Agent-based modeling for realistic reproduction of human mobility and contact behavior to evaluate test and isolation strategies in epidemic infectious disease spread

2024-10-10

arXiv.org

AI Summary

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.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Agent-based models have proven to be useful tools in supporting decision-making processes in different application domains. The advent of modern computers and supercomputers has enabled these bottom-up approaches to realistically model human mobility and contact behavior. The COVID-19 pandemic showcased the urgent need for detailed and informative models that can answer research questions on transmission dynamics. We present a sophisticated agent-based model to simulate the spread of respiratory diseases. The model is highly modularized and can be used on various scales, from a small collection of buildings up to cities or countries. Although not being the focus of this paper, the model has undergone performance engineering on a single core and provides an efficient intra- and inter-simulation parallelization for time-critical decision-making processes. In order to allow answering research questions on individual level resolution, nonpharmaceutical intervention strategies such as face masks or venue closures can be implemented for particular locations or agents. In particular, we allow for sophisticated testing and isolation strategies to study the effects of minimal-invasive infectious disease mitigation. With realistic human mobility patterns for the region of Brunswick, Germany, we study the effects of different interventions between March 1st and May 30, 2021 in the SARS-CoV-2 pandemic. Our analyses suggest that symptom-independent testing has limited impact on the mitigation of disease dynamics if the dark figure in symptomatic cases is high. Furthermore, we found that quarantine length is more important than quarantine efficiency but that, with sufficient symptomatic control, also short quarantines can have a substantial effect.

Problem

• Simulate human mobility and contact for epidemic spread analysis
• Evaluate test and isolation strategies for disease mitigation
• Assess impact of nonpharmaceutical interventions on COVID-19 dynamics

Innovation

• Agent-based modeling for human mobility simulation
• Modular design scalable from buildings to countries
• Efficient parallelization for time-critical decision-making