Hybrid Metapopulation Agent-based Epidemiological Models

Kurzfassung

Emerging infectious diseases and climate change are two of the major challenges in the 21st century. Although over the past decades, highly-resolved mathematical models have contributed in understanding dynamics of infectious diseases and are of great aid when it comes to finding suitable intervention measures, they may require substantial computational effort and produce significant CO2 emissions. Two popular modeling approaches for mitigating infectious disease dynamics are agent-based and equation-based models. Agent-based models offer an arbitrary level of detail and are thus able to capture heterogeneous human contact behavior and mobility patterns. However, insights on individual-level dynamics come with high computational effort that scales with the number of agents. On the other hand, equation-based models are computationally efficient even for large populations due to their complexity being independent of the population size. Yet, equation-based models are restricted in their granularity as they assume a (to some extent) homogeneous and well-mixed population. To manage the trade-off between computational complexity and level of detail, we propose a spatial-hybrid model that uses an agent-based model only in an area of interest (focus region). To account for relevant influences to disease dynamics in the focus region, e.g. due to commuting activities, we use equation-based models in the neighboring regions to consider these influences while keeping moderate computational costs. Our hybridization approach demonstrates significant reduction in computational effort by more than 90% without losing the required depth in information, thus making our computational approach highly energy efficient. The hybrid model is based on two models recently introduced, however, any other suitable combination of agent-based and equation-based model could be used, too. Concluding, hybrid epidemiological models can provide insights on the individual scale where necessary, using aggregated models where possible, thereby making an important contribution to green computing.