making useful climate-based predictions of malaria dave macleod, francesca di giuseppe, anne jones,...
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Malaria biology: malaria parasite (plasmodium) and vector (mosquito) Sporogonic cycleTRANSCRIPT
Making useful climate-based predictions of malaria
Dave MacLeod, Francesca di Giuseppe, Anne Jones, Cyril Caminade & Andy Morse
Introduction to malaria
Weather & climate links to malaria
Current global state and outlook
Using seasonal climate forecasts to anticipate epidemics
Results for Botswana
Malaria biology: malaria parasite (plasmodium) and vector (mosquito)
Sporogonic cycle
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
Malaria dynamics depend on rainfall
Egg to adult takes 10 days on average (gonotrophic cycle)
Needs water!
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
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?
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…
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
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)
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?
Linking seasonal forecasts to malaria- the Liverpool Malaria Model
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
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 LMM3. Take ‘# infected humans’ from LMM and average across
January-May, and across Botswana4. Compare with observed malaria cases (Jan-May 1982-2003)
Validation of seasonal forecasts over Botswana
ObservationsSystem 4 seasonal forecast
Validation of seasonal malaria forecasts over Botswana
Observations + LMMSystem 4 seasonal forecast + LMM
Malaria incidence climatology
Validation of seasonal malaria forecasts over Botswana
Forecast probability of higher than normal malaria incidence
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?
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: [email protected]