Systematic review of predictive models of microbial water quality at freshwater recreational beaches.

Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18-24 hours to obtain a result. To support real-time notifications of water quality, models using...

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Autores principales:	Cole Heasley, J Johanna Sanchez, Jordan Tustin, Ian Young
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c8f80a6c019c4e93b2a366d418214983
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spelling	oai:doaj.org-article:c8f80a6c019c4e93b2a366d4182149832021-12-02T20:19:29ZSystematic review of predictive models of microbial water quality at freshwater recreational beaches.1932-620310.1371/journal.pone.0256785https://doaj.org/article/c8f80a6c019c4e93b2a366d4182149832021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256785https://doaj.org/toc/1932-6203Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18-24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.Cole HeasleyJ Johanna SanchezJordan TustinIan YoungPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256785 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cole Heasley J Johanna Sanchez Jordan Tustin Ian Young Systematic review of predictive models of microbial water quality at freshwater recreational beaches.
description	Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18-24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.
format	article
author	Cole Heasley J Johanna Sanchez Jordan Tustin Ian Young
author_facet	Cole Heasley J Johanna Sanchez Jordan Tustin Ian Young
author_sort	Cole Heasley
title	Systematic review of predictive models of microbial water quality at freshwater recreational beaches.
title_short	Systematic review of predictive models of microbial water quality at freshwater recreational beaches.
title_full	Systematic review of predictive models of microbial water quality at freshwater recreational beaches.
title_fullStr	Systematic review of predictive models of microbial water quality at freshwater recreational beaches.
title_full_unstemmed	Systematic review of predictive models of microbial water quality at freshwater recreational beaches.
title_sort	systematic review of predictive models of microbial water quality at freshwater recreational beaches.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c8f80a6c019c4e93b2a366d418214983
work_keys_str_mv	AT coleheasley systematicreviewofpredictivemodelsofmicrobialwaterqualityatfreshwaterrecreationalbeaches AT jjohannasanchez systematicreviewofpredictivemodelsofmicrobialwaterqualityatfreshwaterrecreationalbeaches AT jordantustin systematicreviewofpredictivemodelsofmicrobialwaterqualityatfreshwaterrecreationalbeaches AT ianyoung systematicreviewofpredictivemodelsofmicrobialwaterqualityatfreshwaterrecreationalbeaches
_version_	1718374191700377600

Systematic review of predictive models of microbial water quality at freshwater recreational beaches.

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