Drinking-water treatment, climate change, and childhood gastrointestinal illness projections for northern Wisconsin (USA) communities drinking untreated groundwater

Uejio, Christopher K.; Christenson, Megan; Moran, Colleen; Gorelick, Mark H.

doi:10.1007/s10040-016-1521-9

Cited by 11 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Well water can be adulterated by agricultural activity, septic systems and other sources 10–13. Contaminants can enter well water after precipitation and flooding, and intrusion may be facilitated by local hydrogeology 11 14 15…”

Section: Introductionmentioning

confidence: 99%

Study design and methods of the Wells and Enteric disease Transmission (WET) Trial: a randomised controlled trial

et al. 2023

View full text Add to dashboard Cite

IntroductionThe burden of disease attributed to drinking water from private wells is not well characterised. The Wells and Enteric disease Transmission trial is the first randomised controlled trial to estimate the burden of disease that can be attributed to the consumption of untreated private well water. To estimate the attributable incidence of gastrointestinal illness (GI) associated with private well water, we will test if the household treatment of well water by ultraviolet light (active UV device) versus sham (inactive UV device) decreases the incidence of GI in children under 5 years of age.Methods and analysisThe trial will enrol (on a rolling basis) 908 families in Pennsylvania, USA, that rely on private wells and have a child 3 years old or younger. Participating families are randomised to either an active whole-house UV device or a sham device. During follow-up, families will respond to weekly text messages to report the presence of signs and symptoms of gastrointestinal or respiratory illness and will be directed to an illness questionnaire when signs/symptoms are present. These data will be used to compare the incidence of waterborne illness between the two study groups. A randomly selected subcohort submits untreated well water samples and biological specimens (stool and saliva) from the participating child in both the presence and absence of signs/symptoms. Samples are analysed for the presence of common waterborne pathogens (stool and water) or immunoconversion to these pathogens (saliva).EthicsApproval has been obtained from Temple University’s Institutional Review Board (Protocol 25665). The results of the trial will be published in peer-reviewed journals.Trial registration numberNCT04826991.

show abstract

Section: Introductionmentioning

confidence: 99%

Study design and methods of the Wells and Enteric disease Transmission (WET) Trial: a randomised controlled trial

et al. 2023

View full text Add to dashboard Cite

show abstract

“…An extensive microbial waterborne disease outbreak among peoples who used water for drinking, domestic purpose and recreation [1][2][3]. Faecal coliforms that include Escherichia coli is the most common risk associated with microbial contamination in water [2].…”

Section: Introductionmentioning

confidence: 99%

Escherichia coli growth modeling using neural network

Shamsudin

Rahiman

Taib

et al. 2018

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

The assessment of water microbial quality is normally performed by verification of Escherichia coli where the growth is in nonlinearity. NARX is computational tools that have extensive utilization in solving nonlinear time series problems. It is well known technique that has the ability to predict with efficient and good performance. Using NARX, a highly accurate model was developed to predict the growth of Escherichia coli (E. coli) based on pH water parameter. The multiparameter portable sen used to build and train the neural network. The selection of neural network structure for pH and optical density modelling was optimized and also the training and validation were analyzed. The result exhibited that NARX based on pH water parameter with overall regression is 0.99956. The assessment of water microbial quality is normally performed by verification of Escherichia coli where the growth is in nonlinearity. NARX is computational tools that have extensive utilization in solving nonlinear time series problems. It is well known as one of the technique that has the ability to predict with efficient and good performance. Using NARX, a highly accurate model was developed to predict the growth of Escherichia coli (E. coli) based sor and spectrophotometer data were used to build and train the neural network. The selection of neural network structure for pH and optical density modelling was optimized and also the training and validation were modeling was able to predict the growth of E. coli neural network; NARX; prediction; Escherichia coli; pH; optical density. Keywords

show abstract

Developing an Integrated “Regression-QMRA method” to Predict Public Health Risks of Low Impact Developments (LIDs) for Improved Planning

Ishaq

Sadiq

Chhipi‐Shrestha

et al. 2022

Environmental Management

View full text Add to dashboard Cite

Drinking-water treatment, climate change, and childhood gastrointestinal illness projections for northern Wisconsin (USA) communities drinking untreated groundwater

Cited by 11 publications

References 45 publications

Study design and methods of the Wells and Enteric disease Transmission (WET) Trial: a randomised controlled trial

Study design and methods of the Wells and Enteric disease Transmission (WET) Trial: a randomised controlled trial

Escherichia coli growth modeling using neural network

Developing an Integrated “Regression-QMRA method” to Predict Public Health Risks of Low Impact Developments (LIDs) for Improved Planning

Contact Info

Product

Resources

About