1. 1 Modelling malaria in Africa driven by DEMETER forecasts Anne Jones Department of Geography University of Liverpool Liverpool UK anne.jones@liv.ac.uk NERC e-science studentship Supervisor: Dr Andy Morse ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

2. 2 Overview Climate and malaria Liverpool malaria model Predicting malaria in Botswana using DEMETER forecasts Discussion of results Model spin up Bias correction Timing issues Conclusions ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

3. 3 Image from The Wellcome Trust Anopheles Life Cycle and Climate Anopheles breeding sites include cattle footprints, water tanks and rice fields ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

4. 4 ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk Image from The Wellcome Trust Anopheles Life Cycle and Climate The adult biting/laying cycle and survivorship depend on temperature Gonotrophic cycle length according to Detinova (1962) Mosquito survival according to Craig et al (1999)

5. 5 Parasite Life Cycle and Climate Temperature 18 °C ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

6. 6 Structure of the Liverpool Malaria Model (LMM) Mosquito population Malaria transmission - mosquito Malaria transmission - human Daily Malaria incidence (number of new cases) and prevalence (proportion of population infected) 10 day rainfall daily temperature daily temperature humidity (10 day rainfall) daily temperature Input data: station or gridded datasets (ERA-40, DEMETER) ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

7. 7 Botswana Malaria MARA map (Craig et al, 1999) Tmin Rain Tmean MARA run with ERA-40 1 deg (1958-2001) MARA limiting variable (Tmin is min of 4 daily values) ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

8. 8 Malaria Anomalies Malaria index of Thomson et al. (2005) and ERA-40-driven LMM yearly total incidence. Thomson et al. found a quadratic relationship between malaria and rainfall

9. 9 Seasonal Forecasts Assessed performance of forecasts made using LMM driven by DEMETER forecasts. Compared to ERA-40 driven forecasts (tier-2 validation) and yearly malaria anomalies (tier-3 validation) DEMETER daily rainfall and temperature series were corrected for model biases then used as input to the malaria model. Botswana malaria forecast for February 1989, LMM driven by DEMETER multi-model (ERA-driven model shown in red) 95 85 65 35 15 5 ERA ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

10. 10 Tier-3 ROC Areas for November malaria forecast ROC Area (<0.5 = no skill), Upper Tercile event, forecast 6 month totals for Botswana grid average, () 95% confidence intervals calculated from 1000 bootstrap samples Validated against Thomson et al (2005) Malaria Index LMM Input DataROC Area ERA-40 0.78 (0.56-0.97) Raw DEMETER 0.31 (0.11-0.56) Bias-corrected DEMETER T correction only 0.67 (0.29-0.80) 0.70 (0.35-0.90) ERA-40 "persistence" (wrong year ensemble) 0.32 (0.07-0.63)

11. 11 Effect of temperature bias correction Temperature variability not a strong driver of malaria variability in this region However malaria model requires realistic temperatures DEMETER temperatures need to be bias corrected to achieve this, because models is sensitive to biases in uncorrected data of ~ 2 degrees DEMETER temperature forecasts for Botswana, November 1997 95 85 65 35 15 5 ERA Uncorrected Temperature Corrected Temperature ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

12. 12 ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk Improvement in skill due to temperature correction If temperatures too low, delay in model is increased Uncorrected input Corrected temperature DEMETER-driven malaria forecasts for November 1997 95 85 65 35 15 5 ERA Effect of temperature bias correction contd.

13. 13 Tier-1 ROC Areas for November rainfall forecast EventUncorrectedCorrected Lower Tercile 0.88 (0.67-1.0) 0.75 (0.45-1.0) Above the median 0.65 (0.37-0.89) 0.68 (0.39-0.92) Upper Tercile 0.72 (0.46-0.93) 0.63 (0.37-0.86) ROC Area (<0.5 = no skill),forecast 6 month totals for Botswana grid average, () 95% confidence intervals calculated from 1000 bootstrap samples Validated against ERA-40 rainfall totals Effect of rainfall bias correction Bias correction of rainfall causes decrease in skill ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

14. 14 Correct for frequency and intensity separately If distributions are very different, large numbers of rainfall days are removed Correction to mean climate but reduction in skill - need alternative method Effect of rainfall bias correction contd. model crfc, daily rainfall over 20 years for Feb at 25E, 22.5S = ERA-40, = uncorr. DEM, = corr. DEM ERA-40 Crfc model November rainfall totals in mm for all grid points in Botswana over 20 years Daily rainfall (mm) Cumulative frequency ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

15. 15 Timing Issues Botswana grid-averaged values for November 1988 forecast Bias corrected data Rainfall LMM Mosquitoes LMM Malaria Bad/lack of bias correction means rainfall can be too low Model peak can be outside the forecast window due to lag between rainfall and malaria cases (made worse when rainfall too low) Skill not improved by extending forecast window with previous year of ERA data

16. 16 Tier-3 ROC Areas - alternative model outputs LMM Input DataLMM Malaria Anomalies LMM Mosquito Anomalies ERA-40 0.78 (0.56-0.97) 0.89 (0.71-1.0) Raw DEMETER 0.31 (0.11-0.56) 0.73 (0.42-0.97) Bias corrected DEMETER 0.67 (0.29-0.80) 0.56 (0.42-0.91) ROC Area (<0.5 = no skill), Upper Tercile event, November forecast 6 month totals for Botswana grid average, () 95% confidence intervals calculated from 1000 bootstrap samples Validated against Thomson et al (2005) Malaria Index Skill improved by using model mosquito numbers Bias correction decreases skill due to strong rainfall driver Cannot use in other areas where temperature a stronger driver

17. 17Conclusions DEMETER-driven forecasts were skilful, better than climatology and persistence forecasts Bias correction of temperature is important even if variability in temperature not important - temperatures must be "realistic" for the application model Bias correction of rainfall is unsatisfactory - use of daily rainfall output is problematic and need to consider other methods using monthly anomalies instead (e.g. weather generator) Lag in model mean malaria cases may occur outside forecast window - can be solved for Botswana using mosquito model but not applicable to other areas ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk

18. 18 Thankyou © L Anderson-Ptito, RBM Partnership Secretariat ECMWF Forecast User Group Meeting, June 2006 anne.jones@liv.ac.uk