دورية أكاديمية
Robust smoothing of left-censored time series data with a dynamic linear model to infer SARS-CoV-2 RNA concentrations in wastewater
| العنوان: | Robust smoothing of left-censored time series data with a dynamic linear model to infer SARS-CoV-2 RNA concentrations in wastewater |
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| المؤلفون: | Luke Lewis-Borrell, Jessica Irving, Chris J. Lilley, Marie Courbariaux, Gregory Nuel, Leon Danon, Kathleen M. O'Reilly, Jasmine M. S. Grimsley, Matthew J. Wade, Stefan Siegert |
| المصدر: | AIMS Mathematics, Vol 8, Iss 7, Pp 16790-16824 (2023) |
| بيانات النشر: | AIMS Press |
| سنة النشر: | 2023 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | dynamic linear model, wastewater-based epidemiology, covid-19, time series, left-censoring, bayesian inference, Mathematics, QA1-939 |
| الوصف: | 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. La détection et la surveillance du SARS-CoV-2 dans les eaux usées ont été développées et réalisées à un rythme sans précédent, mais l'interprétation des mesures de concentrations en ARN viral, et de leurs variations spatio-temporelles, pose question. En particulier, l'importante proportion de mesures en deçà du seuil de quantification, généralement pendant les périodes non endémiques, constitue un défi pour l'analyse de ces séries temporelles. Inspirés par un travail de recherche ayant produit un lisseur de Kalman adapté pour estimer les concentrations ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2473-6988 |
| العلاقة: | https://doaj.org/toc/2473-6988Test; https://doaj.org/article/5d278d74500744eab9dbe14e468287ebTest |
| DOI: | 10.3934/math.2023859 |
| الإتاحة: | https://doi.org/10.3934/math.2023859Test https://doaj.org/article/5d278d74500744eab9dbe14e468287ebTest |
| رقم الانضمام: | edsbas.1785001E |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 24736988 |
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| DOI: | 10.3934/math.2023859 |