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

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  • François Freddy Ateba

    (Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali
    Department of Mathematics, University of Quebec at Montreal (UQAM), Montréal, QC H2X 3Y7, Canada
    Faculty of Health Sciences, University of Buea, Buea BP 63, Cameroon)

  • Manuel Febrero-Bande

    (Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Santiago de Compostela, 15782 Galicia, Spain)

  • Issaka Sagara

    (Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali
    Department of Public Health Education and Research, Faculty of Medicine and Odonto-Stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako 1805, Mali)

  • Nafomon Sogoba

    (Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali)

  • Mahamoudou Touré

    (Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali)

  • Daouda Sanogo

    (Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali)

  • Ayouba Diarra

    (Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali)

  • Andoh Magdalene Ngitah

    (Faculty of Health Sciences, University of Buea, Buea BP 63, Cameroon)

  • Peter J. Winch

    (Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA)

  • Jeffrey G. Shaffer

    (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)

  • Donald J. Krogstad

    (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
    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.)

  • Hannah C. Marker

    (Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA)

  • Jean Gaudart

    (Aix Marseille University, APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistics & ICT, 13005 Marseille, France)

  • Seydou Doumbia

    (Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali
    Department of Public Health Education and Research, Faculty of Medicine and Odonto-Stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako 1805, Mali)

Abstract

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.

Suggested Citation

  • François Freddy Ateba & Manuel Febrero-Bande & Issaka Sagara & Nafomon Sogoba & Mahamoudou Touré & Daouda Sanogo & Ayouba Diarra & Andoh Magdalene Ngitah & Peter J. Winch & Jeffrey G. Shaffer & Donald, 2020. "Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models," IJERPH, MDPI, vol. 17(17), pages 1-16, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6339-:d:406616
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    References listed on IDEAS

    as
    1. Febrero-Bande, Manuel & de la Fuente, Manuel Oviedo, 2012. "Statistical Computing in Functional Data Analysis: The R Package fda.usc," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i04).
    2. Manuel Febrero-Bande & Wenceslao González-Manteiga, 2013. "Generalized additive models for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 278-292, June.
    3. Manuel Oviedo de la Fuente & Manuel Febrero-Bande & María Pilar Muñoz & Àngela Domínguez, 2018. "Predicting seasonal influenza transmission using functional regression models with temporal dependence," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-18, April.
    4. François Freddy Ateba & Issaka Sagara & Nafomon Sogoba & Mahamoudou Touré & Drissa Konaté & Sory Ibrahim Diawara & Séidina Aboubacar Samba Diakité & Ayouba Diarra & Mamadou D. Coulibaly & Mathias Dolo, 2020. "Spatio-Temporal Dynamic of Malaria Incidence: A Comparison of Two Ecological Zones in Mali," IJERPH, MDPI, vol. 17(13), pages 1-21, June.
    5. Müller, Hans-Georg & Yao, Fang, 2008. "Functional Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1534-1544.
    6. Manuel Febrero-Bande & Wenceslao González-Manteiga & Manuel Oviedo de la Fuente, 2019. "Variable selection in functional additive regression models," Computational Statistics, Springer, vol. 34(2), pages 469-487, June.
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