Diouf Ibrahima, Rodriguez-Fonseca Belen, Deme Abdoulaye, Caminade Cyril, Morse Andrew P, Cisse Moustapha, Sy Ibrahima, Dia Ibrahima, Ermert Volker, Ndione Jacques-André, Gaye Amadou Thierno
Laboratoire de Physique de l'Atmosphère et de l'Océan-Siméon Fongang, Ecole Supérieure Polytechnique de l'Université Cheikh Anta Diop (UCAD), BP 5085, Dakar-Fann, Dakar 10700, Senegal.
Department of Geophysics and Meteorology, Universidad Complutense de, Plaza de las Ciencias s/n, Madrid 28040, Spain.
Int J Environ Res Public Health. 2017 Sep 25;14(10):1119. doi: 10.3390/ijerph14101119.
The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models.
分析气候参数的时空变异性对于研究诸如疟疾等气候敏感型媒介传播疾病的影响至关重要。由于长期缺乏可靠的疟疾疫情观测数据，使用疟疾模型是生成塞内加尔潜在疟疾历史数据的一种替代方法。因此，我们在此使用由不同气候数据集驱动的利物浦疟疾模型（LMM），以研究和验证塞内加尔的模拟疟疾参数。研究结果证实，疟疾传播风险主要与降雨和温度等气候变量以及特定的景观特征有关。在整个塞内加尔，通常在8月降雨峰值与10月报告的疟疾病例数最大值之间观察到两个月的滞后。疟疾传播季节通常从9月持续到11月，与10月出现的第二个温度峰值相对应。将塞内加尔国家疟疾控制计划（PNLP）的观测疟疾数据与本研究中使用的气象数据输出进行了比较。疟疾模型输出与塞内加尔观测到的疟疾动态存在一些一致性，并且进一步允许探索使用再分析数据集在更长时间段内进行的模拟。在20世纪70年代和80年代，塞内加尔模拟的疟疾风险显著降低。这一结果与文献中实地昆虫学家和临床医生报告的疟疾媒介和疟疾病例的观测减少一致。模型输出与观测结果之间的主要差异在于幅度，但这不仅可能与再分析的缺陷有关，还可能与该机理疟疾模型框架中未包括的其他环境和社会经济因素有关。本研究可被视为对再分析可靠性的验证，该再分析可作为使用动态疟疾模型计算萨赫勒地区疟疾参数的输入。
