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

TL;DR

This paper presents a large-scale, actor-based model of respiratory pathogen transmission using detailed Dutch demographic data, highlighting how geographic and behavioral factors influence epidemic spread and intervention effectiveness.

Contribution

It introduces a novel dynamic contact network model that incorporates detailed demographic, geographic, and behavioral data for epidemic simulation.

Findings

Densely populated areas act as key hubs for transmission.

Interventions like self-isolation and travel restrictions can significantly alter epidemic trajectories.

The model emphasizes the importance of fine-scale contact realism in epidemic forecasting.

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.