Experimental Prediction of Climaterelated Malaria Incidence

1
Experimental Prediction of Climate-related
Malaria Incidence

• T.N. Krishnamurti and Arindam Chakraborty
• Florida State University
• Tallahassee, Florida, U.S.A.
• Vikram M. Mehta
• The Center for Research on the Changing Earth
  System
• Columbia, Maryland, U.S.A.
• Amita V. Mehta
• NASA-Goddard Space Flight Center
• and
• University of Maryland-Baltimore County
• Greenbelt, Maryland, U.S.A.

Outline Climate Variability and Malaria in
India The FSU Super-ensemble Technique with 13
Coupled Climate Models for Rainfall
Prediction An Experiment for Malaria Incidence
Prediction in Botswana, Southern Africa Next
Steps for a Malaria Early Warning System in
(Western) India
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Poverty and Health Malaria, an Example of
Vector-borne Diseases Influenced by Climate
Variability and Change

• Malaria around for 4,000 years, influenced human
  history to a great extent
• According to the WHOs World Malaria Report 2005,
  3.2 billion people lived in areas at risk of
  malaria transmission at the end of 2004
• 350 to 500 million clinical episodes of malaria
  every year
• At least one million deaths every year due to
  malaria
• Potential destabilization of socio-economic-polit
  ical systems, triggering national/international
  security problems

3
Influence of El Niño-La Niña Climate Variability
on Indian Rainfall and Malaria Incidence
3 million cases
El Niño-La Niña Climate Index (gray) and
annual number of malaria cases in India (blue)
March-April- May
1.8 million cases
June-July- August
1990 1991 1992 1993 1994
1995 1996 1997 1998 1999
2000
Jun-Jul-Aug 1996 La Niña
Jun-Jul-Aug 1998 El Niño

• More rain and more malaria cases in western and
  northwestern India during La Niña (1996 left)
• Less rain and fewer malaria cases in western and
  northwestern India during El Niño (1998 right)
• Rain and malaria prediction 2-3 months in advance
  possible

4
Seasonal Rainfall and Malaria Incidence
Prediction in Botswana in Southern Africa A Case
Study

• Malaria incidence dependent on rainfall,
  temperature, humidity, winds, land cover-use,
  topography, and other local conditions
• Accurate seasonal prediction of rainfall,
  temperature, other hydro-meteorological
  variables, and land cover-use very useful for
  early warning of malaria risk and decision-making
  about prevention/mitigation

Application of the FSU multi-model synthetic
super-ensemble technique with 13 coupled climate
models in predicting malaria incidence a season
in advance in Botswana, using only rainfall
prediction
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Particulars of 13 Coupled Climate Models
6
Total Forecast Data Sets Available for the
Present Study
Total Forecasts 23400 ( 468 x 5 1404 2808
x 7)
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Methodology

• Seasonal, super-ensemble rainfall forecasts
  from 13 coupled atmosphere-ocean models from
  March 1989 to February 2002 over 17.5o-30.0oE,
  27.5oS-17.5oS
• Three months lead time forecasts for the peak
  malaria incidence month of March

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Adjusted Malaria Incidence and Rainfall December
to February 1981-82 to 2001-02
Adjusted log (malaria incidence) (AMI, per
1000 people) in Botswana related to summer
(December-February) rainfall (and other
factors) Initial increase in AMI with increase
in rainfall and decrease in very heavy rainfall
because mosquito breeding areas washed away
Empirical relationship between AMI and rainfall P
(mm/day) in Botswana AMI -0.2541 P2 1.9558 P
- 3.2823
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Step 1 December Forecast of December- January-Fe
bruary Rainfall
Rainfall Forecasts made in December for
December-January-February Season
using Super-ensemble and Ensemble-mean Techniques
Super-ensemble (SSE) provides more accurate
rainfall forecasts compared to the ensemble mean
(EM).
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Step 2 Forecast of March Malaria Incidence
from the December Rainfall Forecast
More Accurate Forecasts of Larger Outbreaks
Malaria Cases per 1000 People
4
1
0.25
SSE Provides Better Malaria Prediction
(Correlation 0.19, RMSE 0.52) Compared to EM
(Correlation-0.47, RMSE0.68).
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Next Steps to Develop a Malaria Early Warning
System in Western India
A network of government and private health
professionals, hydro-meteorological specialists,
and climate prediction specialists A malaria
observing system consisting of weather observing
stations, malaria data gatherers, and a
central/distributed data archive system A
very-high resolution (10 kms.) seasonal climate
prediction system for Western India synergy with
agricultural and water resources impacts
prediction Quantification of relationships
between malaria incidence, hydro-meteorological
variables, land cover-use, and other local
factors at a very-high spatial resolution
Quantitative assessments of monsoon climate
variabilitys, including extreme weather events,
impacts on vector-borne diseases, especially
malaria, regional economies, and other societal
matters
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Thank you
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Multimodel FSU Conventional Super-ensemble

• The superensemble forecast is constructed as,

where,
are the ith model forecasts. are the mean of the
ith model forecasts over the training period. is
the observed mean of the training period. are the
regression coefficient obtained by a minimization
procedure during the training period. Those may
vary in space but are constant in time. is the
number of forecast models involved.
The coefficients ai are derived from estimating
the minimum of function,
the mean square error.

• Multimodel bias removed ensemble is defined as,

• In addition to removing the bias, the
  superensemble scales the individual model
  forecasts contributions according to their
  relative performance in the training period in a
  way that, mathematically, is equivalent to
  weighting them.

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Multimodel Synthetic Ensemble/Superensemble
Prediction System
PC
EOF
Training
Forecast
i model n mode
N - Actual Data Sets
Observed Analysis
Estimating Consistent Pattern What is matching
spatial pattern in forecast data Fi(x,t), which
evolves according to PC time series P(t) of
observed data, O(x,t) ?
Forecasts
Normalized Weights
Obs
N - Synthetic Data Sets
Observation