒病（Covid-19）大流行威胁着人类的健康，并造成了巨大的经济损失。预测性建模和预测流行趋势对于开发对抗这种大流行的对策至关重要。我们开发一个网络模型，每个节点代表一个个体，边缘表示感染可能传播的个体之间的接触。根据他们每天（其节点学位）和感染状态的接触数量进行分类。传输网络模型分别拟合到武汉（中国），多伦多（加拿大）和意大利共和国的Covid-19的报告数据中，使用马尔可夫链蒙特卡洛（MCMC）优化算法。我们的模型与狭窄的置信区间非常适合所有三个区域，并且可以适应其他大型或区域。关于遏制策略作用的模型预测可以帮助公共卫生当局计划控制措施。
The ongoing Coronavirus Disease 2019 (COVID-19) pandemic threatens the health of humans and causes great economic losses. Predictive modelling and forecasting the epidemic trends are essential for developing countermeasures to mitigate this pandemic. We develop a network model, where each node represents an individual and the edges represent contacts between individuals where the infection can spread. The individuals are classified based on the number of contacts they have each day (their node degrees) and their infection status. The transmission network model was respectively fitted to the reported data for the COVID-19 epidemic in Wuhan (China), Toronto (Canada), and the Italian Republic using a Markov Chain Monte Carlo (MCMC) optimization algorithm. Our model fits all three regions well with narrow confidence intervals and could be adapted to simulate other megacities or regions. The model projections on the role of containment strategies can help inform public health authorities to plan control measures.
