Joint Modeling of Two Stochastic Processes, with Application to Learning Hospitalization Dynamics from Wastewater Viral Concentrations

TL;DR

This paper introduces a joint modeling framework linking wastewater viral signals and hospitalizations to better understand COVID-19 infection dynamics, accommodating under-reporting and aggregated data.

Contribution

It proposes a novel statistical approach connecting wastewater data and hospitalizations via a latent infection process, improving inference under data limitations.

Findings

The method provides stable inference across various under-reporting levels.

Application to Ottawa data reveals coherent infection and hospitalization patterns.

Framework effectively captures variant-specific risks.

Abstract

In the post-pandemic era of COVID-19, hospitalization remains a primary public health concern and wastewater surveillance has become an important tool for monitoring its dynamics at the level of community. However, there is usually no sufficient information to know the infection process that results in both wastewater viral signals and hospital admissions. That key challenge has motived a statistical framework proposed in this paper. We formulate the connection of overtime wastewater viral signals and hospitalization counts through a latent process of infection at the level of individual subject. We provide a strategy for accommodating aggregated data, a typical form of surveillance data. Moreover, we ease the conventional procedure of the statistical learning with the joint modeling using available information on the infection process, which can be under-reporting. A simulation study demonstrates that the proposed approach yields stable inference under different degrees of under-ascertainment. The COVID-19 surveillance data from Ottawa, Canada shows that the framework recovers coherent temporal patterns in infection prevalence and variant-specific hospitalization risk under several reporting assumptions.