标题： Epicast 2.0：一种大规模、人口统计学详细的基于代理的模型，用于模拟美国呼吸道病原体传播 显示英文标题

标题： Epicast 2.0: A large-scale, demographically detailed, agent-based model for simulating respiratory pathogen spread in the United States

摘要： 呼吸道病原体流行病的近期历史，包括流感和 SARS-CoV-2 引发的流行病，凸显了迫切需要先进的建模方法，这些方法能够准确捕捉全国范围内异质性疾病动态和结果，从而提高资源分配和决策的有效性。 在本文中，我们描述了Epicast 2.0，这是一个基于代理的模型，利用高度详细的合成人口和高性能计算技术来模拟整个美国的呼吸道病原体传播。 该模型复制了超过3.2亿个代理在日常在学校、工作场所和社区中的接触模式。 Epicast 2.0 支持疫苗接种以及一系列非药物干预措施，这些干预措施可以通过高度精细的、用户指定的政策进行推广或放松。 我们通过广泛的爆发情景展示了该模型的能力，突出了模型的各种动态及其对政策探索的广泛支持。 该模型为开展假设场景分析并提供减轻传染病影响的潜在策略提供了强大的平台。 显示英文摘要

摘要： The recent history of respiratory pathogen epidemics, including those caused by influenza and SARS-CoV-2, has highlighted the urgent need for advanced modeling approaches that can accurately capture heterogeneous disease dynamics and outcomes at the national scale, thereby enhancing the effectiveness of resource allocation and decision-making. In this paper, we describe Epicast 2.0, an agent-based model that utilizes a highly detailed, synthetic population and high-performance computing techniques to simulate respiratory pathogen transmission across the entire United States. This model replicates the contact patterns of over 320 million agents as they engage in daily activities at school, work, and within their communities. Epicast 2.0 supports vaccination and an array of non-pharmaceutical interventions that can be promoted or relaxed via highly granular, user specified policies. We illustrate the model's capabilities using a wide range of outbreak scenarios, highlighting the model's varied dynamics as well as its extensive support for policy exploration. This model provides a robust platform for conducting what if scenario analysis and providing insights into potential strategies for mitigating the impacts of infectious diseases.