Agent-based model (ABM) has been widely used to study infectious disease transmission by simulating behaviors and interactions of autonomous individuals called agents. In the ABM, agent states, for example infected or susceptible, are assigned according to a set of simple rules, and a complex dynamics of disease transmission is described by the collective states of agents over time. Despite the flexibility in real-world modeling, ABMs have received less attention by statisticians because of the intractable likelihood functions which lead to difficulty in estimating parameters and quantifying uncertainty around model outputs. To overcome this limitation, a Bayesian framework that treats the entire ABM as a Hidden Markov Model has been previously proposed. However, existing approach is limited due to computational inefficiency and unidentifiability of parameters. We extend the ABM approach within Bayesian framework to study infectious disease transmission addressing these limitations. We estimate the hidden states, represented by individual agent's states over time, and the model parameters by applying an improved particle Markov Chain Monte Carlo algorithm, that accounts for computing efficiency. We further evaluate the performance of the approach for parameter recovery and prediction, along with sensitivity to prior assumptions under various simulation conditions. Finally, we apply the proposed approach to the study of COVID-19 outbreak on Diamond Princess cruise ship. We examine the differences in transmission by key demographic characteristics, while considering two different networks and limited COVID-19 testing in the cruise.

中文翻译：

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用于分析 COVID-19 数据的基于贝叶斯代理的建模的注意事项

基于代理的模型（ABM）已被广泛用于通过模拟称为代理的自主个体的行为和交互来研究传染病传播。在 ABM 中，媒介状态（例如感染或易感）根据一组简单的规则进行分配，并且疾病传播的复杂动态由媒介随时间的集体状态描述。尽管现实世界建模具有灵活性，但 ABM 受到统计学家的关注较少，因为其棘手的似然函数导致难以估计参数和量化模型输出的不确定性。为了克服这一限制，之前已经提出了将整个 ABM 视为隐马尔可夫模型的贝叶斯框架。然而，由于计算效率低下和参数的不可识别性，现有方法受到限制。我们在贝叶斯框架内扩展 ABM 方法来研究传染病传播，解决这些局限性。我们通过应用改进的粒子马尔可夫链蒙特卡罗算法来估计隐藏状态（由个体代理随时间的状态表示）和模型参数，该算法考虑了计算效率。我们进一步评估该方法的参数恢复和预测性能，以及在各种模拟条件下对先前假设的敏感性。最后，我们将所提出的方法应用于钻石公主号游轮上的 COVID-19 爆发研究。我们研究了关键人口特征的传播差异，同时考虑了两个不同的网络和游轮中有限的 COVID-19 测试。