NOAA Center for Weather and Climate Prediction, College Park, Maryland.
Laboratoire de Physique de L'Atmosphère et de L'Océan-Siméon Fongang, Ecole Supérieure Polytechnique de L'Université Cheikh Anta Diop, Dakar, Sénégal.
Am J Trop Med Hyg. 2020 May;102(5):1037-1047. doi: 10.4269/ajtmh.19-0062.
Malaria is a major public health problem in West Africa. Previous studies have shown that climate variability significantly affects malaria transmission. The lack of continuous observed weather station data and the absence of surveillance data for malaria over long periods have led to the use of reanalysis data to drive malaria models. In this study, we use the Liverpool Malaria Model (LMM) to simulate spatiotemporal variability of malaria in West Africa using daily rainfall and temperature from the following: Twentieth Century Reanalysis (20th CR), National Center for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Twentieth Century (ERA20C), and interim ECMWF Re-Analysis (ERA-Interim). Malaria case data from the national surveillance program in Senegal are used for model validation between 2001 and 2016. The warm temperatures found over the Sahelian fringe of West Africa can lead to high malaria transmission during wet years. The rainfall season peaks in July to September over West Africa and Senegal, and the malaria season lasts from September to November, about 1-2 months after the rainfall peak. The long-term trends exhibit interannual and decadal variabilities. The LMM shows acceptable performance in simulating the spatial distribution of malaria incidence. However, some discrepancies are found. These results are useful for decision-makers who plan public health and control measures in affected West African countries. The study would have substantial implications for directing malaria surveillance activities and health policy. In addition, this malaria modeling framework could lead to the development of an early warning system for malaria in West Africa.
疟疾是西非主要的公共卫生问题。先前的研究表明,气候变异性对疟疾传播有重大影响。由于缺乏连续的观测气象站数据和长期的疟疾监测数据,因此使用再分析数据来驱动疟疾模型。在这项研究中,我们使用利物浦疟疾模型(LMM),使用以下来源的每日降雨量和温度来模拟西非的疟疾时空变异性:二十世纪再分析(20th CR)、美国国家环境预报中心(NCEP)、欧洲中期天气预报中心(ECMWF)二十世纪大气再分析(ERA20C)和临时 ECMWF 再分析(ERA-Interim)。我们使用塞内加尔国家监测计划的疟疾病例数据,在 2001 年至 2016 年期间对模型进行验证。西非萨赫勒边缘地区的高温可能导致湿润年份的疟疾传播率很高。西非和塞内加尔的降雨季节峰值出现在 7 月至 9 月,疟疾季节从 9 月持续到 11 月,大约在降雨高峰期后 1-2 个月。长期趋势表现出年际和年代际变化。LMM 在模拟疟疾发病率的空间分布方面表现出良好的性能。然而,也发现了一些差异。这些结果对规划受影响的西非国家公共卫生和控制措施的决策者有用。该研究将对指导疟疾监测活动和卫生政策产生重大影响。此外,这种疟疾建模框架可以为西非的疟疾预警系统的发展提供帮助。