Meningitis Forecasting using Climate Information Tom Hopson

Utilizing Humidity Forecasts to Forecast Epidemics: WHO 2012-2013 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

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

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

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)

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

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

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

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