A Network-Based Epidemic Simulation Model for Assessing Non-pharmaceutical Interventions

Epidemic Spread and Control Strategies: A Spatial-individual Agent-based Modeling and Optimization Approach.

A spatial-individual agent-based model is developed that integrates fine-grained spatiotemporal dynamics, where transmission risk is quantified by the exact distance and duration of contact and provides data-driven guidance for optimizing public health policies on campuses.

Imperfect vaccination and coupled epidemic dynamics in age-structured multiplex networks.

Vaccination behavior is closely coupled with disease transmission through individual risk perception and age-dependent contact patterns. However, the combined effects of imperfect vaccine protection and age-specific mixing on behavior-epidemic dynamics remain insufficiently characterized. We develop a network-based dynamical model that couples susceptible-infected-recovered-vaccinated (SIRV) transmission with an evolutionary vaccination game. Empirical age-specific contact matrices are used to represent interactions among age groups and to examine how contact heterogeneity shapes vaccination behavior and disease transmission. Approximate theoretical analysis and stochastic simulations indicate that vaccination uptake depends on the balance between individual protection and population-level feedback. When vaccine efficacy is high, individual incentives are stronger and promote higher vaccination coverage. When efficacy is low, population-level feedback becomes more important for limiting free riding. Age-structured contacts further amplify inter-group differences, with highly connected age groups showing higher vaccination uptake and a stronger effect on epidemic outcomes. These results identify age-specific contact patterns as a key structural factor in behavior-transmission coupling. The proposed framework provides a basis for studying immunization strategies in structured populations.

Mobility and Contact Networks Shape Epidemic Outcomes: A Large-Scale Agent-Based Modeling Study

Human mobility plays a central role in shaping contact patterns that drive infectious disease transmission, yet mobility is often simplified in agent-based models (ABMs) due to data and computational constraints. The effects of these simplifications on model outputs are poorly understood. In this study, we systematically examined how alternative mobility assumptions influence emergent contact networks and epidemic dynamics within a large-scale ABM. Using a synthetic population of one million agents representing an urban environment, we implemented five mobility models varying along two dimensions: activity patterns (empirically derived vs. randomized) and destination choice mechanisms (empirical popularity, distance-based, or random). Holding disease parameters constant, we found that mobility assumptions alone produced substantially different contact network structures and epidemic trajectories, including differences in peak incidence and shifts in outbreak timing. Importantly, these differences could not be attributed to agents simply moving more or less overall since aggregate movement volumes were broadly comparable across models. Instead, the contrasting dynamics arose from how mobility generates contact opportunities: specifically, who meets whom, where, and how often. These results suggest that in models where mobility has not been carefully calibrated, simulated epidemic outcomes and evaluations of interventions may reflect mobility assumptions as much as underlying disease parameters. Our findings underscore the importance of mobility model calibration and validation, particularly in policy-facing applications.

Applied Mathematical Modelling

Multi-scale local network structure critically impacts epidemic spread and interventions

Network epidemic simulation enables fine-grained understanding of epidemic behavior. However, empirical samples of interaction networks display properties that are challenging to capture with popular synthetic models of networks. Our empirical results show that epidemic spread behavior is sensitive to a form of multi-scale local structure that is absent in common baseline models, (e.g., Erdős–Rényi, Chung-Lu, etc). This structure critically impacts the effect of local quarantining and stops epidemic spread in samples of interaction networks, even when it cannot be halted in simple synthetic models of those networks. Insights from our analysis include how epidemics on networks with widespread multi-scale local structure are easier to mitigate, as well as characterizing which nodes are ultimately not likely to be infected. We demonstrate that this structure results from more than just local triangle structure in the network, and we illustrate processes based on homophily or social influence and random walks that suggest how this multi-scale local structure arises and use it to cleanly isolate intervention sensitivity to multi-scale local structure.

Evaluating the Impact of Epidemic Control via State-Dependent Markovian Switching Modeling

We develop an exact finite-population stochastic framework for SIR epidemics evolving under Markovian switching between intervention regimes. The epidemic state is augmented by a finite phase component, allowing transmission, recovery, and direct immunity-acquisition rates to depend on the active regime. Phase-transition intensities may depend on the current epidemic state, so that policy escalation can react to the number of infectious individuals. Exploiting the monotonicity of the susceptible compartment, we derive level-wise recursions for the joint Laplace--Stieltjes transform and probability generating function of the extinction time and the number of infections generated before extinction. These recursions yield the infection-count distribution, conditional extinction-time transforms, and mixed moments linking epidemic duration and infection burden, while replacing a large global linear system with small phase-level solves. The framework is illustrated using weekly mpox incidence data from Luxembourg. A baseline one-phase SIR model is calibrated by maximum likelihood under a Poisson observation model. The calibrated baseline is then used for conditional comparisons of fixed control regimes, early versus delayed strict intervention, vaccination-supported control, and state-dependent escalation. The results show how switching mechanisms affect both the total number of infected individuals and the extinction time, including their dispersion. Since the switching mechanisms are specified rather than estimated from the intervention history, the results are conditional model-based comparisons rather than estimates of the historical effects of interventions in Luxembourg.