Context: In the village of M’pody in the Anyama district, a diarrhea epidemic was detected in January 2020. According to the affected population, these cases of diarrhea were related to the consumption of water from the improved village water system, which had not been maintained for nearly three years. The objective of this work was to develop a bacteriological characterization model of the water table in the village of M’pody based on physicochemical parameters and meteorology in order to estimate the concentration of Enterococcus faecalis by well.

Methods: The methodology consisted of four water sampling campaigns per well during the year’s four seasons on all 72 wells in this region, for a total of 288 visits. The development of the prediction model for the Enterococcus faecalis indicator was performed using a linear mixed model. The performance of our model was evaluated by bootstrap and k-fold cross-validation techniques.

Results: The mixed linear model with random slope chosen following likelihood ratio test gave the following results: The predictive model explained 49.67% of the variance in Enterococcus faecalis concentrations. It is based on 9 variables. Validation of the model performance by bootstrap gave us a very low relative bias < 5%, average prediction errors and absolute prediction errors per k-fold lower than 2.5.

Conclusion: This implemented model could be used in the event of a declaration of waterborne diseases in this locality prior to the return of the results of the microbiological analysis.

mixed linear model, bootstrap, principal component analysis.

Received: October 5, 2022; Accepted: November 15, 2022; Published: January 11, 2023

How to cite this article: Meless Djedjro Franck-Renaud, Lehot Laurent, Gbagbo Tchape Aubin, Kpaibe Sawa Andre Philippe, Yapo Toussaint Wolfgang, Kouassi-Agbessi Therese Brah and Amin N’cho Christophe, Prediction of concentrations of bacteria indicative of fecal pollution of the water table of the village M’pody of the riding of Anyama, JP Journal of Biostatistics 23(1) (2023), 29-49. http://dx.doi.org/10.17654/0973514323003

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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