ICTEAM Institute, UCLouvain, B-1348 Louvain-la-Neuve, Belgium.
Molecular Biology Unit/Bacteriology-Virology Lab, CNHU A. Le Dantec/Université Cheikh Anta Diop, Dakar Fann P.O. Box 5005, Senegal.
Int J Environ Res Public Health. 2023 Jul 5;20(13):6303. doi: 10.3390/ijerph20136303.
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.
每年影响数百万人，疟疾是最危险和致命的热带疾病之一。它是一个主要的全球公共卫生问题，寄生虫通过蚊子（疟蚊属）传播的情况令人震惊。已经出现了各种研究，构建了疟疾发病率预测的数学和统计模型。在这项研究中，我们基于泊松和负二项回归模型制定了一个广义线性模型，以预测达喀尔、法蒂克和盖迪奥三个塞内加尔不同流行地区的疟疾发病率，考虑到气候变量（如每月降雨量、平均温度、相对湿度）、其他预测变量（经杀虫剂处理的蚊帐（ITN）分布和青蒿素为基础的联合疗法（ACT））以及疟疾发病率的历史。通过在气象解释变量 Xj 在时间 t-𝓁j 时采取预测算法，其中 是观察时间， 𝓁j 是 Xj 的滞后时间，它与疟疾发病率的相关性最大。我们饱和了降雨量，以减少过度预测。这项研究的结果表明，泊松回归模型比负二项回归模型更适合在考虑一些解释变量的情况下准确预测疟疾发病率。在观察到过度预测的情况下应用饱和度明显提高了预测的质量。
