title: Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: NASA Goddard Institute for Space Studies, New York, New York, USA.
authors.forename: Carlos
authors.initials: C
authors.lastname: Pérez García-Pando
authors.forename: Michelle C
authors.initials: MC
authors.lastname: Stanton
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Diggle
authors.forename: Sylwia
authors.initials: S
authors.lastname: Trzaska
authors.forename: Ron L
authors.initials: RL
authors.lastname: Miller
authors.forename: Jan P
authors.initials: JP
authors.lastname: Perlwitz
authors.forename: José M
authors.initials: JM
authors.lastname: Baldasano
authors.forename: Emilio
authors.initials: E
authors.lastname: Cuevas
authors.forename: Pietro
authors.initials: P
authors.lastname: Ceccato
authors.forename: Pascal
authors.initials: P
authors.lastname: Yaka
authors.forename: Madeleine C
authors.initials: MC
authors.lastname: Thomson
mesh_terms: D000336:Aerosols / Q000032:analysis*; D002980:Climate; D004391:Dust / Q000032:analysis*; D005544:Forecasting; D006801:Humans; D015994:Incidence; D016014:Linear Models; D008585:Meningitis, Meningococcal / Q000453:epidemiology* / Q000382:microbiology; D015233:Models, Statistical; D009548:Niger / Q000453:epidemiology; D012621:Seasons; D012987:Soil; D014919:Wind
chemical_list: D000336:Aerosols; D004391:Dust; D012987:Soil