Use of Bayesian networks in predicting contamination of drinking water with E. coli in rural Vietnam
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Notice bibliographique
Résumé
Background: More than 70 million Vietnamese rely on small-scale farming for some form of household income. Water on many of those farms is contaminated with waste, including animal manure, partly due to non-sustainable waste management. This increases the risk of water-related zoonotic disease transmission. The purpose of this research was to examine the impact of various demographic and management factors on the likelihood of finding Escherichia coli in drinking water sourced from wells and rainwater on farms in Vietnam. Methods: A Bayesian Belief Network (BBN) was designed to describe association between various deterministic and probabilistic variables gathered from 600 small-scale integrated (SSI) farmers in Vietnam. The variables relate to E. coli content of their drinking water sourced on-farm from wells and rainwater, and stored in on-farm large vessels, including concrete water tanks. The BBN was developed using the Netica software tool; the model was calibrated and goodness of fit examined using concordance of predictability. Results: Sensitivity analysis of the model revealed that choice variables, including engagement in mitigation of water contamination and livestock management activities, were particularly likely to influence endpoint values, reflecting the highly variable and impactful nature of preferences, attitudes and beliefs relating to mitigation strategies. Quantitative variables including numbers of livestock (particularly chickens) and income also had a high impact. The highest concordance (62%) was achieved with the BBN reported in this paper. Conclusions: This BBN model of SSI farming in Vietnam is helpful in understanding the complexity of small-scale agriculture and how various factors work in concert to influence contamination of on-farm drinking water as indicated by the presence of E. coli. The model will also be useful for identifying and estimating the impact of policy options such as improved delivery of clean water management training for rural areas, particularly where such analysis is combined with other analytical and policy tools. With appropriate knowledge translation, the model results will be particularly useful in helping SSI farmers understand their options for engaging in public health mitigation strategies addressing clean water that do not significantly disrupt their agriculture-based livelihoods.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle