Risk maps of Lassa fever in West Africa.
Risk maps of Lassa fever in West Africa.
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DOI：
10.1371/journal.pntd.0000388
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发表时间：
2009
影响因子：
3.8
通讯作者：
Rogers, David John
中科院分区：
文献类型：
--
作者：
Fichet-Calvet, Elisabeth;Rogers, David John
拉沙热是由一种病毒性出血性沙粒病毒引起的，影响西非200万至300万人，每年造成5,000至10,000人死亡。拉沙病毒的天然宿主是多哺乳动物大鼠Mastomys natalensis，它生活在房屋和周围的田地里。为了获得更多的信息来控制这种疾病，我们在这里进行了拉沙热的数据从人类病例和感染啮齿动物宿主涵盖1965-2007年期间的空间分析。关于当代环境条件（温度、降雨量、植被）的信息来自美国航天局Terra中分辨率成像光谱仪卫星传感器数据和其他来源，以及从塞内加尔到刚果地区GTOPO 30地表的高程。使用时间傅立叶技术分析所有多时相数据，以生成均值、振幅和相位的图像，这些图像用作模型中的预测变量。此外，1951年至1989年期间收集的气象降雨量数据被用来生成同一地区的天气降雨面。三种不同的分析（模型），一个叠加拉沙热暴发的平均降雨面（模型1）和其他两个使用非线性判别分析技术。模型2以逐步包容的方式选择变量，模型3使用信息理论方法，其中将10个变量的许多不同随机组合拟合到拉沙热数据中。在模型2和模型3中使用了三种不存在：存在聚类的组合，2不存在：1存在聚类组合给出了似乎是最好的结果。模型1显示，记录到的拉沙热在人群中的暴发发生在年降雨量在1 500至3 000毫米之间的地区。在模型2和模型3中，降雨量和较小程度上的温度变量都是最强有力的选择，植被和海拔似乎都不是特别重要。模型2和模型3均产生了超过0.91（模型2）或0.86（模型3）的平均kappa值，使其成为“顺从性”。该模型预测的拉沙热疫区覆盖了塞拉利昂和利比里亚各约80%的地区，几内亚50%的地区，尼日利亚40%的地区，科特迪瓦、多哥和贝宁各30%的地区，以及加纳10%的地区。此前对西非几内亚拉沙热生态流行病学的研究表明，雨季水库感染拉沙病毒的人数是旱季的两到三倍。鼠种群的内在变量，如丰度或繁殖，都不能解释这种流行率的季节性变化。因此，我们在这里调查的重要性，外部环境变量，部分影响的想法，在欧洲的情况下，肾病的环境污染，因此生存的病原体外的主机，似乎是一个重要因素，这种疾病的流行病学。因此，我们对文献进行了广泛的回顾，收集了有关拉沙热已被确定的地点的地理位置的信息。这些地点的环境数据（降雨量、温度、植被和海拔）是从卫星和地面气象站等各种来源收集的。几个统计处理被应用到制作拉萨“风险地图”。这些地图都表明，在确定高风险地区时，降雨量的影响很大，温度的影响较小。风险最大的地区位于几内亚和喀麦隆之间。
Lassa fever is caused by a viral haemorrhagic arenavirus that affects two to three million people in West Africa, causing a mortality of between 5,000 and 10,000 each year. The natural reservoir of Lassa virus is the multi-mammate rat Mastomys natalensis, which lives in houses and surrounding fields. With the aim of gaining more information to control this disease, we here carry out a spatial analysis of Lassa fever data from human cases and infected rodent hosts covering the period 1965–2007. Information on contemporary environmental conditions (temperature, rainfall, vegetation) was derived from NASA Terra MODIS satellite sensor data and other sources and for elevation from the GTOPO30 surface for the region from Senegal to the Congo. All multi-temporal data were analysed using temporal Fourier techniques to generate images of means, amplitudes and phases which were used as the predictor variables in the models. In addition, meteorological rainfall data collected between 1951 and 1989 were used to generate a synoptic rainfall surface for the same region. Three different analyses (models) are presented, one superimposing Lassa fever outbreaks on the mean rainfall surface (Model 1) and the other two using non-linear discriminant analytical techniques. Model 2 selected variables in a step-wise inclusive fashion, and Model 3 used an information-theoretic approach in which many different random combinations of 10 variables were fitted to the Lassa fever data. Three combinations of absence∶presence clusters were used in each of Models 2 and 3, the 2 absence∶1 presence cluster combination giving what appeared to be the best result. Model 1 showed that the recorded outbreaks of Lassa fever in human populations occurred in zones receiving between 1,500 and 3,000 mm rainfall annually. Rainfall, and to a much lesser extent temperature variables, were most strongly selected in both Models 2 and 3, and neither vegetation nor altitude seemed particularly important. Both Models 2 and 3 produced mean kappa values in excess of 0.91 (Model 2) or 0.86 (Model 3), making them ‘Excellent’. The Lassa fever areas predicted by the models cover approximately 80% of each of Sierra Leone and Liberia, 50% of Guinea, 40% of Nigeria, 30% of each of Côte d'Ivoire, Togo and Benin, and 10% of Ghana. Previous studies on the eco-epidemiology of Lassa fever in Guinea, West Africa, have shown that the reservoir is two to three times more infected by Lassa virus in the rainy season than in the dry season. None of the intrinsic variables of the murine population, such as abundance or reproduction, was able to explain this seasonal variation in prevalence. We therefore here investigate the importance of extrinsic environmental variables, partly influenced by the idea that in the case of nephropathia epidemica in Europe contamination of the environment, and therefore survival of the pathogen outside the host, appears to be an important factor in this disease's epidemiology. We therefore made an extensive review of the literature, gathering information about the geographical location of sites where Lassa fever has been certainly identified. Environmental data for these sites (rainfall, temperature, vegetation and altitude) were gathered from a variety of sources, both satellites and ground-based meteorological stations. Several statistical treatments were applied to produce Lassa ‘risk maps’. These maps all indicate a strong influence of rainfall, and a lesser influence of temperature in defining high risk areas. The area of greatest risk is located between Guinea and Cameroon.
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DOI：
10.4269/ajtmh.1970.19.670
发表时间：
1970-01-01
影响因子：
3.3
作者：
FRAME, JD;BALDWIN, JM;TROUP, JM
通讯作者：
TROUP, JM
DOI：
10.1016/0035-9203(85)90242-1
发表时间：
1985-01-01
影响因子：
2.2
作者：
GEORGES, AJ;GONZALEZ, JP;MCCORMICK, JB
通讯作者：
MCCORMICK, JB
影响因子：
--
作者：
FISHERHOCH, SP;TOMORI, O;MCCORMICK, JB
通讯作者：
MCCORMICK, JB
DOI：
10.1016/0035-9203(72)90271-4
发表时间：
1972-01-01
影响因子：
2.2
作者：
CAREY, DE;CASALS, J;KEMP, GE
通讯作者：
KEMP, GE
DOI：
10.1073/pnas.252617999
发表时间：
2002-12-24
影响因子：
11.1
作者：
Glass, GE;Yates, TL;Mills, JN
通讯作者：
Mills, JN