Quantitative analysis of the transmission dynamics of an adenovirus outbreak in a physical training school based on a dynamic social network (Preprint)

BACKGROUND Background: As a respiratory infectious disease that demands particular attention and prevention in places with dense crowds, the adenovirus requires epidemiological research to investigate its transmission mechanisms and the effectiveness of intervention measures. In this study, we conducted a theoretical epidemiological analysis based on a real-life adenovirus outbreak at a physical training school. OBJECTIVE none METHODS Methods: We first constructed a time-varying, multilevel social contact network that included four types of individuals: trainees, class monitors, team leaders, and company officers. The topological structure of this network was designed to align with the statistical characteristics of field survey data. Secondly, an individual-based dynamic model was employed to simulate the transmission of adenovirus type 7 within this social contact network. Then, the model's predicted outcomes were fitted to real incidence data to calibrate the model and estimate the parameters. Finally, sensitivity analysis was conducted to verify the reliability of the model. RESULTS Results: Trainees, as the largest group, played a significant role in the adenovirus transmission. Isolation was implemented on Day 14 of the outbreak after the index case, and daily new cases exhibited a fluctuating decrease after a brief growth spurt. Early case isolation and reduced inter-company personnel contact effectively lowered the overall infection rate. In the early stage of the outbreak, the effective reproduction number rapidly increased to over 3, then significantly decreased after Day 9 and stabilized between 1 and 2. Case isolation quickly reduced it to below 1, thereby effectively controlling the outbreak. CONCLUSIONS Conclusions: Interpersonal contact, intervention measures, and epidemic transmission form a complex system with mutual influence. Simulating individual behaviors as the core modeling approach accurately captured the dynamics of the epidemic. Results show that early warning, case isolation, and social distancing can effectively control the epidemic. CLINICALTRIAL none