Data prediction system in malaria control based on physio-chemical parameters of anopheles breeding sites




algorithms classification, larvae control, data analysis, data predictions, malaria, python


Malaria is a public health problem in Senegal. As a result, a real program focused on prevention and treatment has been put in place to fight it. Despite the efforts made, the prevalence rate of malaria is still worrying. To have a prediction system that, once certain physicochemical information, will inform if we can or not attend to the development of anopheles larvae. Our work consisted of collecting data on mosquito breeding sites, processing, and analyzing them in order to predict the physicochemical conditions for the development of Anopheles larvae. Larval control is an alternative to reduce the prevalence rate of malaria. We retain logistic regression as an algorithm and water electrical conductivity, water turbidity, temperature, and dissolved oxygen as determinant parameters. The learning and prediction system set up on the basis of the determining parameters and logistic regression worked. The predictions will be improved by further training our system with field data.


Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">


World Health Organization, World malaria report 2021. Geneva: World Health Organization, 2021. Consulted the: 12 May 2022. [Online]. Available on:

World Health Organization, World malaria report 2020: 20 years of global progress and challenges. Geneva: World Health Organization, 2020. Consulted the: 12 May 2022. [Online]. Available on:

U.S. President’s Malaria Initiative Senegal Malaria Operational Plan FY 2020. Retrieved from ( Consulted the: 13 may 2022. [Online]. Available on:

World Health Organization, Insecticide-treated nets for malaria transmission control in areas with insecticide-resistant mosquito populations: preferred product characteristics. Geneva: World Health Organization, 2021. Consulted the: 17 May 2022. [[Online]. Available on:

J. Mabrouki, M. Azrour, et S. E. Hajjaji, « Use of internet of things for monitoring and evaluating water’s quality: a comparative study », Int. J. Cloud Comput., vol. 10, no 5/6, p. 633, 2021, doi: 10.1504/IJCC.2021.120399. DOI:

Programme National de Lutte Contre Le Paludisme (PNLP), Plan Strategique National De Lutte Contre Le Paludisme Au Senegal 2021 - 2025. Consulted the: 12 may 2022. [Online]. Available on:

R. Poplin et al., « Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning », Nat. Biomed. Eng., vol. 2, no 3, p. 158‑164, March 2018, doi: 10.1038/s41551-018-0195-0. DOI:

P. Carnevale et V. Robert, Éd., « 2. Morphologie », in Les anophèles : Biologie, transmission du Plasmodium et lutte antivectorielle, Marseille: IRD Éditions, 2017, p. 22‑46. Consulted the: 21 May 2022. [Online]. Available on:

D. N. Burkett-Cadena, « Morphology of Adult and Larval Mosquitoes », p. 14.


N. Becker, Éd., Mosquitoes and their control, 2nd ed. Heidelberg: Springer, 2010.

A. N. Clements et A. N. Clements, Development, nutrition and reproduction, Print on demand ed. Wallingford: CABI Publ, 2008.

B. Ngom, M. Diallo, B. Gueye, et N. Marilleau, « LoRa-based Measurement Station for Water Quality Monitoring: Case of Botanical Garden Pool », in 2019 IEEE Sensors Applications Symposium (SAS), Sophia Antipolis, France, mars 2019, p. 1‑4. doi: 10.1109/SAS.2019.8705986. DOI:

M. R. Seye, B. Ngom, B. Gueye, et M. Diallo, « A Study of LoRa Coverage: Range Evaluation and Channel Attenuation Model », in 2018 1st International Conference on Smart Cities and Communities (SCCIC), Ouagadougou, juill. 2018, p. 1‑4. doi: 10.1109/SCCIC.2018.8584548. DOI:

« La régression logistique, qu’est-ce que c’est ? », Formation Data Science |, 4 novembre 2020. (Consulted the 19 May 2022).

« Forêts aléatoires de classification et de régression », XLSTAT, Your data analysis solution. (Consulted the 20 May 2022).

L. Rokach et O. Maimon, Data Mining with Decision Trees: Theory and Applications, 2e éd., vol. 81. WORLD SCIENTIFIC, 2014. doi: 10.1142/9097. DOI:

+Bastien L, « Réseau de neurones artificiels : qu’est-ce que c’est et à quoi ça sert ? »,, 5 avril 2019. (Consulted the 20 May 2022).

W. G. Baxt, « Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion », Neural Comput., vol. 2, no 4, p. 480‑489, déc. 1990, doi: 10.1162/neco.1990.2.4.480. DOI:

« Les Algorithmes de Naïves Bayes », Analytics & Insights, 1 March 2019. (Consulted the 20 May 2022).

S. C. Government of Canada, « 3.4.4 Imputation », 2 septembre 2021. (Consulted the 7 August 2022).

« Comment lire et exploiter une matrice de confusion ? », Formation Data Science |, 16 February 2021. (Consulted the May 2022).

C.-C. for D. C. and Prevention, « CDC - Malaria - Malaria Worldwide - How Can Malaria Cases and Deaths Be Reduced? - Larval Control and Other Vector Control Interventions », 16 July 2020. (Consulted the 5 October 2022).




How to Cite

K. M. Parkoo, B. Gueye, C. Sarr, and I. Dia, “Data prediction system in malaria control based on physio-chemical parameters of anopheles breeding sites”, EAI Endorsed Trans IoT, vol. 8, no. 4, p. e3, Dec. 2022.