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)
Mosquito survival
Sporogonic cycle length >
mosquito life cycle
Mosquitos take more
frequent blood meals
(50% survive each blood
meal: high temp = lower
mosquito rates)
Malaria dynamics depend on rainfall
Egg to adult takes 10 days on average
(gonotrophic cycle)
Needs water!
Current state of the world
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]
Endemicity class and change
since pre-intervention
1900
(Gething et al 2010)
Intervention works!
2007
Any increases in malaria
due to climate change so
far have been outweighed
by impact of interventions
& other factors
2007-1900
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
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Seasonal climate forecast + LMM = malaria forecast
But how good is it?
Hindcasting
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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, 19822003
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)
Validation of seasonal forecasts over Botswana
System 4 seasonal forecast
Observations
Validation of seasonal malaria forecasts over
Botswana
Malaria incidence climatology
System 4 seasonal forecast + LMM
Observations + LMM
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]