Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis.
Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis.
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DOI：
10.1186/s12874-023-01987-5
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发表时间：
2023-07-22
影响因子：
4
通讯作者：
中科院分区：
文献类型：
--
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COVID-19给公共卫生监测带来了巨大挑战，并强调了开发和维护强大的准确监测系统的重要性。随着公共卫生数据收集工作的扩大，传染病建模研究人员迫切需要继续开发前瞻性监测指标和统计模型，以适应大型疾病计数和变异性的建模。本文评估了不同的可能性疾病计数模型和各种时空平均模型的前瞻性监测。我们评估了贝叶斯时空模型，这是基于模型的传染病监测指标的基础。贝叶斯时空均值模型的基础上的泊松和负二项似然进行了评估与过去的数据使用的不同长度。我们将它们的拟合优度和短期预测性能与模拟疫情数据和COVID-19大流行的真实的数据进行了比较。仿真结果表明，负二项似然模型比泊松似然模型具有更好的拟合优度，因为偏差信息准则（DIC）值较小。然而，泊松模型产生较小的均方误差（MSE）和平均绝对一步预测误差（MAOSPE）的结果，当我们使用一个较短的长度的过去的数据，如7和3个时间段。对新泽西和南卡罗来纳州的真实的COVID-19数据分析显示，拟合优度和短期预测结果相似。当我们使用52个时间段的过去数据时，基于负二项的均值模型表现出更好的性能。当我们使用7和3个时间段的过去数据时，基于泊松的平均模型显示出相当的拟合优度性能和较小的MSE和MAOSPE结果。我们评估这些模型，并为贝叶斯时空分析提供未来传染病爆发建模指南。我们的选择的可能性和时空平均模型的历史数据的长度和变异性的影响。随着过去数据使用时间的延长和数据的过度分散，负二项似然比泊松似然显示出更好的模型拟合。然而，由于我们使用较短长度的过去数据进行监测分析，泊松和负二项模型之间的差异变得更小。在这种情况下，泊松似然显示了稳健的后验均值估计和短期预测结果。在线版本包含补充材料，可通过10.1186/s12874-023-01987-5获得。
COVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance. We evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic. The simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods. We evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results. The online version contains supplementary material available at 10.1186/s12874-023-01987-5.
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影响因子：
5.8
作者：
Dadaneh, Siamak Zamani;Zhou, Mingyuan;Qian, Xiaoning
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DOI：
10.1080/10618600.2016.1172487
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