Bicker Julia, Schmieding René, Meyer-Hermann Michael, Kühn Martin J
Institute of Software Technology, Department of High-Performance Computing, German Aerospace Center, Cologne, Germany.
Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Brunswick, Germany.
Infect Dis Model. 2025 Jan 10;10(2):571-590. doi: 10.1016/j.idm.2024.12.015. eCollection 2025 Jun.
Emerging infectious diseases and climate change are two of the major challenges in 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 need substantial computational effort and produce significant CO emissions. Two popular modeling approaches for mitigating infectious disease dynamics are agent-based and population-based models. Agent-based models (ABMs) offer a microscopic view 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, population-based models (PBMs) using e.g. ordinary differential equations (ODEs) are computationally efficient even for large populations due to their complexity being independent of the population size. Yet, population-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 spatial- and temporal-hybrid models that use ABMs only in an area or time frame of interest. To account for relevant influences to disease dynamics, e.g., from outside, due to commuting activities, we use population-based models, only adding moderate computational costs. Our hybridization approach demonstrates significant reduction in computational effort by up to 98% - without losing the required depth in information in the focus frame. The hybrid models used in our numerical simulations are based on two recently proposed models, however, any suitable combination of ABM and PBM could be used, too. Concluding, hybrid epidemiological models can provide insights on the individual scale where necessary, using aggregated models where possible, thereby making a contribution to green computing.
新发传染病和气候变化是21世纪的两大主要挑战。尽管在过去几十年里，高分辨率数学模型有助于理解传染病动态，并且在寻找合适的干预措施方面有很大帮助，但它们可能需要大量的计算工作并产生大量的碳排放。两种流行的减轻传染病动态的建模方法是基于主体的模型和基于群体的模型。基于主体的模型（ABM）提供微观视角，因此能够捕捉异质的人类接触行为和流动模式。然而，对个体层面动态的洞察伴随着与主体数量成比例的高计算量。另一方面，使用例如常微分方程（ODE）的基于群体的模型（PBM），由于其复杂性与群体规模无关，即使对于大量群体也具有计算效率。然而，基于群体的模型在粒度上受到限制，因为它们假设群体在某种程度上是同质且充分混合的。为了在计算复杂性和细节程度之间进行权衡，我们提出了时空混合模型，该模型仅在感兴趣的区域或时间框架内使用ABM。为了考虑对疾病动态的相关影响，例如来自外部的通勤活动的影响，我们使用基于群体的模型，只增加适度的计算成本。我们的混合方法表明计算量显著减少了高达98%——同时在焦点框架中不会丢失所需的信息深度。我们数值模拟中使用的混合模型基于最近提出的两个模型，然而，也可以使用ABM和PBM的任何合适组合。总之，混合流行病学模型可以在必要时提供个体尺度的见解，在可能的情况下使用聚合模型，从而为绿色计算做出贡献。
