Pérez García-Pando Carlos, Stanton Michelle C, Diggle Peter J, Trzaska Sylwia, Miller Ron L, Perlwitz Jan P, Baldasano José M, Cuevas Emilio, Ceccato Pietro, Yaka Pascal, Thomson Madeleine C
NASA Goddard Institute for Space Studies, New York, New York, USA.
Environ Health Perspect. 2014 Jul;122(7):679-86. doi: 10.1289/ehp.1306640. Epub 2014 Mar 17.
Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea.
We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels.
We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January-May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data.
At the national level, a model using early incidence in December and averaged November-December zonal wind provided the best fit (pseudo-R2 = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R2 = 0.41).
We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic.
脑膜炎球菌性脑膜炎的疫情集中在撒哈拉以南非洲的旱季，在此期间，该地区受到哈马丹风的影响，这是一种从撒哈拉吹向几内亚湾的干燥多尘的东北信风。
我们研究了基于气候的统计预测模型预测尼日尔全国和地区层面脑膜炎季节性发病率的潜力。
我们使用了1986年至2006年期间尼日尔38个地区的脑膜炎发病率时间序列。我们基于业务框架中容易获取的数据测试模型，如气候和沙尘、人口以及1月至5月脑膜炎季节开始前早期病例的发病率。发病率被用作人群免疫状态、易感性和带菌情况的替代指标。我们比较了一系列拟合脑膜炎数据的负二项广义线性模型。
在国家层面，一个使用12月早期发病率和11月至12月平均纬向风的模型拟合效果最佳（伪R2 = 0.57），其中纬向风的影响最大。一个以地表沙尘浓度为预测变量的模型表现同样出色。在地区层面，最佳的时空模型包括纬向风、沙尘浓度、12月早期发病率和人口密度（伪R2 = 0.41）。
我们表明，风和沙尘信息以及旱季早期的发病率可预测尼日尔全国和地区层面脑膜炎季节性发病率的部分逐年变化情况。这种形式的模型可以提供早期季节警报，提示风、沙尘和其他条件可能有利于疫情爆发。
