IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i13p6303-d1187439.html
   My bibliography  Save this article

Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal

Author

Listed:
  • Ousmane Diao

    (ICTEAM Institute, UCLouvain, B-1348 Louvain-la-Neuve, Belgium
    The first author is supported by a fellowship awarded by UCLouvain’s Conseil de l’action internationale.
    These authors contributed equally to this work.)

  • P.-A. Absil

    (ICTEAM Institute, UCLouvain, B-1348 Louvain-la-Neuve, Belgium
    These authors contributed equally to this work.)

  • Mouhamadou Diallo

    (Molecular Biology Unit/Bacteriology-Virology Lab, CNHU A. Le Dantec/Université Cheikh Anta Diop, Dakar Fann P.O. Box 5005, Senegal)

Abstract

Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical model for malaria incidence forecasting. In this study, we formulate a generalized linear model based on Poisson and negative binomial regression models for forecasting malaria incidence, taking into account climatic variables (such as the monthly rainfall, average temperature, relative humidity), other predictor variables (the insecticide-treated bed-nets (ITNs) distribution and Artemisinin-based combination therapy (ACT)) and the history of malaria incidence in Dakar, Fatick and Kedougou, three different endemic regions of Senegal. A forecasting algorithm is developed by taking the meteorological explanatory variable X j at time t − 𝓁 j , where t is the observation time and 𝓁 j is the lag in X j that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts.

Suggested Citation

  • Ousmane Diao & P.-A. Absil & Mouhamadou Diallo, 2023. "Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal," IJERPH, MDPI, vol. 20(13), pages 1-27, July.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:13:p:6303-:d:1187439
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/13/6303/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/13/6303/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eiji Nakashima, 1997. "Some Methods for Estimation in a Negative-Binomial Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(1), pages 101-115, March.
    2. Farid Saberi-Movahed & Mohammad Najafzadeh & Adel Mehrpooya, 2020. "Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 529-561, January.
    3. Felix Famoye, 2015. "A Multivariate Generalized Poisson Regression Model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(3), pages 497-511, February.
    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. Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
    2. Giulia Carallo & Roberto Casarin & Christian P. Robert, 2020. "Generalized Poisson Difference Autoregressive Processes," Papers 2002.04470, arXiv.org.
    3. Márton Gosztonyi & Csákné Filep Judit, 2022. "Profiling (Non-)Nascent Entrepreneurs in Hungary Based on Machine Learning Approaches," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    4. Sarni Maniar Berliana & Purhadi & Sutikno & Santi Puteri Rahayu, 2020. "Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression," Mathematics, MDPI, vol. 8(9), pages 1-14, September.
    5. Heelak Choi & Sang-Ik Suh & Su-Hee Kim & Eun Jin Han & Seo Jin Ki, 2021. "Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction," Sustainability, MDPI, vol. 13(19), pages 1-11, September.
    6. Umar, Muhammad & Sabir, Zulqurnain & Raja, Muhammad Asif Zahoor & Aguilar, J.F. Gómez & Amin, Fazli & Shoaib, Muhammad, 2021. "Neuro-swarm intelligent computing paradigm for nonlinear HIV infection model with CD4+ T-cells," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 241-253.
    7. Lluís Bermúdez & Dimitris Karlis, 2021. "Multivariate INAR(1) Regression Models Based on the Sarmanov Distribution," Mathematics, MDPI, vol. 9(5), pages 1-13, March.
    8. Ahmed M. Ebid & Ahmed Farouk Deifalla & Hisham A. Mahdi, 2022. "Evaluating Shear Strength of Light-Weight and Normal-Weight Concretes through Artificial Intelligence," Sustainability, MDPI, vol. 14(21), pages 1-49, October.
    9. Kutlu Onay, Funda, 2023. "A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 195-223.
    10. Rolf Larsson, 2020. "Discrete factor analysis using a dependent Poisson model," Computational Statistics, Springer, vol. 35(3), pages 1133-1152, September.
    11. Mohammad Bahrami Yarahmadi & Abbas Parsaie & Mahmood Shafai-Bejestan & Mostafa Heydari & Marzieh Badzanchin, 2023. "Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3563-3584, July.
    12. Purhadi & Sutikno & Sarni Maniar Berliana & Dewi Indra Setiawan, 2021. "Geographically weighted bivariate generalized Poisson regression: application to infant and maternal mortality data," Letters in Spatial and Resource Sciences, Springer, vol. 14(1), pages 79-99, April.

    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:20:y:2023:i:13:p:6303-:d:1187439. 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.