Analysis of the competition among viral strains using a temporal interaction-driven contagion model

Abstract

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.

References in corpus (12)

• Epidemic processes in complex networks
• Temporal Networks
• The origin of bursts and heavy tails in human dynamics
• Measuring large-scale social networks with high resolution
• Coevolution spreading in complex networks
• Competing epidemics on complex networks
• Can co-location be used as a proxy for face-to-face contacts?
• Random graph models for dynamic networks
• Diffusion on networked systems is a question of time or structure
• Optimal Containment of Epidemics in Temporal and Adaptive Networks
• Two-pathogen model with competition on clustered networks
• Spread of variants of epidemic disease based on the microscopic numerical simulations on networks