1. Meningitis Forecasting using Climate Information Tom Hopson

2. Utilizing Humidity Forecasts to Forecast Epidemics: WHO Outbreak Prediction Exercise for Benin, Chad, Nigeria, Togo (this year Ivory Coast, Senegal)
Several meteorological centers produce global forecasts that can be used to estimate future humidity 2 weeks in-advance
Available through THORPEX-TIGGE archive
These forecasts need to be corrected, slightly
we can do this using past forecasts and observations via “quantile regression”
The many ensemble forecasts themselves can also be used to estimate the uncertainty in the prediction

3. Unique Datasets/Software Created
Thorpex-Tigge
UKMO
CMC
CMA
ECMWF
MeteoFrance
NCAR
NCEP
JMA
NCDC
KMA
IDD/LDM
HTTP
FTP
Green dots show forecasting centers where we used forecasts from
Archive Centre
CPTEC
Current Data Provider
BoM
Unidata IDD/LDM
Internet Data Distribution / Local Data Manager
Commodity internet application to send and receive data

4. Relative humidity improves prediction
Knowing the RH two weeks ago improves accuracy in predicting an epidemic by ~25%1
Coupled with a two week forecast, this indicates an improved ability to anticipate a roll-off in epidemic 4 weeks in advance
Using RH
Most weather variables by themselves had significance in forecasting changes in case likelihood (in particular, vapor pressure and vapor pressure/airT [number density measure]); only when used in conjunction with all other variable they weren’t found to be useful
Without RH
1It turns out other variables (air temp, winds, NE winds) also help, but less than relative humidity

5. Relative Humidity linkage
across the Meningitis
Belt forecast 2 week
in advance …
Within dashed region,
risk has doubled
(above background levels)
… converted to
probability of an
epidemic alert occurring
across the Belt
4 weeks in advance
National Center for Atmospheric Research (NCAR)

6. Countries provided forecasts
Countries in ovals participated in the WHO weekly teleconferences

7. Web-based display
This is a screenshot of the GUI that we created (via Arnaud) as part of our project.

8. End-of-season date (risk less than “climatological” risk)
Movie shows the change in the “end of season” date for different years, pointing out the variability in this date and thus the utility of having year to year forecasts of what this might be for optimizing meningitis vaccine distribution – if you like this movie, I’ll modify a bit to make clearer and send back
Day of Year

9. Locations where end-of-season forecasts would have saved vaccine
Locations shown are WHO districts where end-of-season forecasts would have argued that a vaccination campaign would not have been required due to changing weather conditions. As a result, this would have saved roughly 2.6 million doses of vaccine that could have been more effectively positioned elsewhere around the meningitis belt if weather information, in addition to standard WHO protocol, were factored into the decision making process.
Day of Year