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

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...

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Bibliographic Details
Published in:International journal of environmental research and public health 2023-07, Vol.20 (13), p.6303
Main Authors: Diao, Ousmane, Absil, P-A, Diallo, Mouhamadou
Format: Article
Language:English
Subjects:
Artemisinin
Binomial distribution
Business metrics
Climate
Climate change
Forecasting
Generalized linear models
Humans
Humidity
Incidence
Insecticides
Linear Models
Machine learning
Malaria
Malaria - epidemiology
Mathematical models
Methods
Models, Statistical
Parameter estimation
Parasites
Poisson density functions
Public health
Rain
Rainfall
Regression analysis
Relative humidity
Senegal - epidemiology
Statistical analysis
Statistical models
Temperature
Variables
Vector-borne diseases
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Summary: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 Xj at time t-lj, where is the observation time and lj is the lag in Xj 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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph20136303