Development of Bayesian segmented Poisson regression model to forecast COVID-19 dynamics based on wastewater data: a case study in Nanning City, China.

ABSTRACT:

Introduction

COVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.

Method

Viral load in wastewater and the number of daily reported COVID-19 cases were collected from Nanning CDC and the Chinese Disease Prevention and Control Information System, respectively. We used the viral load to estimate daily reported

SUBMITTER: Xu B 

PROVIDER: S-EPMC11721287 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature