用污水病毒浓度预测新冠感染数，解决不同时段数据可靠性差异问题。
Predicting COVID-19 Prevalence Using Wastewater RNA Surveillance: A Semi-Supervised Learning Approach with Temporal Feature Trust
- 基于污水中病毒RNA浓度与检测数据，构建深度神经网络模型。
- 在数据可靠性高的时段训练，实现对每日感染数的精准估计。
- 适用于长期监测疫情、无需大规模核酸检测的场景。
随着新冠疫情转入长期流行状态，无需侵入性手段监测其流行程度变得日益重要。本文提出一种基于污水监测数据和其它混杂因素的深度神经网络估计器，用于预测每日新冠感染病例数。该工作建立在Jiang、Kolozsvary和Li（2024）的研究基础上，该研究将新冠病例数与疫情初期的检测数据相关联。利用在数据高度可靠时期采集的新冠检测数据与污水监测数据，可训练人工神经网络以学习每日感染数与污水中病毒RNA浓度之间的非线性关系。从机器学习角度看，主要挑战在于处理时间特征的可靠性差异，因训练数据在不同时间段的可信度存在差异。
原文摘要 · Abstract (English)
As COVID-19 transitions into an endemic disease that remains constantly present in the population at a stable level, monitoring its prevalence without invasive measures becomes increasingly important. In this paper, we present a deep neural network estimator for the COVID-19 daily case count based on wastewater surveillance data and other confounding factors. This work builds upon the study by Jiang, Kolozsvary, and Li (2024), which connects the COVID-19 case counts with testing data collected early in the pandemic. Using the COVID-19 testing data and the wastewater surveillance data during the period when both data were highly reliable, one can train an artificial neural network that learns the nonlinear relation between the COVID-19 daily case count and the wastewater viral RNA concentration. From a machine learning perspective, the main challenge lies in addressing temporal feature reliability, as the training data has different reliability over different time periods.
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