The temporal dynamics of social interactions were shown to influence the spread of disease. Here, we model the conditions of progression and competition for several viral strains, exploring various levels of cross-immunity over temporal networks. We use our interaction-driven contagion model and characterize, using it, several viral variants. Our results, obtained on temporal random networks and on real-world interaction data, demonstrate that temporal dynamics are crucial to determining the competition results. We consider two and three competing pathogens and show the conditions under which a slower pathogen will remain active and create a second wave infecting most of the population. We then show that when the duration of the encounters is considered, the spreading dynamics change significantly. Our results indicate that when considering airborne diseases, it might be crucial to consider the duration of temporal meetings to model the spread of pathogens in a population.
Viral transmission in pedestrian crowds: Coupling an open-source code assessing the risks of airborne contagion with diverse pedestrian dynamics models
Viral mutation spread controlled by inter-host network dynamics
Galician Center for Mathematical Research and Technology (CITMAga)（加利西亚数学研究与技术中心）；Group of Nonlinear Physics, Universidade de Santiago de Compostela（圣地亚哥-德孔波斯特拉大学非线性物理研究组）；Department of Applied Mathematics, Universidade de Santiago de Compostela（圣地亚哥-德孔波斯特拉大学应用数学系）
Reducing Recurrent Competitive Epidemics via Dynamic Resource Allocation
Centre Borelli, ENS Paris-Saclay, CNRS（博雷利中心，巴黎萨克雷高等师范学院，法国国家科学研究中心）；School of Computer Science and Applied Mathematics, University of the Witwatersrand（威特沃特斯兰德大学计算机科学与应用数学学院）
