A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.
A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.
复制标题
通过纵向监测废水中的 SARS-CoV-2 RNA 对 COVID-19 病例数进行建模的贝叶斯框架。
作者：
Dai,Xiaotian;Acosta,Nicole;Lu,Xuewen;Hubert,CaseyRJ;Lee,Jangwoo;Frankowski,Kevin;Bautista,MariaA;Waddell,BarbaraJ;Du,Kristine;McCalder,Janine;Meddings,Jon;Ruecker,Norma;Williamson,Tyler;Southern,DanielleA;Hollman,Jordan;
基于废水的监测已成为研究小组和公共卫生机构调查和监测COVID-19大流行和其他公共卫生紧急情况（包括其他病原体和药物滥用）的重要工具。虽然有大量证据正在探索从废水信号预测COVID-19感染的可能性，但统计建模仍面临重大挑战。城市污水中病毒拷贝的纵向观察可能会受到噪声数据集和不规则和稀疏采样的缺失值的影响。我们提出了一个综合的贝叶斯框架，通过功能数据分析技术，从每周的废水观测与缺失值中预测每日阳性病例。在统一的过程中，所提出的分析将严重急性呼吸综合征冠状病毒-2 RNA废水信号建模为具有误差的平滑过程的实现，并将平滑过程与COVID-19病例相结合，以评估阳性病例的预测。我们表明，所提出的框架可以实现这些目标，通过模拟和观察到的真实的数据具有较高的预测精度。
Wastewater‐based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID‐19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID‐19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus‐2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID‐19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.