College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, China.
School of Journalism and Communication, Tsinghua University, Beijing, China.
PLoS One. 2020 Nov 24;15(11):e0242761. doi: 10.1371/journal.pone.0242761. eCollection 2020.
The Coronavirus Disease 2019 (COVID-19) has proved a globally prevalent outbreak since December 2019. As a focused country to alleviate the epidemic impact, China implemented a range of public health interventions to prevent the disease from further transmission, including the pandemic lockdown in Wuhan and other cities. This paper establishes China's mobility network by a flight dataset and proposes a model without epidemiological parameters to indicate the spread risks through the network, which is termed as epidemic strength. By simply adjusting an intervention parameter, traffic volumes under different travel-restriction levels can be simulated to analyze how the containment strategy can mitigate the virus dissemination through traffic. This approach is successfully applied to a network of Chinese provinces and the epidemic strength is smoothly interpreted by flow maps. Through this node-to-node interpretation of transmission risks, both overall and detailed epidemic hazards are properly analyzed, which can provide valuable intervention advice during public health emergencies.
自 2019 年 12 月以来,2019 年冠状病毒病(COVID-19)已在全球范围内广泛流行。中国作为一个重点国家,采取了一系列公共卫生干预措施来防止疾病进一步传播,包括对武汉和其他城市实施的封城措施。本文通过航班数据集建立了中国的流动网络,并提出了一种没有流行病学参数的模型来通过网络指示传播风险,即疫情强度。通过简单地调整干预参数,可以模拟不同旅行限制水平下的交通量,以分析控制策略如何通过交通来减轻病毒传播。该方法成功应用于中国各省的网络,通过流图可以顺利地解释疫情强度。通过这种节点到节点的传播风险解释,可以对整体和详细的疫情风险进行适当分析,为突发公共卫生事件期间提供有价值的干预建议。