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Open AccessArticle

Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

1
Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali
2
Department of Mathematics, University of Quebec at Montreal (UQAM), Montréal, QC H2X 3Y7, Canada
3
Faculty of Health Sciences, University of Buea, Buea BP 63, Cameroon
4
Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Santiago de Compostela, 15782 Galicia, Spain
5
Department of Public Health Education and Research, Faculty of Medicine and Odonto-Stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako 1805, Mali
6
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
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Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street New Orleans, New Orleans, Louisiana, LA 70112, USA
8
Aix Marseille University, APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistics & ICT, 13005 Marseille, France
*
Author to whom correspondence should be addressed.
We dedicate the article in memory of Prof. Donald Krogstad, in acknowledgment of his life-long commitment to training and mentoring of African scientists in the fight against malaria. The West African International Center of Excellence in Malaria Research is his legacy.
Int. J. Environ. Res. Public Health 2020, 17(17), 6339; https://doi.org/10.3390/ijerph17176339
Received: 26 July 2020 / Revised: 19 August 2020 / Accepted: 26 August 2020 / Published: 31 August 2020
(This article belongs to the Special Issue Geo-Epidemiology of Malaria)
Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence. View Full-Text
Keywords: malaria; functional model; passive case detection; meteorological indicators; Mali malaria; functional model; passive case detection; meteorological indicators; Mali
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MDPI and ACS Style

Ateba, F.F.; Febrero-Bande, M.; Sagara, I.; Sogoba, N.; Touré, M.; Sanogo, D.; Diarra, A.; Magdalene Ngitah, A.; Winch, P.J.; Shaffer, J.G.; Krogstad, D.J.; Marker, H.C.; Gaudart, J.; Doumbia, S. Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models. Int. J. Environ. Res. Public Health 2020, 17, 6339. https://doi.org/10.3390/ijerph17176339

AMA Style

Ateba FF, Febrero-Bande M, Sagara I, Sogoba N, Touré M, Sanogo D, Diarra A, Magdalene Ngitah A, Winch PJ, Shaffer JG, Krogstad DJ, Marker HC, Gaudart J, Doumbia S. Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models. International Journal of Environmental Research and Public Health. 2020; 17(17):6339. https://doi.org/10.3390/ijerph17176339

Chicago/Turabian Style

Ateba, François F.; Febrero-Bande, Manuel; Sagara, Issaka; Sogoba, Nafomon; Touré, Mahamoudou; Sanogo, Daouda; Diarra, Ayouba; Magdalene Ngitah, Andoh; Winch, Peter J.; Shaffer, Jeffrey G.; Krogstad, Donald J.; Marker, Hannah C.; Gaudart, Jean; Doumbia, Seydou. 2020. "Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models" Int. J. Environ. Res. Public Health 17, no. 17: 6339. https://doi.org/10.3390/ijerph17176339

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