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.

翻译：社会互动的时间动态被显示影响疾病的传播。 在这里, 我们模拟几种病毒菌株的进化和竞争条件, 探索各种水平的跨时间网络的交叉免疫性。 我们使用互动驱动的传染模式, 并使用它来描述几种病毒变异。 我们通过时间随机网络和现实世界互动数据获得的结果, 表明时间动态对于确定竞争结果至关重要。 我们考虑的是两种和三种相互竞争的病原体, 并显示一种较慢的病原体在何种条件下会继续活跃, 并造成第二波感染大多数人口。 然后我们表明, 当考虑这些相遇的期间时, 传播的动态会发生重大变化。 我们的结果显示, 当考虑空气传播的疾病时, 考虑时间会议的时间长度来模拟病原体在人口中的传播可能是至关重要的。