Towards spatially explicit
malaria risk models for the
Peruvian Amazon
Ben Zaitchik, Beth Feingold , Keith Spangler
Margaret Kosek, Pablo Penataro Yori, Weston Anderson
Johns Hopkins University
Christa Peters-Lidard
NASA Goddard Space Flight Center
Bill Pan
Duke University
Motivation
• Almost 90% of malaria in the Western
Hemisphere is located in the Amazon
• 25% of the malaria burden
in the Americas is in 12
municipalities of Peru,
Brazil and Venezuela
• 60% of cases in Peru are in
the Department of Loreto
Motivation
• Relationships between land use,
mosquito ecology, climate, human
activity, and malaria risk are complex
# Cases (1000s)
Malaria, Loreto Province 1990‐2006
140
120
100
80
60
40
20
0
High Deforestation
Roll Back Malaria Program
P. falciparum
P. vivax
Total
1990
1992
1994
1996
1998
2000
2002
2004
2006
Motivation
• Relationships between land use,
mosquito ecology, climate, human
activity, and malaria risk are complex
• But strong biophysical links exist, and
they can be monitored and addressed
through integrated analysis
Malaria on the Amazon Frontier
Climate and
Hydrology
Infrastructure
Land Use
Migration,
Colonization, and
Agriculture
Anopheles Larva
Habitat
Human
Exposure
Density of
Anopheles Adults
Human Malaria
Infection
Process of Land Use Change
Infrastructure Expansion
• Oil Exploration
• Highway construction
• Urban growth
Process of Land Use Change
Infrastructure Expansion
• Oil Exploration
• Highway construction
• Urban growth
Migration, Colonization, and
Phases of Agriculture
Development
Annual Crops
Pastures
Perennial Crops
Frontier Malaria Hypothesis
200
Annual Parasite Index
180
160
Phase 1: EPIDEMIC
• High density of and exposure to
vectors
Phase 3: ENDEMIC
•Outdoor transmission
• Expanded urbanization
• Limited knowledge of the
• High community cohesion
Phase 2: TRANSITION
disease
• More deforestation
• Poor health infrastructure• Declining rate of malaria
• Low, stable rates of malaria
• Better infrastructure
• Decreased population
mobility
140
120
100
80
60
40
20
0
0
5
10
15
Years since settlement
Phase 1
Phase 2
Phase 3
Research Questions
• How do physiography, land use, climate and hydrology
interact to influence patterns of Anopheles density? How
does this differ by species?
• What is the temporal relationship between land
clearance and rates of malaria?
• What is the spatial structure of association between
biophysical conditions and mosquito density?
• How do human settlement and migration patterns
contribute to existing patterns of transmission risk, and
how do they drive the evolution of risk zones?
Malaria on the Amazon Frontier
Climate and
Hydrology
Infrastructure
Land Use
Migration,
Colonization, and
Agriculture
Anopheles Larva
Habitat
Human
Exposure
Density of
Anopheles Adults
Human Malaria
Infection
Malaria on the Amazon Frontier
Climate and
Hydrology
Infrastructure
Land Use
Migration,
Colonization, and
Agriculture
Anopheles Larva
Habitat
Human
Exposure
Density of
Anopheles Adults
Human Malaria
Infection
Approach
• Spatio-temporal Ecological Anopheles model
– Input 1: Satellite-based land cover maps
– Input 2: Meteorological data
– Input 3: Land Data Assimilation System
• Human Activities and Settlements Map
– Input 1: Satellite and in situ mapping
– Input 2: Census and Economic data
• Eco-epidemiological Malaria Transmission
Model
– Application: Risk monitoring and prediction
Methods:
Land Cover Mapping
• Deforestation and forest
disturbance in Peru can
be subtle
• Primary tool: Landsat,
multi-temporal analysis
• Supplemented with
commercial high
resolution imagery
• Extensive ground truth
Methods:
Land Data Assimilation System
• A Land Data Assimilation System
(LDAS) is a computational tool that
merges observations with numerical
models to produce optimal estimates
of land surface states and fluxes.
Methods:
Land Data Assimilation System
Update Observations
Landscape Information
LDAS Output
Numerical Model
Meteorological Data
• Hydrological fluxes and storage
• Localized meteorology
• Surface energy balance
Methods:
Spatially explicit Anopheles Model
Methods:
Human Activities & Settlement Maps
Methods:
Transmission Risk Monitoring
The study is developing four spatially explicit risk factors:
• Human biting rate, a function of the number of
mosquitoes per human and the human feeding rate
• Sporozoite rate, the % of mosquitoes with sporozoite in
their salivary glands
• Stability index, the expected number of human bites
taken by a vector over its lifetime
• Parasite ratio, the number prevalence of infection in
humans
Results: Land Cover Analysis
• 12 class supervised
classification
• Nauta-Iquitos road in
2001and Iquitos-Mazan
road in 2009
• Distinction between
forest and non-forest
appears to be
adequate
• Identification of
secondary forest is not
Results: Anopheles Analysis
Mosquito Collection
• Iquitos-Nauta
road: 1999-2001
• Iquitos-Mazan
road: 2007-2011
• Additional survey
of logging camps
Mazan
Iquitos
Nauta
Results: Anopheles analysis
Vectors
Non-Vectors
35%
30%
25%
20%
15%
10%
5%
% of site shaded
100
90-99
80-89
70-79
60-69
50-59
40-49
30-39
20-29
10-19
0%
0-9.9
% of sites with An. Present
40%
Results: Anopheles analysis
250 m radius
1000 m radius
Results: Anopheles analysis
% Deforested Land Cover
Deforestation vs. Scale
90
80
70
60
50
40
30
20
10
0
0
1000
2000
3000
4000
5000
Radius of Analysis (m)
6000
7000
Results: LDAS
Precipitation
900
800
MERRA
TRMM
PERSIANN
GPCC
Dec-96
May-02
R2 = 0.96
mm/month
700
600
500
400
300
200
100
0
Dec-85
Jun-91
Nov-07
Results: LDAS
Soil Surface Temperature
303
302
NautaRoadS
NautaRoadN
MazanRoad
LoggingCamp
301
Mazan
300
299
Iquitos
298
297
296
295
Oct-00
Jun-03
Mar-06
Dec-08
Nauta
Results: LDAS
% Soil Water Content, top 10cm
45
40
35
30
Mazan
25
Iquitos
20
15
10
5
0
Oct-00
NautaRoadS
NautaRoadN
MazanRoad
LoggingCamp
Jun-03
Mar-06
Dec-08
Nauta
Conclusions
• Land Surface Model simulations show
strong potential to inform predictions
of Anopheles distribution
– Active data assimilation not yet tested
• The relationship between land cover
and mosquito distribution is robust and
species-specific, and it appears to be
strongest at 3-5km radius of influence
Next Steps
• Integrate LDAS results to Anopheles distribution
models
• Compile Human settlements and activities map
• Continued and enhanced mosquito collection and
malaria monitoring
• Work with end-user partners to ensure that the
products are taking on a useful form
• Constant cross-examination of accepted
hypotheses regarding what governs malaria risk in
this region
THANK YOU