Team Location: International Research Institute for Climate and Society, Palisades, New York
Elisabeth Gawthrop (Columbia University)
Tam Tran (Columbia University)
Pietro Ceccato, IRI (Columbia University, International Research Institute for Climate and Society)
Madeleine Thomson, Ph.D. (Columbia University, International Research Institute for Climate and Society)
Carlos Perez, Ph.D. (Columbia University, NASA Goddard Institute Space Studies)
John del Corral (Columbia University, International Research Institute for Climate and Society)
Meningococcal meningitis (MM) is a bacterial cerebrospinal infection and has the highest rates of incidence in sub-Saharan Africa. The region most affected by MM stretches from Senegal in the west to Ethiopia in the east and is commonly referred to as the ÛÏMeningitis Belt.Û Epidemics occur in a given area every 7-14 years with average fatality rates of around 10 percent. Although many factors affect transmission, climate variables have demonstrated an influence on the timing of meningitis outbreaks. It has been hypothesized that low humidity and dust help convert benign meningitis bacteria into a pathogenic form that compromises the epithelial lining for the nasal cavity. The mechanism of transmission and the extent of the role of climate in affecting meningitis incidence intensity are still not well understood. Recent studies have shown correlations between MM incidence and such variables as maximum monthly temperature, relative humidity, and number of days with dust per month.åÊåÊ Understanding climatic influence will potentially allow for early identification of an increased risk of epidemics, thus allowing for more effective intervention.
The overall goal of this project was to understand how climate variables relate to meningitis incidence and, if a consistent relationship is validated, provide a map tool for decision-makers that can allow for more effective timing of mass vaccinations. The study first validated whether humidity data from the National Center for Environmental Prediction’s Non-hydrostatic Mesoscale Model (NCEP NMMB) closely corresponds to the National Center for Atmospheric Research Climate Data Assimiliation System I (NCAR CDAS-1) Reanalysis humidity data. The NCEP NMMB monthly absolute, specific, and relative humidity data were correlated with national annual meningitis incidence for six countries on the periphery of the Meningitis Belt. The same method was repeated with relative humidity from the NCAR CDAS-1 dataset. A similar method was used again to examine the relationship between MM incidence and NASA Moderate Resolution Imaging Spectroradiometer (MODIS) minimum and maximum land surface temperature (LST) and calculated maximum air temperature. To examine the relationships at a finer scale, the above humidity datasets were correlated with meningitis incidence in the Southern Nations, Nationalities and People’s Region (SNNPR) of Ethiopia at a weekly and monthly scale.
The correlation results indicate that while climate variables and meningitis incidence are related, the relationships appear to vary by location. A suitability map of climate conditions conducive to the incidence of meningitis across the entire Meningitis Belt may not be achievable, but certain areas may be able to use climate predictors, shown to be statistically significant to that area, to forecast a high-risk meningitis season. While climate variables do not explain all of the variation in MM incidence (factors such as vaccinations, population density, and travel patterns also influence the spread of MM), the results will assist the World Health Organization and Meningitis Environmental Risk Information Technologies (MERIT) project with decision-making. The project also further contributes to the current meningitis research on the dynamics of transmission and in the development of new warning and intervention control methods.