Risk mapping novel respiratory pathogens with large-scale dynamic contact networks

Abstract Background Human-to-human transmission of pathogens fundamentally depends on interactions among infectious and susceptible individuals, yet traditional population-scale models often overlook the stochastic, behaviour-driven, and highly heterogeneous nature of these interactions. Methods Here, we develop a large-scale actor-based model capturing early epidemic dynamics of a novel respiratory pathogen on dynamic contact networks. We build these networks upon explicitly integrating detailed demographic and residential registry data from the Netherlands. The model simulates the Dutch population characterised by age, residency and mobility patterns, with actors interacting stochastically across households, workplaces and schools. Results We show how the geographic and demographic profiles of initial cases impact transmission trajectories, with densely populated municipalities in the country’s western core acting as key hubs driving epidemic spread. The framework enables rigorous assessment of intervention strategies incorporating behavioural adaptations. As case studies, we quantify the effects of symptomatic self-isolation and travel restrictions to and from major urban centres, highlighting their potential to modulate epidemic outcomes. Conclusions Our findings underscore the necessity of integrating fine-scale human-to-human contact realism and population scale in epidemic forecasting and control. Plain-language summary Mathematical modelling of infectious diseases is a cornerstone for understanding and predicting how pathogens spread in populations. Current models of disease spread, despite their widespread use, rely on one-size-fits-all assumptions that fail to capture the dynamic, and adaptive nature of real-world human interactions. Network models have the fine detail needed to represent these complexities, but face challenges in scalability and generalisability. Here, we introduce a novel hybrid model that combines the realism of network models with the adaptability of population-level models, enabling a more accurate overall analysis. Our framework advances epidemic modelling by bridging detailed interpersonal behaviour and large-scale generalisability.