ABM-SIRTEM：一种用于大流行应对的基于智能体的混合模型与流行病学模型

中文摘要 AI 辅助

COVID-19大流行对全球健康、社会结构和经济产生了深远影响。它不成比例地影响了较低社会经济群体以及依赖互动型工作的个体。监管机构面临设计政策的挑战，这些政策需在维护公共卫生的同时，限制对经济稳定和生产力的干扰。诸如SIR等流行病学模型和基于智能体的模型（ABMs）已被用于研究疾病动态以及疾病和干预措施的社会经济影响。人群层面的模型往往简化个体异质性，而详细的ABMs随着智能体和互动数量的增加，计算成本可能变得高昂。我们提出ABM-SIRTEM，一种混合模型，它在个体层面纳入职业类别、经济生产力和福利，同时动态建模对政府干预措施的遵从性。我们根据美国四个州的历史阳性和阴性检测计数对模型进行校准，并检查由此产生的遵从动态。该框架为研究大流行应对规划中疾病传播与社会经济行为之间的相互作用提供了基础。

英文摘要

The COVID-19 pandemic has had profound impacts on global health, social structures, and economies. It disproportionately affected lower socioeconomic groups and those reliant on interaction-based jobs. Regulatory bodies faced the challenge of designing policies that preserve public health while limiting disruption to economic stability and productivity. Epidemiological models such as SIR and agent-based models (ABMs) have been used to study disease dynamics and the socioeconomic impacts of disease and interventions. Population-level models often simplify individual heterogeneity, while detailed ABMs can become computationally expensive as the numbers of agents and interactions increase. We propose ABM-SIRTEM, a hybrid model that incorporates occupation categories, economic productivity, and welfare at the individual level while dynamically modeling compliance with government interventions. We calibrate the model against historical positive and negative test counts from four U.S. states and examine the resulting compliance dynamics. This framework provides a basis for studying the interaction between disease spread and socioeconomic behavior in pandemic-response planning.

发表机构

• University of Southern California（南加州大学）
• Arizona State University（亚利桑那州立大学）

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