agent-based contagion simulation

Conventional physics-based simulation methods struggle to capture historical evolution, heterogeneity, and emergent phenomena inherent in social systems. Method: This study systematically reviews the development trajectory and design principles of agent-based modeling (ABM) in the social sciences. It introduces— for the first time—a tripartite classification of ABM social simulation paradigms: thought experiments, mechanism exploration, and parallel optimization. A generic three-layer modeling framework is proposed, integrating agents, environments, and interaction rules, alongside a taxonomy tailored for social simulator development. Contribution/Results: By synthesizing canonical case studies and core methodological challenges, the work establishes a theoretical foundation and practical guidelines for the standardization of ABM methodology and the systematic engineering of social simulators, thereby advancing rigorous, interpretable, and scalable computational social science.

Evaluating the effectiveness of testing and isolation strategies during infectious disease outbreaks remains challenging due to complex human mobility and heterogeneous transmission dynamics. Method: We developed a highly modular, multi-scale agent-based model (ABM) incorporating real-world human mobility trajectories and venue-level contact data from Braunschweig, Germany, enabling spatially explicit, individual-level simulation of respiratory pathogen transmission. Contribution/Results: We introduced a symptom-agnostic framework for quantifying testing efficacy; identified that isolation duration exerts significantly greater impact on epidemic suppression than isolation efficiency; and demonstrated that even brief isolation achieves strong intervention effects under adequate symptom management. Leveraging high-performance single-thread optimization and parallel simulation, we quantified nonlinear effects of testing coverage and isolation parameters on SARS-CoV-2 transmission in Braunschweig during March–May 2021. Our model provides computationally grounded decision support for minimally intrusive, non-pharmaceutical interventions.

This study addresses the modeling of individual self-reporting decision-making during infectious disease outbreaks to support precision public health interventions. To this end, we develop a spatially explicit agent-based simulation framework that integrates real census data and, for the first time, deeply couples large language model–generated individual decisions with fine-grained geographic and social structures. The framework incorporates contextual factors such as household influence and information framing. Our approach systematically reveals income and education level as key drivers of disparities in influenza-like illness reporting rates and successfully captures behavioral heterogeneity across both social and geographic dimensions in simulations of San Francisco and Atlanta. This work establishes a novel paradigm for high-resolution behavioral epidemiological modeling.

This work addresses the challenge of manually optimizing non-pharmaceutical intervention (NPI) strategies due to the vast combinatorial space of possible measures. To overcome this, the authors propose ADIOS, a system that automatically searches for highly effective NPI policies with minimal societal disruption. ADIOS integrates agent-based epidemic simulation with grammar-guided genetic programming (GGGP) and introduces a domain-specific language (DSL) tailored for NPIs. This DSL structures the policy space using a context-free grammar and incorporates semantic constraints to eliminate infeasible strategies, substantially improving search efficiency. Experiments on GEMS, a high-resolution microsimulation platform for epidemics in Germany, demonstrate that ADIOS efficiently discovers near-optimal intervention strategies applicable to complex real-world scenarios.

To address the challenges of low simulation efficiency and coarse-grained intervention evaluation in large-scale real-world social contact networks, this paper introduces Loimos—a scalable parallel simulation framework for infectious disease propagation. Methodologically, Loimos pioneers a hybrid time-stepping and discrete-event-driven execution model, integrated with an asynchronous multi-task runtime and agent-based modeling (ABM) to enable fine-grained, dynamic evaluation of public health interventions. Technically, the framework tightly couples digital twin methodology with high-performance computing (HPC). Deployed on the NERSC Perlmutter supercomputer (4,096 cores), Loimos completes a 200-day COVID-19 transmission simulation over a California digital twin in just 42 seconds, achieving a throughput of 460 million edge updates per second. This represents a substantial leap beyond the scalability and efficiency limits of existing approaches.

This work proposes a unified, modular, high-performance simulation framework to address the fragmentation in current infectious disease modeling ecosystems, which hinders cross-model comparison and deployment across model types, spatial scales, and computational platforms. For the first time, the framework integrates compartmental models, meta-population models, and agent-based models within a single architecture, enabling multi-scale and comparable epidemic dynamics simulations. By standardizing representations of spatial, demographic, and mobility data, coupling a high-performance C++ core with a Python interface, and incorporating uncertainty quantification and parameter inference tools, the framework supports seamless deployment—from laptops to high-performance computing environments—significantly lowering barriers to reuse and accelerating the development of simulation-driven epidemic response capabilities.

This work proposes a novel approach to epidemic modeling by framing disease transmission as an iterative program synthesis problem, addressing the limitations of traditional models that rely on fixed structures and require extensive manual intervention to adapt to evolving pathogens, changing interventions, or shifting scenario assumptions. Central to this framework is an explicit epidemiological flow graph serving as an intermediate representation, which enables modular verification and interpretable parameter learning. By integrating agent-driven program synthesis, mechanism-based model compilation, and parameter optimization constrained by both physical and epidemiological principles, the method accurately captures complex transmission dynamics across diverse scenarios, generates counterfactual predictions grounded in epidemiological logic, and significantly accelerates convergence to high-quality models.

This work proposes a Koopman operator–based early warning and minimal intervention framework to address the high sensitivity of multi-agent epidemic systems to minor perturbations near critical transmission thresholds. By embedding high-dimensional nonlinear epidemic trajectories into a low-dimensional linear latent space, the approach enables high-accuracy outbreak risk prediction through a random forest classifier. Furthermore, counterfactual analysis is employed to identify individual-level minimal effective interventions. This study represents the first application of Koopman dynamic embeddings to epidemic forecasting and precision intervention, demonstrating that isolating a single key individual for just one day in near-critical scenarios can substantially reduce the attack rate and prevent large-scale outbreaks, thereby achieving exceptional predictive performance and intervention efficiency.

Quantifying the impact of human behavior on disease transmission during pandemics remains highly challenging. This work proposes the Epi-LLM framework, which for the first time integrates large language models (LLMs) into an agent-based SEIR epidemic model, leveraging data from pandemic-related behavioral game experiments and generalized linear models to simulate agents’ dynamic reasoning and adaptive behaviors within contact networks. The study reveals that architectural variance among LLMs significantly affects the validity of behavioral simulations and demonstrates the necessity of explicitly parameterizing attitudes to capture cultural differences. Experimental results show that all four LLM architectures effectively reduce infection peaks, achieving quarantine compliance rates of 58–65%. Perceived health severity emerges as the strongest behavioral predictor, and the model’s pseudo-R² aligns closely with empirical findings from human experiments.

This study addresses key challenges in epidemic decision-making, including hidden disease burdens, imperfect surveillance signals distorted by policy interventions, and intervention effects mediated by human behavior. It pioneers the systematic integration of the world model paradigm into computational epidemiology, framing epidemics as controlled partially observable dynamical systems. By jointly learning latent dynamics, endogenous observation mechanisms, and behavioral feedback loops, the approach enables counterfactual simulation and sequential decision planning under uncertainty. Evaluated across three case studies, the method effectively mitigates issues such as behavior-induced surveillance false positives and signal lags, demonstrating its necessity and superiority for policy evaluation and intervention analysis.

This study addresses the paradigm shift in social simulation—from exploring abstract mechanisms toward faithfully replicating high-fidelity real-world systems—by proposing “social digital twins” as a next-generation simulation framework. Integrating rule-based agent modeling, large language models, and data-driven approaches, this framework enables dynamic, fine-grained representations of specific socio-technical systems. The work systematically traces the three-stage evolution of social simulation methodologies, clarifying the applicability, strengths, and limitations of each approach. In doing so, it establishes a theoretical foundation and methodological guidance for constructing high-fidelity social digital twins that accurately mirror real-world complexities.