Sophia Johanna Wagner

arXiv2026

Infectious behaviour: Simulating the effects of communication and social influence on pathogen transmission in crowds

Public health measures at mass gatherings work only if people follow them, and adherence depends both on how instructions are communicated and what others do. We link these behavioural factors to pathogen transmission in a local-scale, agent-based exposure model combining pedestrian dynamics with airborne transmission. Rather than fitting a parametric behavioural model, agents draw their adherence at run time from survey respondents (2,170 attendees of UK sports and music events) who reported the same context: we vary stewards' communication effectiveness and adherence of role models, as well as agents' shared social identity with each, assuming that strong shared social identity increases their influence. In a ticket-checkpoint queue of 101 agents, effective communication combined with strong shared social identity with stewards reduces the number of highly exposed agents by 82\% for mask wearing. In contrast, strong shared social identity with non-adhering role models gives almost nine times as many highly exposed agents as with adhering role models. Physical distancing was not monotonically beneficial, because adherence changes how agents move: an infectious agent that kept distance moved along the edge of the queue, leading to similar results across conditions. Our pipeline transfers to any survey that includes behavioural measures and contextual variables.

math.DS↗

arXiv2026

On the Effect of Missing Transmission Chain Information in Agent-Based Models: Outcomes of Superspreading Events and Workplace Transmission

Agent-based models (ABMs) have emerged as distinguished tools for epidemic modeling due to their ability to capture detailed human contact patterns and can, thus, support decision-makers in times of outbreaks and epidemics. However, as a result of missing correspondingly resolved data transmission events are often modeled based on simplified assumptions. In this article, we present a framework to assess the impact of these simplifications on epidemic prediction outcomes, considering superspreading and workplace transmission events. We couple the VADERE microsimulation model with the large-scale MEmilio-ABM and compare the outcomes of four outbreak events after 10 days of simulation in a synthetic city district generated from German census data. In a restaurant superspreading event, where up to four households share tables, we observe 17.2 % more infections on day 10 after the outbreak. The difference increases to 46.0 % more infections when using the simplified initialization in a setting where only two households share tables. We observe similar outcomes (41.3 % vs. 9.3 % more infections) for two workplace settings with different mixing patterns between teams at work. In addition to the aggregated difference, we show differences in spatial dynamics and transmission trees obtained with complete or reduced outbreak information. We observe differences between simplified and fully detailed initializations that become more pronounced when the subnetworks in the outbreak setting are mixing less. In consequence and aside from classical calibration of models, the significant outcome differences should drive us to develop a more profound understanding of how and where simplified assumptions about transmission events are adequate.

physics.soc-ph↗