Safta Cosmin, Ray Jaideep, Bridgman Wyatt
Data Sciences and Computing, Sandia National Laboratories, Livermore, California, United States of America.
PLoS One. 2025 Jul 31;20(7):e0328770. doi: 10.1371/journal.pone.0328770. eCollection 2025.
In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.
在本文中，我们提出了一种将疾病感染率估计为时空场的方法。我们的数据包括一个地区各个区域单元中有症状患者的时间序列病例数。我们扩展了一个最初为单个区域单元设计的流行病学模型，以适应多个单元。场估计是在贝叶斯框架内进行的，利用参数化高斯随机场作为空间先验。我们应用自适应马尔可夫链蒙特卡罗方法，以美国新墨西哥州三个相邻县的COVID-19病例数数据为条件，对模型参数的后验分布进行采样。我们的结果表明，相邻区域流行病学动态之间的相关性有助于对数据方差高（即质量差）的地区的估计进行正则化。使用校准后的流行模型，我们预测每个区域单元的感染率，并开发一个简单的异常检测器来发出新的疫情波信号。我们的研究结果表明，基于估计感染率的异常检测器优于仅依赖病例数的传统算法。
