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MODELS FOR EARLY DETECTION OF
MALARIA EPIDEMIC IN EAST AFRICA
Dr. A. K. Githeko PhD
Principal Investigator
Epidemic
detection
Intervention
Epidemic
early
prediction
Risk
communication
Intervention
Time
Outbreak
Epidemic
Epidemic detection using
clinical data
Early epidemic prediction
using climate dataEpidemic
prevented
Climate Drivers
Increase in mean
temperature
Temperature
variability
Deforestation
Swamp
reclamation
Typical impact on
ambient
temperature
0.5-1oC
1-5oC
1-2oC
Water temp 2-5oC
Ambient temp 1oC
Effects on malaria
transmission
Additive effect to
threshold
transmission
temperature 18oC
18.5-19oC
19-23oC
19-20oC
19oC
Enable
Epidemic
Enable
Enable
Spatial
impacts of
drivers
Increase in mean temp Temp
variability
Deforestation Swamp
reclamation
Regional Local
Greatest impact of drivers on Plasmodium falciparum malaria occurs
in the exponential phase 18-22oC of parasite development in the vector
NOTES
Changes in temperature affects both Plasmodium falciparum and
Anopheles gambiae rates of development
Anopheles gambiae the major African malaria vector in
Kenya requires at least 150 mean monthly rainfall for its
population to increase in poorly drained ecosystem and
250-300 mm in well drained ecosystems. Rainfall has no
effects on Plasmodium falciparum development rates.
The highlands lie in the temperature range of 16-19oC. The most sensitive
development phase of Plasmodium falciparum in the mosquito lies between
18-22oC (exponential phase).
The macroclimate and microclimate drivers can interact and
enhance malaria transmission.
MODELS DETECT CHANGES IN
MEAN MONTHLY TEMPERATURE
AND RAINFALL IN THE
HIGHLANDS.
THESE CHANGE CAN INITIATE
MALARIA EPIDEMICS
1002
xER mm
ii
MT
RT
1002
xER mm
ii
xRT
xRT
10018 xER mm
ii
xRT
xRT
Additive model
Multiplicative
model
Models using climate data
18+C model
KAKAMEGA 1997: EARLY EPIDEMIC PREDICTION
SEQUENCE
4oCRainfall100.0%Epidemic risk
145.6%
Case
increase1
First signal
Temperature
anomaly
Epidemic
risk
confirmed
EPIDEMIC
OCCURES
FEB-97 MAR APR MAY JUN
KAKAMEGA 1998: EARLY EPIDEMIC PREDICTION
SEQUENCE
4OC Rainfall81.8%
Epidemic risk
330.1%Case
increase
First signal
Temperature
anomaly
Epidemic
risk
confirmed
EPIDEMIC
OCCURES
AUG-97 SEP OCT NOV DEC JAN_98
KAKAMEGA 1999: EARLY EPIDEMIC PREDICTION
SEQUENCE
2OCRainfall 40.9%
Epidemic risk
272.7%
Case
increas
e
First signal
Temperatur
e anomaly
Epidemic
risk
confirmed
EPIDEMIC
OCCURES
JAN_99 FEB MAR APR MAY
KAKAMEGA: ADDITIVE MODEL
Sensitivity 1
Specificity 1
Positive predictive
value
1
NANDI: MULTIPLICATIVE MODEL
Sensitivity 0.78
Specificity 0.99
Positive predictive
value
0.86
AUTOMATED KAKAMEGA MODEL FOR USERS
OUTPUT IN GRAPH: INPUTS ARE MEAN MONTHLY TEMPERATURE AND
RAINFALL
KAKAMEGA MALARIA EPIDEMIC EARLY DETECTION SYSTEM
YEAR
MAX TEMP
INPUT
COLUMMLTM max
temp
MAX TEMP
ANOMALY
RAINFALL
INPUT
COLUMMRAINFALL
CODES
TEMP
ANOMALY
CODES
ADDITIVE
MODEL:
Percent
risk
JAN 09 29.5 28.3 1.2 163.8 1 4 #REF!
FEB 30.6 29.2 1.4 8.0 0 4 18.2
MAR 31.1 29.1 2.0 125.7 0 4 18.2
APR 27.6 27.3 0.3 267.0 5 1 40.9
MAY 27.2 26.4 0.8 210.2 3 1 18.2
JUN 27.7 25.8 1.9 132.1 0 4 4.5
JUL 26.9 25.6 1.2 91.0 0 4 18.2
AUG 27.6 26.1 1.5 180.5 0 4 18.2
SEP 27.4 26.9 0.5 227.4 2 1 27.3
OCT 26.7 27.0 -0.3 124.9 4 0 22.7
NOV 27.5 26.9 0.7 98.2 0 1 0.0
DEC 29.7 27.5 2.1 178.3 0 9 4.5
JAN 10 28.8 28.3 0.6 38.7 2 1 50.0
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Malaria early epidemic prediction: Kakamega
0
10
20
30
40
50
60
JAN
09
MAR MAY JUL SEP NOV JAN
10
MAR MAY JUL SEP NOV
Time (Month)
Ep
ide
mic
ris
k
OTHER DRIVERS OF MALARIA
Topography and drainage
FLAT BOTTOMED “U” SHAPED VALLEY POOR DRAINAGE, GOOD FOR MOSQUITO
BREEDING
3D SATELLITE IMAGE OF “U” SHAPED VALLEY
NARROW “V” SHAPED VALLEY WITH FAST FLOWING STREAM
“
“U” SHAPED VALLEY HAVE 2.9-FOLD MORE
MALARIA MOSQUITOES THAN “V” SHAPED
VALLEYS
THIS AFFECTS MALARIA TRANSMISSION
AND IMMUNITY TO MALARIA
ASSESSING MALARIA PREVALENCE AND IMMUNE RESPONSE IN “U” AND “V”
SHAPED ECOSYSTEMS
MANY EPISODES OF MALARIA PER YEAR IN “U” SHAPED VALLEY
FEW EPISODES OF MALARIA IN “V” SHAPED VALLEY ECOSYSTEM
CSP-MSP Antibody positive children
0
0.1
0.2
0.3
0.4
0.5
0.6
MAY JUNE JULY AUG SEPT OCT NOV DEC JAN
Time
Pro
po
rtio
n +
ve
MARANI "V"
FORTTERNAN "V"
SHIKONDI "plateau"
IGUHU "U"
EMUTETE "U"
SENSITIVITY OF HIGHLAND ECOSYSTEMS TO MALARIA EPIDEMIC:
RAINFALL
“U” shaped ecosystems require mean rainfall
of 150mm/month for mosquito populations to
increase
“V” shaped valleys require mean rainfall of
250-300mm/month for mosquito population
to increase
Vector breeding and population size in the U and V shaped
valleys
The U shaped valleys has more that twice
the size of breeding habitats compared to the
V shaped valley
The U shaped valley has 3 time more adult
Anopheles gambiae females that the V
shaped valley
SENSITIVITY OF HIGHLAND ECOSYSTEMS TO MALARIA EPIDEMICS
IMMUNE PROFILE
The ratio of immune response to malaria
parasites antigens (CSP & MSP) is 1:2.2
between the “V” shaped and “U” shaped
valleys ecosystem
2.2 more people in the “U” shaped valley
have an immune response to malaria
compared to the “V” shaped valley
ecosystem
CONCLUSION
Human populations in the “V” shaped valleys
are less immune to malaria due to lower
transmission rates and low immunity
Heavy rains are required to precipitate
epidemics in the “V” shaped ecosystems
Plateau ecosystems have a similar response
to malaria as the “V” shaped ecosystems
TRAINING AND CAPACITY BUILDING FOR USE OF THE MODELS:
NATIONAL EXPERTS TRAINING WORKSHOP, NAIROBI
PROVINCIAL TRAINING WORKSHOP: KISUMU
DISTRICT LEVEL TRAINING: DAR ES SALAAM TANZANIA
District level end user training workshop Uganda
MODELS USED AT THE SEASONAL CLIMATE OUTLOOK FORUM OF THE GREATER HORN
OF AFRICA TO FOR SIMULATION OF MALARIA EPIDEMICS
MSC TRAINING
2 MSc students from Tanzania
1 MSc student from Uganda
1 MSc student from Kenya
Collaborators• Dr. Andrew K. Githeko (KEMRI)
• Dr. Martha Lemnge (NIMR, Tanzania)
• Mr. Michael Okia (MOH, Uganda)
• Prof. Laban Ogallo (ICPAC)
• COHESU (NGO)
• Dr. John Waitumbi (WRP)
• Dr. John I Githure (ICIPE)
Collaborating Institutions 1. Kenya Medical Research Institute
2. National Institute for Medical Research (NIMR) Tanzania
3. Ministry of Health Uganda
4. Igad Climate Prediction and Application Centre (ICPAC)
5. Community Health Support Program (COHESU)
6. Walter Reed Project, (WRAIR) Kenya International Centre for
7. Insect Physiology and Ecology (ICIPE)
