TL;DR
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.
This study presents an agent-based model (ABM), implemented in NetLogo, for evaluating face-mask use and mobility restrictions in an isolated mining company town in the Brazilian Amazon. Unlike aggregate formulations, the model resolves individual agents, routine-based mobility, work shifts, household organization, and location-specific interactions within eight micro-environments. Across the configurations evaluated, the no-intervention scenario reached a peak of 793 active infections (approximately 40% of the modeled population), whereas full N95-mask adherence reduced the peak to 73 cases. The idealized hybrid scenario, combining full mask adherence and full mobility-restriction adherence, produced the lowest peak (48 cases), a growth rate of 3.19 infections/day, and a flattening duration of 30 days. Quantitative validation against the first epidemic wave in Porto Trombetas showed that the critical scenario most closely reproduced the empirical curve shape (r = 0.94; NRMSE = 22.2%), although it overestimated the empirical peak by 26.3%. Within the assumptions and intervention configurations examined, the hybrid strategy provided the strongest epidemic control; however, it should be interpreted as a full-compliance counterfactual rather than as a reconstruction of observed behavior. The proposed framework is intended for reproducible comparative scenario analysis and decision support in semi-closed populations, not for deterministic epidemic forecasting.
SW-SEIQR is presented, an individual-based epidemic model in which individuals are represented as nodes of a Watts–Strogatz small-world contact network, which indicates that highly effective risk-reduction measures, such as high-quality mask use, can substantially reduce disease transmission even when adopted by only a fraction of the population.
Understanding how individual protection and population density influence epidemic spreading remains a central challenge in epidemiology. While classical compartmental models successfully describe the temporal evolution of epidemics, they do not explicitly account for the microscopic motion and spatial organisation of individuals. Here, we investigate contagion dynamics using an agent-based model of Active Brownian Particles (ABPs), where self-propelled agents interact through local contact and a prescribed fraction of the population is protected. By systematically varying the protected-agent fraction and the population density, we identify two distinct contagion regimes separated by a crossover protection of approximately $30\%$. Below this threshold, the maximum contagion rate follows the universal scaling $\nu_{\mathrm{max}}\propto\phi^{1/2}$, indicating that disease transmission is governed primarily by frequency of encounters. Above the threshold, universal scaling is lost and protection becomes the dominant mechanism controlling epidemic spreading, with the strongest suppression occurring in low-density populations. These results demonstrate that microscopic active-matter models provide a powerful framework for investigating epidemic dynamics beyond the assumptions of well-mixed population models and reveal how spatial organisation and individual protection jointly determine contagion dynamics.
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.
The global spread of COVID-19 highlighted the necessity of control strategies that are not only epidemiologically effective but also economically viable. Designing such interventions requires balancing transmission reduction with associated implementation costs. Many existing mathematical models rely on continuous control mechanisms and do not explicitly connect cost-effectiveness analysis with observed epidemiological data. In this work, we first formulate a SEIAR compartmental model and subsequently extend it to a vaccination-incorporated SEIAVR framework. To capture the realistic implementation of sudden public health measures, we employ a Bang–Bang optimal control approach. The basic reproduction number is derived and the local stability of the equilibria is rigorously analyzed. The optimal control structure is characterized using the Pontryagin Maximum Principle. Model validation and policy assessment are carried out through numerical simulations calibrated with COVID-19 data from the United Kingdom. The findings indicate that the integration of vaccination with Bang–Bang interventions significantly suppresses infection peaks and reduces the duration of the overall outbreak. From an economic perspective, the combined strategy achieves improved cost-effectiveness compared to non-vaccination scenarios. Although the model assumes homogeneous mixing and simplifies certain operational aspects of intervention deployment, it offers a mathematically grounded and practically relevant framework for rapid-response policy design, contributing to the integration of epidemiological dynamics and economic optimization in infectious disease modeling. Jagannath University Journal of Science, Volume 12, Number 1, Jun. 2025, pp. 119−142
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.
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