A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.
Bayesian Poisson regression
Bayesian negative binomial regression
SARS-CoV-2
functional data analysis
wastewater epidemiology
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
14 Jan 2024
14 Jan 2024
Historique:
revised:
11
11
2023
received:
02
05
2023
accepted:
21
12
2023
medline:
15
1
2024
pubmed:
15
1
2024
entrez:
15
1
2024
Statut:
aheadofprint
Résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : CIHR
ID : 448242
Pays : Canada
Organisme : NIGMS NIH HHS
ID : 1R35GM150537-01
Pays : United States
Informations de copyright
© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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