Bayesian Sequential Approach to Monitor COVID-19 Variants Through Positivity Rate from Wastewater

Overview

Journal medRxiv

Date 2023 Jan 30

PMID 36711939

Authors

J Cricelio Montesinos-Lopez

Maria L Daza-Torres

Yury E Garcia

Cesar Herrera

C Winston Bess

Heather N Bischel

Miriam Nuno

Affiliations

Soon will be listed here.

Abstract

Trends in COVID-19 infection have changed throughout the pandemic due to myriad factors, including changes in transmission driven by social behavior, vaccine development and uptake, mutations in the virus genome, and public health policies. Mass testing was an essential control measure for curtailing the burden of COVID-19 and monitoring the magnitude of the pandemic during its multiple phases. However, as the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementing vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 tests reduced the demand for mass severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. This paper proposes a sequential Bayesian approach to estimate the COVID-19 positivity rate (PR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. PR estimates are used to compute thresholds for WW data using the CDC thresholds for low, substantial, and high transmission. The effective reproductive number estimates are calculated using PR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring the COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. The proposed modeling framework was applied to the City of Davis and the campus of the University of California Davis.

References

DAoust P, Tian X, Towhid S, Xiao A, Mercier E, Hegazy N . Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities. Sci Total Environ. 2022; 853:158547. PMC: 9444156. DOI: 10.1016/j.scitotenv.2022.158547. View

Bottcher L, DOrsogna M, Chou T . A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors. Philos Trans A Math Phys Eng Sci. 2021; 380(2214):20210121. PMC: 8607147. DOI: 10.1098/rsta.2021.0121. View

Rader B, Gertz A, Iuliano A, Gilmer M, Wronski L, Astley C . Use of At-Home COVID-19 Tests - United States, August 23, 2021-March 12, 2022. MMWR Morb Mortal Wkly Rep. 2022; 71(13):489-494. PMC: 8979595. DOI: 10.15585/mmwr.mm7113e1. View

Rothman J, Loveless T, Kapcia 3rd J, Adams E, Steele J, Zimmer-Faust A . RNA Viromics of Southern California Wastewater and Detection of SARS-CoV-2 Single-Nucleotide Variants. Appl Environ Microbiol. 2021; 87(23):e0144821. PMC: 8579973. DOI: 10.1128/AEM.01448-21. View

Graham M, Sudre C, May A, Antonelli M, Murray B, Varsavsky T . Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study. Lancet Public Health. 2021; 6(5):e335-e345. PMC: 8041365. DOI: 10.1016/S2468-2667(21)00055-4. View

Fernandez-Cassi X, Scheidegger A, Banziger C, Cariti F, Tunas Corzon A, Ganesanandamoorthy P . Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high. Water Res. 2021; 200:117252. PMC: 8126994. DOI: 10.1016/j.watres.2021.117252. View

Xiao A, Wu F, Bushman M, Zhang J, Imakaev M, Chai P . Metrics to relate COVID-19 wastewater data to clinical testing dynamics. Water Res. 2022; 212:118070. PMC: 8758950. DOI: 10.1016/j.watres.2022.118070. View

Engbert R, Rabe M, Kliegl R, Reich S . Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics. Bull Math Biol. 2020; 83(1):1. PMC: 7721793. DOI: 10.1007/s11538-020-00834-8. View

Montesinos-Lopez J, Daza-Torres M, Garcia Y, Barboza L, Sanchez F, Schmidt A . The Role of SARS-CoV-2 Testing on Hospitalizations in California. Life (Basel). 2021; 11(12). PMC: 8707159. DOI: 10.3390/life11121336. View

10.

Capistran M, Capella A, Christen J . Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves. PLoS One. 2021; 16(1):e0245669. PMC: 7822260. DOI: 10.1371/journal.pone.0245669. View

11.

Ahmed W, Angel N, Edson J, Bibby K, Bivins A, OBrien J . First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: A proof of concept for the wastewater surveillance of COVID-19 in the community. Sci Total Environ. 2020; 728:138764. PMC: 7165106. DOI: 10.1016/j.scitotenv.2020.138764. View

12.

Choi P, Tscharke B, Samanipour S, Hall W, Gartner C, Mueller J . Social, demographic, and economic correlates of food and chemical consumption measured by wastewater-based epidemiology. Proc Natl Acad Sci U S A. 2019; 116(43):21864-21873. PMC: 6815118. DOI: 10.1073/pnas.1910242116. View

13.

Yang J, Chen X, Deng X, Chen Z, Gong H, Yan H . Disease burden and clinical severity of the first pandemic wave of COVID-19 in Wuhan, China. Nat Commun. 2020; 11(1):5411. PMC: 7591855. DOI: 10.1038/s41467-020-19238-2. View

14.

Furuse Y, Ko Y, Ninomiya K, Suzuki M, Oshitani H . Relationship of Test Positivity Rates with COVID-19 Epidemic Dynamics. Int J Environ Res Public Health. 2021; 18(9). PMC: 8125747. DOI: 10.3390/ijerph18094655. View

15.

Jewell N, Lewnard J, Jewell B . Predictive Mathematical Models of the COVID-19 Pandemic: Underlying Principles and Value of Projections. JAMA. 2020; 323(19):1893-1894. DOI: 10.1001/jama.2020.6585. View

16.

Montesinos-Lopez J, Daza-Torres M, Garcia Y, Herrera C, Bess C, Bischel H . Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater. mSystems. 2023; 8(4):e0001823. PMC: 10469603. DOI: 10.1128/msystems.00018-23. View

17.

Mercer T, Salit M . Testing at scale during the COVID-19 pandemic. Nat Rev Genet. 2021; 22(7):415-426. PMC: 8094986. DOI: 10.1038/s41576-021-00360-w. View

18.

Zuccato E, Chiabrando C, Castiglioni S, Calamari D, Bagnati R, Schiarea S . Cocaine in surface waters: a new evidence-based tool to monitor community drug abuse. Environ Health. 2005; 4:14. PMC: 1190203. DOI: 10.1186/1476-069X-4-14. View

19.

Shinde G, Kalamkar A, Mahalle P, Dey N, Chaki J, Hassanien A . Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Comput Sci. 2020; 1(4):197. PMC: 7289234. DOI: 10.1007/s42979-020-00209-9. View

20.

Daza-Torres M, Montesinos-Lopez J, Kim M, Olson R, Bess C, Rueda L . Model training periods impact estimation of COVID-19 incidence from wastewater viral loads. Sci Total Environ. 2022; 858(Pt 1):159680. PMC: 9597566. DOI: 10.1016/j.scitotenv.2022.159680. View