Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal
Bibliographic Data
| ID | 15514453 |
|---|---|
| Authors | Ousmane Diao (0000-0003-3598-9835, UCLouvain, corresponding author), P-A Absil, Mouhamadou Saliou Diallo (0000-0002-6514-0448, Cheikh Anta Diop University), Mouhamadou Diallo (Molecular Biology Unit/Bacteriology-Virology Lab, CNHU A. Le Dantec/Université Cheikh Anta Diop, Dakar Fann P.O. Box 5005, Senegal) |
| Year | 2023 |
| Volume | 20 |
| Issue | 13 |
| Pages | 6303-6303 |
| Publication date | 2023-07-05 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph20136303 |
| PMID | 37444150 |
| OpenAlex | W4383346745 |
| Language | EN |
| References cited | 20 |
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 t 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
Biology · Econometrics · Environmental health · Generalized linear model · Geography · Incidence (geometry · Linear regression · Malaria · Negative binomial distribution · Poisson distribution · Poisson regression · Population · Regression analysis · Statistics · COVID-19 epidemiological studies · Malaria Research and Control · Mathematics · Medicine
| Highly cited | No |
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