Huang He, Chen Yahong, Yan Zhijun
School of Economics and Management, China University of Geosciences (Beijing), Beijing 100083, China.
School of Information, Beijing Wuzi University, Beijing 101149, China.
Appl Math Comput. 2021 Jun 1;398:125983. doi: 10.1016/j.amc.2021.125983. Epub 2021 Jan 17.
Social distancing can be divided into two categories: spontaneous social distancing adopted by the individuals themselves, and public social distancing promoted by the government. Both types of social distancing have been proved to suppress the spread of infectious disease effectively. While previous studies examined the impact of each social distancing separately, the simultaneous impacts of them are less studied. In this research, we develop a mathematical model to analyze how spontaneous social distancing and public social distancing simultaneously affect the outbreak threshold of an infectious disease with asymptomatic infection. A communication-contact two-layer network is constructed to consider the difference between spontaneous social distancing and public social distancing. Based on link overlap of the two layers, the two-layer network is divided into three subnetworks: communication-only network, contact-only network, and overlapped network. Our results show that public social distancing can significantly increase the outbreak threshold of an infectious disease. To achieve better control effect, the subnetwork of higher infection risk should be more targeted by public social distancing, but the subnetworks of lower infection risk shouldn't be overlooked. The impact of spontaneous social distancing is relatively weak. On the one hand, spontaneous social distancing in the communication-only network has no impact on the outbreak threshold of the infectious disease. On the other hand, the impact of spontaneous social distancing in the overlapped network is highly dependent on the detection of asymptomatic infection sources. Moreover, public social distancing collaborates with infection detection on controlling an infectious disease, but their impacts can't add up perfectly. Besides, public social distancing is slightly less effective than infection detection, because infection detection can also promote spontaneous social distancing.
个人自行采取的自发社交距离，以及政府推动的公共社交距离。这两种社交距离均已被证明能有效抑制传染病传播。以往研究分别考察了每种社交距离的影响，但对它们的同时影响研究较少。在本研究中，我们建立了一个数学模型，以分析自发社交距离和公共社交距离如何同时影响无症状感染传染病的爆发阈值。构建了一个通信 - 接触两层网络，以考虑自发社交距离和公共社交距离之间的差异。基于两层的链接重叠，将两层网络划分为三个子网：仅通信网络、仅接触网络和重叠网络。我们的结果表明，公共社交距离可显著提高传染病的爆发阈值。为实现更好的控制效果，公共社交距离应更有针对性地针对感染风险较高的子网，但感染风险较低的子网也不应被忽视。自发社交距离的影响相对较弱。一方面，仅通信网络中的自发社交距离对传染病的爆发阈值没有影响。另一方面，重叠网络中自发社交距离的影响高度依赖于无症状感染源的检测。此外，公共社交距离与感染检测在控制传染病方面相互协作，但其影响并非完全叠加。此外，公共社交距离的效果略低于感染检测，因为感染检测还能促进自发社交距离。
