A Bayesian framework for modeling COVID‐19 case numbers through longitudinal monitoring of SARS‐CoV‐2 RNA in wastewater

Author:

Dai Xiaotian12ORCID,Acosta Nicole3,Lu Xuewen2,Hubert Casey R. J.4,Lee Jangwoo34,Frankowski Kevin5,Bautista Maria A.4,Waddell Barbara J.3,Du Kristine3,McCalder Janine34,Meddings Jon67,Ruecker Norma8,Williamson Tyler910,Southern Danielle A.910,Hollman Jordan11,Achari Gopal12,Ryan M. Cathryn11,Hrudey Steve E.13,Lee Bonita E.14,Pang Xiaoli13,Clark Rhonda G.4,Parkins Michael D.367,Chekouo Thierry215ORCID

Affiliation:

1. Department of Mathematics Illinois State University Normal Illinois USA

2. Department of Mathematics and Statistics University of Calgary Calgary Alberta Canada

3. Department of Microbiology, Immunology and Infectious Diseases University of Calgary Calgary Alberta Canada

4. Department of Biological Science University of Calgary Calgary Alberta Canada

5. Advancing Canadian Water Assets University of Calgary Calgary Alberta Canada

6. Department of Medicine, Cumming School of Medicine University of Calgary Calgary Alberta Canada

7. Alberta Health Services Edmonton Alberta Canada

8. Water Services City of Calgary Calgary Alberta Canada

9. Department of Community Health Sciences University of Calgary Calgary Alberta Canada

10. Centre for Health Informatics, Cumming School of Medicine University of Calgary Calgary Alberta Canada

11. Department of Geosciences University of Calgary Calgary Alberta Canada

12. Department of Civil Engineering University of Calgary Calgary Alberta Canada

13. Department of Laboratory Medicine and Pathology University of Alberta Edmonton Alberta Canada

14. Department of Pediatrics University of Alberta Edmonton Alberta Canada

15. Division of Biostatistics and Health Data Science, School of Public Health University of Minnesota Minneapolis Minnesota USA

Abstract

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.

Funder

Alberta Health

Canadian Institutes of Health Research

National Institute of General Medical Sciences

Illinois State University

Natural Sciences and Engineering Research Council of Canada

University of Minnesota

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

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