Transcript Assessment of seasonal and climatic effects on the

Assessment of seasonal and climatic effects
on the incidence and species composition of
malaria by using GIS methods
Ali-Akbar Haghdoost
Neal Alexander (supervisor)
Main objectives
1. Assessment of the feasibility of an early
warning system based on ground climate and
remote sensing data
2. Assessment of the interaction between
Plasmodium spp from different points of view:
meta-analysis, modelling, and extended
analysis of a large epidemiological dataset
Climate effects on malaria
1. The rate at which mosquitoes develop into
adults
2. Frequency of blood feeding
3. Adult mosquito survival
4. The incubation time of parasites in the
mosquito
Other considerations related to climate
1. Deforestation
2. Migration and urbanisation
3. Changing human behaviour
4. Natural disaster and conflict
GIS and malaria
Sipe (2003) reviewed the GIS and malaria literature
and divided the publications into the five categories
outlined below:
1. Mapping malaria incidence/prevalence
2. Mapping the relationships between malaria
incidence/prevalence and other potential related
variables
3. Using innovative methods of collecting data such as
remote sensing (e.g., GIS)
4. Modelling malaria risks
5. General commentary and reviews of GIS used in
malaria control and research
Modelling of malaria (1)
1. Modelling of the abundance of vectors
2. Modelling of the frequency of malaria
cases/infections
Research setting (1)
Mountainous
area (4)
Tropical
climate (3)
Mediterranean
climate (1)
Hot and dry summer
and snow -bounded
winter (2)
Research setting (2)
Research setting (3): Kahnooj District
• Arid and semiarid
• Around 230,000 population in 800 villages
and 5 cities
• Area: 32,000km2, less than 8% of area is
used for agriculture purposes
Research setting (4) Kahnooj
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road types
paved (asfalt)
gravel
dirt
population density(per square kilometer)
0 - 10
10.1 - 30
>30
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20 Miles
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API
50
40
AFI
7
6
5
4
3
2
1
0
SPR
30
20
10
0
1987
1989
1991
1993
1995
1997
1999
2001
87
89
SPR (per 100)
API/AFI (per 1000)
Research setting (5) Malaria In Iran
More than 80% of cases
are infected by P.vivax in
recent years
annual risk per 1000 persons
year
Annual number of
malaria cases dropped
from around 100,000
to 15,000 between
1985 and 2002
60
50
40
30
20
10
0
91
any species
93
95
P.falciparum
97
99
P.vivax
01
year
Research setting(6) Malaria In Kahnooj
Annual risk of malaria per 100,000
population between 1994 and 2001
Year
1997
1998
1999
Population
235297
249448
251315
Positive slides
1378
3407
1924
Annual parasitic index
5.86
13.66
7.66
64 - 286
287 - 537
538 - 839
840 - 3129
3130 - 5019
Research setting (7) Health System
• Rural health centres
– Trained health workers
– Microscopists
– GPs
• Malaria Surveillance system
– Active: follow-up of cases up to one year, febrile people and their
families
– Passive: case finding in all rural and urban health centres free of
charge
– Private sector does not have access to malaria drugs, it refers all cases
to public sector
• Reporting system: weekly report to the district centre
• Supervision: An external quality control scheme is in place
Research setting (8) Treatment Of Malaria
• GPs Prescribe medicine
– P.falciparum: chloroquine (3 days) + primaquine (with
the second dose of chloroquine)
– P.vivax: chloroquine (3 days) + primaquine (weekly
does for eight weeks, or daily dose for two weeks)
• Health works supervise that patients take drugs
completely, also take follow-up slides
Objective
Assessment of the feasibility
of an early warning system
based on ground climate and
remote sensing data
Data Collection (1)
Surveillance malaria data between 1994 and 2002
–
–
–
–
–
Age
Sex
Village
Date of taking blood slides
Plasmodium species
Data Collection (2)
The ground climate data (1975-2003) from the
synoptic centre in Kahnooj City
–
Daily temperature
– Relative humidity
– Rainfall
Data Collection (3)
• GIS maps and RS data:
– Electronic maps of Kahnooj contain the borders, roads,
villages and cities. The map scale was 1:50,000 in
Arcview format
– Landsat data with 30x30m spatial resolution in January
2001, contained NDVI
– NOAA-AVHRR data with 8x8km spatial resolution and
10 day temporal resolution from 1990 to 2001,
contained NDVI and LST
– DEM images with 1x1km resolution (National Imagery
and Mapping Agency of United State of America,
http://geoengine.nima.mil/)
Statistical methods (1)
• The risk of disease was estimated per village per
dekad (10 days)
• Using mean-median smoothing method the temporal
variations were explored
• Poisson method was used to model the risk of
disease
• Fractional polynomial method was used to maximise
the accuracy of models
• The time trend was model by using parametric
method (sine and cos)
Statistical methods (2)
•Models predicted the risk of malaria in three distinct
spatial levels: district, sub-sub- district (SSD) and
village
•Using sensitivity analysis the best gap between the
predictors and malaria risk was estimated
•The data were allocated into modelling (75%) and
checking parts (25%)
•Using forward method the significant variables were
entered in the model. The significance of variables
were assessed by likelihood ratio test and pseudo-R2
Statistical methods (3)
• Using sensitivity analysis the best buffer zone
around each village was defined
• The number of under and over-estimations and
percentages in the final model were computed
• The feasibility of models were assessed by
comparing the over and under-estimations of
models with their corresponding values based on
the extrapolation from the previous month
Results (1)
malaria risk factors
number of malaria
cases
Population
Risk ratio
(95% CI)
Sex
Male
Female
9,932
8,326
98,330
97,950
1
0.86 (0.83-0.88)
Nationality
Iran
Afghanistan
17,471
401
191,400
4880
1
0.52(0.48-0.56)
Age
<5
5-14
15-29
>=30
2,972
7,436
5,001
2,84
28,571
66,316
48,498
50,962
1
1.07(1.03-1.11)
0.99(0.95-1.04)
0.56(0.53-0.59)
Results (2)
Pearson correlation coefficients between the annual risk of
malaria and meteorological variables in Kahnooj 1887-2001
Meteorological factor
API
AFI
AVI
Minimum temperature
-0.02
-0.01
-0.04
Maximum temperature
0.40
0.33
0.46
Mean temperature
0.18
0.15
0.19
Humidity
-0.12
-0.09
-0.14
Rainfall
0.45*
0.54*
0.40*
Results (3)
40
20
0
number of cases
60
80
Temporal variations of malaria over a year; the observed
numbers classified by species, based on 8-year data
0
10
20
30
dekad
P. vivax
All species
P. falciparum
40
Results (4)
The seasonality and time trend of malaria classified by species
fitted value
ppv
0
0
100 200 300
ppv
100 200 300
fitted value
Jun 94
Jun 96
Jun 98
date
Jun 94
ppf
Jun 96
Jun 98
date
fitted value
Jun 00
ppf
200
fitted value
Jun 00
0
100
seasonality and time trend
0
100
200
Jun 94
Jun 96
Jun 98
date
Jun 94
Jun 96
Jun 98
date
fitted value
Jun 00
all species
0
200
0
200
400
all species
400
fitted value
Jun 00
Jun 94
Jun 96
Jun 98
date
Jun 00
Jun 94
Jun 96
Jun 98
date
Jun 00
Results (5)
The fitted values of models based on seasonality, time trend and
meteorological variables
ppv
P. vivax
fitted value
ppf
P. falciparum
0
0
100
100
200
200
300
300
fitted value
Jun 94
Jun 96
Jun 98
date
all species
500
fitted value
Jun 00
Jun 94
Jun 96
0
Jun 96
Jun 98
date
Jun 00
Jun 00
The optimum temperature and humidity
temperature
humidity
Jun 94
Jun 98
date
P.v
P.f
35°C
31.1°C
27.3% 32%
Results (6)
Autocorrelations and partial autocorrelations between the residuals of models,
which estimated risks, based on climate, seasonality and time trend
Results (7)
Pseudo R2
Model number and Explanatory variables
P. falciparum
P. vivax
All species
0.2
0.43
0.35
M1
Sine transform of time
M2
M1 & linear effect of year
0.76
0.49
0.6
M3
M1 & quadratic effect of year
0.76
0.49
0.61
M4
M2 & mean daily min temperatures in last 6 dekads1
0.76
0.5
0.61
M5
M2 & mean daily max temperatures in last 6 dekads1
0.76
0.53
0.62
M6
M2 & mean daily mean temperatures in last 6 dekads1
0.76
0.51
0.62
M7
M2 & mean daily relative humidity in last 6 dekads1
0.78
0.56
0.67
M8
M2 & mean daily min temperatures in last 2 dekads1
0.76
0.49
0.61
M9
M2 & mean daily max temperatures in last 2 dekads1
0.76
0.52
0.62
M10
M2 & mean daily mean temperatures in last 2 dekads1
0.76
0.51
0.61
M11
M2 & mean daily relative humidity in last 2 dekads1
0.78
0.55
0.66
M12
M8 & M9 & M10 & M11
0.78
0.55
0.66
M13
M8 & M9 & M11
0.78
0.55
0.66
M14
M13 & rainfall2
0.8
0.6
0.72
M15
M14 & quadratic effect of min1, max2 of temperature and humidity
in last 2 dekads
0.8
0.66
0.75
M16
M15 and the sum of cases in last dekad, and periods with these
dekad lags:2-4, 5-16, 17-24, 25-36 and 37-48
0.83
0.8
0.84
M17
M15 and the sum of cases in last dekad
0.83
0.79
0.84
M18
M15 and the sum of cases in 2-4 dekad ago
0.82
0.75
0.79
Results (8)
Results (9)
Results (10)
The pseudo R2 between malaria risks and the average NDVI around
villages in 2001
All species
Radius1
Linear
P. falciparum
P. vivax
FR2
Linear
FR2
Linear
FR2
15m
0.004
0.009
0.006
0.07
0.006
0.06
1km
0.04
0.14
0.02
0.04
0.02
0.05
2km
0.07
0.17
0.03
0.03
0.03
0.04
3km
0.08
0.16
0.06
0.07
0.06
0.07
4km
0.08
0.12
0.07
0.1
0.09
0.11
5km
0.09
0.15
0.09
0.12
0.1
0.13
6km
0.06
0.083
0.05
0.094
0.05
0.075
1: The average NDVI around each village was computed in circles with 15m up to 6km radiuses
2: Fractional polynomial, degree two
3: Powers (1,2); 4: powers (-2,-0.5); 5: powers (-2,-0.5))
Results (11)
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The observed and
predicted risk maps of
malaria in 2001 in
Kahnooj, the predicted
maps were computed
based on NDVI around
villages (in 5km radius)
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Risk in 2001
very low
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Results (12)
The observed and predicted
risk maps of malaria in 19942001 in Kahnooj, the predicted
maps were computed based
on the mean of altitude three
kilometres around villages by
using fractional polynomial
models
observed
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# #
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#
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#
#
#
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#
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#
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Malaria was rare in villages
with less than 450 or more
than 1400 meter altitude.
The maximum risks were
observed in villages with
700 to 900 meters altitude.
predicted
##
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Results (13)
The pseudo R2 of Poisson models classified by the
species based on village, SSD or whole district data
Pseudo R2
P. falciparum
village
SSD
District
P. vivax
village
SSD
All species
District
village
SSD
District
0.5
0.13
0.17
0.47
Models based on remote sensing data
0.03
0.13
0.33
0.03
0.16
Models based on time trend, seasonality and autocorrelation
0.11
0.40
0.65
0.07
0.29
0.66
0.08
0.36
0.67
The final model based on time trend, seasonality, autocorrelation
and remote sensing data
0.17
0.46
0.77
0.12
0.32
0.73
0.14
0.4
0.75
Results (14)
Checking part2 (%)
Over estimation
District
Predicted value extrapolated from previous month’s data
P. falciparum
P. vivax
All species
372 (27.3)
438 (22.6)
613 (22.1)
303 (25.6)
441 (22.6)
743 (24.7)
Seasonality, time trend and ground climate data
P. falciparum
P. vivax
All species
321(16.3)
408(18.4)
570(16.5)
296(20.1)
365(17.1)
581(16.7)
Seasonality, time trend and mean of LST and NDVI
P. falciparum
P. vivax
All species
709 (45.1)
697 (20.0)
1,271 (25.2)
376 (23.9)
812 (23.3)
1,187 (23.5)
SSD
Predicted value extrapolated from previous month’s data
P. falciparum
P. vivax
All species
535 (38.4)
1,286 (40.2)
1,654 (36.2)
524 (37.6)
864 (27)
1,220(26.7)
Seasonality, time trend, NDVI and LST
P. falciparum
P. vivax
All species
673 (48.3)
1,179 (36.9)
1,647 (36.0)
759 (54.5)
1,602 (50.1)
2,215 (48.5)
Predicted value extrapolated from previous month’s data
Village
Over and underpredictions of models
based on seasonality,
time trend and ground
and remote sensing
data
Under estimation
P. falciparum
P. vivax
All species
1,233 (84.9)
2,133 (71.5)
3,137 (70.1)
952 (65.6)
1,903 (63.8)
2,621 (59.2)
Seasonality, time trend, NDVI and LST
P. falciparum
P. vivax
1,205 (82.9)
2,599 (87.1)
1,285 (88.4)
2,309 (77.4)
Results (15)
0.00
0.25 0.50 0.75
1 - Specificity
1.00
1.00
0.00
0.25
0.50
Sensitivity
0.75
1.00
0.75
0.50
0.25
0.00
0.00
0.25
0.50
Sensitivity
0.75
1.00
Species-specific ROCs, they assess the relationship between
sensitivity and specificity of the full models (with NDVI and LST)
in predicting local transmissions in all data
0.00
0.25 0.50 0.75
1 - Specificity
1.00
0.00
0.25 0.50 0.75
1 - Specificity
Area under ROC curve = 0.8626
Area under ROC curve = 0.8462
Area under ROC curve = 0.8452
P.vivax
P.falciparum
all species
1.00
Results (16)
observed
fitted
# #
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Comparing the fitted and
observed risk maps of local
transmission, the fitted
values were computed
based on seasonality, time
trend, history of disease,
NDVI and LST
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Summary of main findings (1)
1. Ground climate data explained around 80% of
P. vivax and 75% of P. falciparum variations one
month ahead
2. Comparing to the extrapolation of data from
previous month, ground climate data improve the
accuracies around 10%; but remote sensing data
does not improve
3. The ground climate data are freely available in
the filed; therefore, it was concluded that the
models based on ground climate data are feasible.
Summary of main findings (2)
4. Ground climate data improved predictions
around 10% one month ahead in district level
5. NDVI and LST (with 8x8km resolution) did not
improve the prediction
6. Elevation (with 1x1km resolution) improved
predictions around 15%
7. NDVI (with 30x30m resolution) did not improve
the predictions
Summary of main findings (3)
8. Elevation (with 1x1km resolution) improved
predictions around 15%
9. NDVI (with 30x30m resolution) did not improve
the predictions
10. P. falciparum and P. vivax models had different
parameters.
11. The accuracy of temporal P. vivax variations
was less than that in P. falciparum
conclusion
• Ground climate data (which are
available free of charge)
improved the model accuracies
around 10% and it seems that
early warning system based on
these models is feasible
Time for your comments
Thanks for you kind
attention