Predicting COVID-19 Prevalence Using Wastewater RNA Surveillance: A Semi-Supervised Learning Approach with Temporal Feature Trust

TL;DR

This paper introduces a deep neural network model that predicts COVID-19 case counts from wastewater RNA data, addressing temporal feature reliability to improve monitoring during endemic phases.

Contribution

It presents a semi-supervised learning approach with temporal feature trust to enhance COVID-19 prevalence estimation from wastewater data.

Findings

Effective neural network estimator for COVID-19 case prediction.

Improved accuracy by handling temporal feature reliability.

Demonstrated applicability during stable endemic periods.

Abstract

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.