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Robust smoothing of left-censored time series data with a dynamic linear model to infer SARS-CoV-2 RNA concentrations in wastewater

Lookup NU author(s): Dr Matthew WadeORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2023, American Institute of Mathematical Sciences. All rights reserved.Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation of measurements that are below a quantifiable threshold, usually during non-endemic periods, poses a further challenge to interpretation and time-series analysis of the data. Inspired by research in the use of a custom Kalman smoother model to estimate the true level of SARS-CoV-2 RNA concentrations in wastewater, we propose an alternative left-censored dynamic linear model. Cross-validation of both models alongside a simple moving average, using data from 286 sewage treatment works across England, allows for a comprehensive validation of the proposed approach. The presented dynamic linear model is more parsimonious, has a faster computational time and is represented by a more flexible modelling framework than the equivalent Kalman smoother. Furthermore we show how the use of wastewater data, transformed by such models, correlates more closely with regional case rate positivity as published by the Office for National Statistics (ONS) Coronavirus (COVID-19) Infection Survey. The modelled output is more robust and is therefore capable of better complementing traditional surveillance than untransformed data or a simple moving average, providing additional confidence and utility for public health decision making.

Publication metadata

Author(s): Lewis-Borrell L, Irving J, Lilley CJ, Courbariaux M, Nuel G, Danon L, O'reilly KM, Grimsley JMS, Wade MJ, Siegert S

Publication type: Article

Publication status: Published

Journal: AIMS Mathematics

Year: 2023

Volume: 8

Issue: 7

Pages: 16790-16824

Online publication date: 15/05/2023

Acceptance date: 17/04/2023

Date deposited: 01/06/2023

ISSN (electronic): 2473-6988

Publisher: American Institute of Mathematical Sciences

URL: https://doi.org/10.3934/math.2023859

DOI: 10.3934/math.2023859

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