Water quality index and sanitary and socioeconomic indicators in Minas Gerais, Brazil

Pataca, Luiz Carlos Moutinho; Pedrosa, Marco Antônio Ferreira; Zolnikov, Tara Rava; Mol, Marcos Paulo Gomes

doi:10.1007/s10661-020-08425-9

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Numerous waterborne outbreaks have been reported in Brazil since 1966, including a 2018 outbreak with at least 2270 suspected cases, which was the largest oocyst-associated outbreak reported to date [17]. One of the reasons that waterborne transmission is so common is variability in the quality of drinking water available to many Brazilians on average [73] and huge disparities in the availability and quality of clean water in Brazil across socioeconomic levels [74]. Aside from a lack of knowledge of proper food safety, unreliable or nonexistent access to treated water can put individuals from poorer socioeconomic backgrounds at a higher risk for T. gondii exposure.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Risk Assessment of Oocyst Versus Bradyzoite Foodborne Transmission of Toxoplasma gondii in Brazil

et al. 2023

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Toxoplasma gondii is a globally distributed zoonotic protozoan parasite. Infection with T. gondii can cause congenital toxoplasmosis in developing fetuses and acute outbreaks in the general population, and the disease burden is especially high in South America. Prior studies found that the environmental stage of T. gondii, oocysts, is an important source of infection in Brazil; however, no studies have quantified this risk relative to other parasite stages. We developed a Bayesian quantitative risk assessment (QRA) to estimate the relative attribution of the two primary parasite stages (bradyzoite and oocyst) that can be transmitted in foods to people in Brazil. Oocyst contamination in fruits and greens contributed significantly more to overall estimated T. gondii infections than bradyzoite-contaminated foods (beef, pork, poultry). In sensitivity analysis, treatment, i.e., cooking temperature for meat and washing efficiency for produce, most strongly affected the estimated toxoplasmosis incidence rate. Due to the lack of regional food contamination prevalence data and the high level of uncertainty in many model parameters, this analysis provides an initial estimate of the relative importance of food products. Important knowledge gaps for oocyst-borne infections were identified and can drive future studies to improve risk assessments and effective policy actions to reduce human toxoplasmosis in Brazil.

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Section: Discussionmentioning

confidence: 99%

Quantitative Risk Assessment of Oocyst Versus Bradyzoite Foodborne Transmission of Toxoplasma gondii in Brazil

et al. 2023

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“…The GLM is an advanced version of the most widely used linear regression model. It provides a comprehensive function that can handle datasets for regression analysis that are both linear and nonlinear [48,49]. It creates the output model using the exponential family.…”

Section: Generalized Linear Model (Glm)mentioning

confidence: 99%

Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Alshehri

Rahman

2023

Water

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Recently, machine learning (ML) and deep learning (DL) models based on artificial intelligence (AI) have emerged as fast and reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework based on DL models for WQI prediction, employing a convolutional neural network (CNN) model. Additionally, we introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies in water management. Our findings demonstrate that the stacking model achieves the highest accuracy in WQI prediction (R2: 0.99, MAPE: 15.99%), outperforming the CNN model (R2: 0.90, MAPE: 58.97%). Although the CNN model shows a relatively high R2 value, other statistical measures indicate that it is actually the worst-performing model among the five tested. This discrepancy may be attributed to the limited training data available for the CNN model. Furthermore, the application of explainable AI (XAI) techniques, specifically XGBoost-based SHAP values, allows us to gain deep insights into the models and extract valuable information for water management purposes. The SHAP values and interaction plot reveal that elevated levels of total dissolved solids (TDS), zinc, and electrical conductivity (EC) are the primary drivers of poor water quality. These parameters exhibit a nonlinear relationship with the water quality index, implying that even minor increases in their concentrations can significantly impact water quality. Overall, this study presents a comprehensive and integrated approach to water management, emphasizing the need for collaborative efforts among all stakeholders to mitigate pollution levels and uphold water quality. By leveraging AI and XAI, our proposed framework not only provides a powerful tool for accurate WQI prediction but also offers deep insights into the models, enabling informed decision-making in water management strategies.

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“…The main biological indicator for determining water quality is the bacteria Escherichia coli, however, several other microorganisms may be present, such as viruses, protozoa and fungi [8][9][10]. Pollution of the environment is almost always a consequence of human actions, either through illegally discharged sewage into surface waters or the use of chemical substances that reach groundwater [11].…”

Section: Introductionmentioning

confidence: 99%

Association among the Presence of Rotavirus Group A and Types of Sources Located in Rural Communities

Gonçalves

Lima

Silva

et al. 2023

Water

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Among the etiological agents of acute gastroenterocolitis, group A Rotavirus stands out. Proper hygiene and basic sanitation are the prophylactic means. The present research aimed to carry out the tracking of Rotavirus in waters of rural communities. The collections were carried out in 86 residences, distributed in 16 communities, considering the types of sources: tubular wells, excavated wells, surface springs, cistern with rainwater and springs. The elution of membranes with negative charges was used to obtain viral particles. Extraction of the genetic material and cDNA synthesis were performed according to the rules of the kit used, then qPCR was performed with an Applied Biosystems StepOne® thermal cycler. Statistical analysis was performed using Stata® 16.0, BioEstat® 5.3 and Minitab® 1. Rotavirus was detected in 47 samples (54.6%), however, there was no significant association among municipalities and the presence of Rotavirus. There was a strong association with shallow excavated well OR = 3.59 [1.05–12.28, p = 0.04]. The virus to infect humans and non-human animals, it can infect poultry, cattle and pigs. The presence of Rotavirus in surface water qualifies it as a biological marker of water quality and would occur through fecal contamination in water surface and underground.

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Water quality index and sanitary and socioeconomic indicators in Minas Gerais, Brazil

Cited by 7 publications

References 19 publications

Quantitative Risk Assessment of Oocyst Versus Bradyzoite Foodborne Transmission of Toxoplasma gondii in Brazil

Quantitative Risk Assessment of Oocyst Versus Bradyzoite Foodborne Transmission of Toxoplasma gondii in Brazil

Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Association among the Presence of Rotavirus Group A and Types of Sources Located in Rural Communities

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