FaMAF, Universidad Nacional de Córdoba, Córdoba, Córdoba, Argentina.
FaCENA, Universidad Nacional del Nordeste, Corrientes, Corrientes, Argentina.
PLoS One. 2022 Mar 4;17(3):e0264892. doi: 10.1371/journal.pone.0264892. eCollection 2022.
To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiological interest. However, there are many important features which are based on the individual interactions that cannot be represented in the mean field equations, such as social network and bubbles, contact tracing, isolating individuals in risk, and social network-based distancing strategies. Agent-based models can describe contact networks at an individual level, including demographic attributes such as age, neighborhood, household, workplaces, schools, entertainment places, among others. Nevertheless, these models have several unknown parameters which are thus difficult to prescribe. In this work, we propose the use of ensemble-based data assimilation techniques to calibrate an agent-based model using daily epidemiological data. This raises the challenge of having to adapt the agent populations to incorporate the information provided by the coarse-grained data. To do this, two stochastic strategies to correct the model predictions are developed. The ensemble Kalman filter with perturbed observations is used for the joint estimation of the state and some key epidemiological parameters. We conduct experiments with an agent based-model designed for COVID-19 and assess the proposed methodology on synthetic data and on COVID-19 daily reports from Ciudad Autónoma de Buenos Aires, Argentina.
为了根据数据代表疾病传播动力学中的复杂个体相互作用，提出了将基于代理的流行病学模型与集合卡尔曼滤波器耦合。以前的工作已经研究了通过基于集合的数据同化系统对疾病传播进行统计推断。所使用的模型大多是通过常微分方程表示平均场演化的隔间模型。这些技术允许从数据监测感染的传播并估计几个感兴趣的流行病学参数。然而，有许多重要的特征是基于个体相互作用的，无法用平均场方程表示，例如社交网络和泡沫、接触追踪、隔离风险个体以及基于社交网络的隔离策略。基于代理的模型可以在个体层面描述接触网络，包括年龄、社区、家庭、工作场所、学校、娱乐场所等人口统计属性。然而，这些模型有几个未知的参数，因此难以规定。在这项工作中，我们提出使用基于集合的数据同化技术来使用每日流行病学数据校准基于代理的模型。这提出了一个挑战，即必须适应代理人群，以纳入由粗粒度数据提供的信息。为此，开发了两种随机策略来纠正模型预测。带有受扰观测的集合卡尔曼滤波器用于状态和一些关键流行病学参数的联合估计。我们使用针对 COVID-19 设计的基于代理的模型进行实验，并在合成数据和阿根廷布宜诺斯艾利斯自治市的 COVID-19 每日报告上评估所提出的方法。
