Co-evolution of social networks and infectious diseases

Abstract

Since the beginning of the COVID-19 pandemic in early 2020 until the time of finishing this dissertation in July 2022, the World Health Organization has registered almost 560 Million cases and well over 6 Million deaths from COVID-19. Although a number of effective vaccines have been developed and large-scale vaccination campaigns have been implemented globally, only 58 of 194 WHO member states have met the 70\% vaccine coverage goal. Thus, mathematical models of disease spread remain one of the most important tools to understand the spread of such dynamics and can thus support public health policymaking. Network models of disease spread, the type of model addressed in this dissertation, have the advantage of accounting for social structure. However, they typically neglect the changes in our social relationships, despite the evidence that people avoid public places or infectious others during times of increased risk of infection. This dissertation presents, to our knowledge, the first model for the co-evolution of social networks and infectious diseases considering theory from social sciences, health psychology and epidemiology. The Networking during Infectious Diseases Model (NIDM) describes networking decisions as a trade-off between the benefits, costs, and potential harms of infections that a social relation creates. Agent-based simulations suggest that disease avoidance may reduce infection numbers significantly, depending on risk perceptions and network structure. A large-scale networking experiment, however, suggests that when disease avoidance is prioritized over maintaining social relationships, the cost might be a loss of social cohesion.

The effect of heterogeneous distributions of social norms on the spread of infectious diseases

“…Unfortunately, most existing data either allows for subgroup comparisons in a single time point [46] or allows for a comparison of the evolution of the pandemic and social norm compliance over time within a single population [16], thus limiting the possibilities of a calibration exercise. In the absence of another epidemic/pandemic, one could also test this model experimentally by assigning to participants a random infectivity risk and letting them interact in a SIRS environment were they have to choose between complying with costly preventive social norms or violating these norms and thereby increasing their payoff but also their risk of infection (see [47] for an example of an experimental test of a SIRS model with endogenous network formation).…”