IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i17p6339-d406616.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/17/6339/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/17/6339/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Müller, Hans-Georg & Yao, Fang, 2008. "Functional Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1534-1544.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Febrero-Bande, Manuel & González-Manteiga, Wenceslao & Prallon, Brenda & Saporito, Yuri F., 2023. "Functional classification of bitcoin addresses," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    3. Shang, Han Lin, 2013. "Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 185-198.
    4. Kehui Chen & Xiaoke Zhang & Alexander Petersen & Hans-Georg Müller, 2017. "Quantifying Infinite-Dimensional Data: Functional Data Analysis in Action," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 582-604, December.
    5. Philip T. Reiss & Jeff Goldsmith & Han Lin Shang & R. Todd Ogden, 2017. "Methods for Scalar-on-Function Regression," International Statistical Review, International Statistical Institute, vol. 85(2), pages 228-249, August.
    6. Manuel Febrero-Bande, 2016. "Comments on: Probability enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 35-40, March.
    7. J. A. Cuesta-Albertos & M. Febrero-Bande & M. Oviedo de la Fuente, 2017. "The $$\hbox {DD}^G$$ DD G -classifier in the functional setting," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 119-142, March.
    8. Chiou, Jeng-Min & Yang, Ya-Fang & Chen, Yu-Ting, 2016. "Multivariate functional linear regression and prediction," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 301-312.
    9. Merve Yasemin Tekbudak & Marcela Alfaro-Córdoba & Arnab Maity & Ana-Maria Staicu, 2019. "A comparison of testing methods in scalar-on-function regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 411-436, September.
    10. Laura Freijeiro‐González & Manuel Febrero‐Bande & Wenceslao González‐Manteiga, 2022. "A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates," International Statistical Review, International Statistical Institute, vol. 90(1), pages 118-145, April.
    11. Manuel Febrero-Bande, 2016. "Comments on: Probability enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 35-40, March.
    12. Manuel Oviedo de la Fuente & Carlos Cabo & Javier Roca-Pardiñas & E. Louise Loudermilk & Celestino Ordóñez, 2024. "3D Point Cloud Semantic Segmentation Through Functional Data Analysis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 723-744, December.
    13. Ufuk Beyaztas & Han Lin Shang, 2021. "A partial least squares approach for function-on-function interaction regression," Computational Statistics, Springer, vol. 36(2), pages 911-939, June.
    14. Manuel Oviedo-de la Fuente & Carlos Cabo & Celestino Ordóñez & Javier Roca-Pardiñas, 2021. "A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification," Mathematics, MDPI, vol. 9(12), pages 1-19, June.
    15. Huang, Lele & Zhao, Junlong & Wang, Huiwen & Wang, Siyang, 2016. "Robust shrinkage estimation and selection for functional multiple linear model through LAD loss," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 384-400.
    16. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2020. "Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA," SoDa Laboratories Working Paper Series 2020-04, Monash University, SoDa Laboratories.
    17. Carlo Sguera & Pedro Galeano & Rosa Lillo, 2014. "Spatial depth-based classification for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 725-750, December.
    18. Boente, Graciela & Parada, Daniela, 2023. "Robust estimation for functional quadratic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    19. Kosiorowski Daniel & Mielczarek Dominik & Rydlewski Jerzy P. & Snarska Małgorzata, 2018. "Generalized Exponential Smoothing In Prediction Of Hierarchical Time Series," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 331-350, June.
    20. Miguel Flores & Salvador Naya & Rubén Fernández-Casal & Sonia Zaragoza & Paula Raña & Javier Tarrío-Saavedra, 2020. "Constructing a Control Chart Using Functional Data," Mathematics, MDPI, vol. 8(1), pages 1-26, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6339-:d:406616. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.