1. Making useful climate-based predictions of malaria Dave MacLeod, Francesca di Giuseppe, Anne Jones, Cyril Caminade & Andy Morse

2. Introduction to malaria Weather & climate links to malaria Current global state and outlook Using seasonal climate forecasts to anticipate epidemics Results for Botswana

3. Malaria biology: malaria parasite (plasmodium) and vector (mosquito) Sporogonic cycle

4. Malaria dynamics depend on temperature # days for parasite to develop in mosquito (sporogonic cycle) Sporogonic cycle length > mosquito life cycle Mosquitos take more frequent blood meals (50% survive each blood meal: high temp = lower mosquito rates) Mosquito survival

5. Malaria dynamics depend on rainfall Egg to adult takes 10 days on average (gonotrophic cycle) Needs water!

6. 2015 statistics: 214m cases, 839,000 deaths (9 out of 10 in Africa) Since 2000: ~50% countries reduced incidence by >75% Malaria mortality decreased globaly by 60% Millenium Development Goal 6C “to have halted and begun to reverse the incidence of malaria” achieved [source: WHO World malaira report 2015] Current state of the world

7. 1900 2007 2007-1900 Endemicity class and change since pre-intervention (Gething et al 2010) Intervention works! Any increases in malaria due to climate change so far have been outweighed by impact of interventions & other factors But what about the future?

8. Projections for 2080 [Caminade et al 2014] Warm/cold colours indicate longer/shorter transmission Hatched area where models agreement on sign of change Unquantified uncertainties remain…

9. Outlook (personal opinion!) Continuation of anti-malaria initiatives can deal with increased risk from climate change (climate is just one factor) Far future is uncertain (runaway climate change? Parasite mutation?) Taking the shorter route (Washington et al 2006) Malaria epidemics are happening now! Adapt to climate-related changes by anticipating variability Use short-term forecasts to anticipate seasonal epidemics and mitigate the worst

10. Taking the shorter route - with seasonal climate forecasts Impossible to predict day-to-day changes beyond a week Slow fluctuations in surface conditions influence long-term average weather (e.g. El Niño)

11. Linking seasonal forecasts to malaria Seasonal forecasts indicate departures from normal temperature and precipitation, months in advance How to link temperature & precipitation anomalies to malaria?

12. Linking seasonal forecasts to malaria - the Liverpool Malaria Model

13. Validating climate-driven malaria forecasts Seasonal climate forecast + LMM = malaria forecast But how good is it? Hindcasting Forecast as if we were in the past Compare ‘forecast’ with observed data Repeat for all available observations Not a lot of season average malaria data! 1 data point per year Botswana data (Thomson, 2003) Clinical observed malaria cases, over January-May, 1982- 2003

14. Creating and validating climate-driven malaria forecasts - a recipe for Botswana 1.Create a seasonal climate forecast using System 4 (ECMWF seasonal climate model) – initialized separately at the start of every November 1981-2002 2.Use forecast precipitation & temperature to drive LMM 3.Take ‘# infected humans’ from LMM and average across January-May, and across Botswana 4.Compare with observed malaria cases (Jan-May 1982-2003)

15. Validation of seasonal forecasts over Botswana Observations System 4 seasonal forecast

16. Validation of seasonal malaria forecasts over Botswana Observations + LMM System 4 seasonal forecast + LMM Malaria incidence climatology

17. Validation of seasonal malaria forecasts over Botswana Forecast probability of higher than normal malaria incidence

18. Implications In the long term forecast we beat the house…but Impact of a forecast bust. Boy who cried wolf! Decisions to inform? Preplacement & allocation of resources, funding appeal Who takes responsibility? Less individual/institutional risk in playing it safe Imperfect data Uncertainty in validation ‘Invisible skill’: is the model doing things well which we can’t validate? e.g. timing of first outbreak of the season?

19. Recommendations More data! Better seasonal forecasts! Co-design: more involvement of end-users See MacLeod et al 2015 Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model, ERL, OPEN ACCESS Contact me: macleod@atm.ox.ac.uk