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

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  • 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
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    References listed on IDEAS

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    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.
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