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Modeling West Nile virus
Distribution from Surveillance
Data
Josh Bader
16 February 2009
University of California-Santa Barbara
Department of Geography
Outline
•
Biogeography Background
•
WNV Background
•
My Research
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Conceptual model
Predicting WNV distribution
Identify optimal surveillance location
Source: birds.cornell.edu
Biogeography
• Intersection of life sciences and geography
– Also ecology, geology, molecular biology
• Two divisions
– Historical
• Evolutionary perspective
• Pleistocene Ice Age
– Ecological
• Modern persective
• Why are species where they are (were)?
Biology
• Process behind spatial distributions
• Without tolerance limits, a species will occupy all
available areas--maximum dispersion
• Limits determined by:
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Biotic factors
Abiotic factors
Genetics
Population dynamics
• Intraspecies and interspecies
• Often related to fundamental niche of species
Potential range map
Deductive Approach
Biological Information
Geographic Information
•Habitat Req.
•Tolerance limits
•Presence/Absence
•Land cover, etc.
Inductive Approach
Biogeography Links
• California Wildlife Habitat Relationships
– http://www.dfg.ca.gov/bdb/html/cwhr.html
• GAP Analysis
– California:
http://www.biogeog.ucsb.edu/projects/gap/gap_
proj.html
– National: http://gapanalysis.nbii.gov/
http://www.biogeog.ucsb.edu/projects/gap/gap_proj.html
Outline
•
Biogeography Background
•
WNV Background
•
My Research
–
–
–
Conceptual model
Predicting WNV distribution
Identify optimal surveillance location
West Nile virus
• First isolated in Uganda—1937
• First detected in US—1999
• Since spread to entire contiguous 48
states
• Infection can cause range of
symptoms
– Mild: West Nile fever
– Severe: Encephalitis and Meningitis
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2003: 9862 cases & 264 deaths
2004: 2539 cases & 100 deaths
2005: 3000 cases & 119 deaths
2006: 4219 cases & 161 deaths
2007: 906 cases & 26 deaths
2008: 1370 cases & 37 deaths
http://www.cdc.gov/ncidod/dvbid/westnile/index.htm
Source: http://www.cdc.gov/ncidod/dvbid/westnile/Mapsactivity/surv&control06Maps.htm
http://www.cdc.gov/ncidod/dvbid/westnile/Mapsactivity/surv&control07Maps.htm
http://www.cdc.gov/ncidod/dvbid/westnile/Mapsactivity/surv&control08Maps.htm
http://www.cdc.gov/ncidod/dvbid/westnile/index.htm
Surveillance
• Human
– Mandatory reporting to CDC
– Blood donations
– Point of infection difficult to
ascertain
• Mosquito
– Set trap locations
– Trap placement important
Surveillance
• Sentinel chickens
– Similar to mosquito
– Show seroconversion
– Effort > warning
• Veterinary
– Similar to human
surveillance
– Mainly equines
– Vaccine available
http://www.hhs.state.ne.us/wnv/
Surveillance
• Dead bird
– Good early indicators
– Rely on public participation
• Find a dead birdCall
hotline
–1-877-WNV-BIRD
– Species and condition
important
– Volunteered geographic
information
Know Your WNV Hosts
A.
B.
C.
D.
E.
F.
Surveillance Links
• California
– http://westnile.ca.gov/latest_activity.php
• National (CDC)
– http://www.cdc.gov/ncidod/dvbid/westnile/inde
x.htm
• National (USGS)
– http://diseasemaps.usgs.gov/
http://westnile.ca.gov/
http://westnile.ca.gov/
http://diseasemaps.usgs.gov/
Why map WNV?
• Ultimate goal: limit human
infection
• Map migration across the
country
• Identify areas of high risk
for mosquito control and
health alerts
• Determine outbreak patterns
– Perennial: Japanese
encephalitis
– Sporadic: St. Louis
encephalitis
http://www.cdc.gov/ncidod/dvbid/westnile/index.htm
Previous Work
• Disease Mapping
• Largely descriptive
• Little predictive value
• No process behind pattern
• Geographic Correlation
• Sin Nombre—mice
• Lyme—ticks
• DYCAST
• Predict hotspots from dead
bird reports
• Urban areas
http://westnile.ca.gov/2005_maps.htm
Outline
•
Biogeography Background
•
WNV Background
•
My Research
–
–
–
Conceptual model
Predicting WNV distribution
Identify optimal surveillance location
Research Objectives
I.
Define a conceptual model for WNV
distribution
II. Predict WNV distribution from
surveillance data and ancillary
environmental variables
III. Identify optimal areas for additional
surveillance sampling
I. Conceptual Model
• Ecological/biogeographical approach
• Mapping WNV as function of pertinent life
cycle components
• Virus propagation areas
– Amplification and transmission
– “Reproductive range”
• WNV only needs reservoir host (birds) and
vector (mosquito)
• Human, sentinel, and veterinary instances
can be considered sterile
I. Habitat suitability models
• Based on Hutchinson’s (1957) concept of
niche
– Hypervolume where species is found within
suitable ranges for all variables
– Biology reflected in habitat selection
– Fundamental niche
– Realized niche does not often match fundamental
niche
• Includes biotic interactions and competitive exclusion
• Species is not at equilibrium
– Number of multivariate techniques
• Probabilistic techniques are similar
– High suitability implies high presence probability
I. Hosts + Vectors
• Suitability/probabilistic techniques classify area
for one species
• Multiple reservoir and vector species
– Need at least one host and one vector
• P(H) = P(H1 U H2)
= P(H1) + P(H2) – P(H1 ∩ H2)
• P(V) = P(V1 U V2)
= P(V1) + P(V2) – P(V1 ∩ V2)
• P(WNV) = P(H ∩ V)
Study Area
• W Kern County
– W of Sierra Nevada mountains
– 10,000 sq. km
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Kern Co. MVCD
Jepson’s ecoregions
Rural & urban
20+ data points for
for 2 intermediate
hosts and 2 vectors
– 2004 season
Intermediate Hosts
• Corvids most important
– Susceptible, conspicuous, recognizable
– 8 species within study area
• American Crow
– Corvus brachyrhynchus
– cosmopolitan
– Woodlands, grasslands, croplands, and
urban areas
• Western Scrub-jay
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Aphelocoma californicus
More selective
Woodlands & shrublands—Oak
Residential urban areas
Vectors
• Genus Culex
– Permanent water breeders
– Bloodfeeding usually close to
breeding sites
• Culex tarsalis
http://www.usask.ca
– Western encephalitis mosquito
– Irrigation ditches, riparian
• Culex pipiens quinquefasciatus
http://www.fehd.gov.hk
– Southern House mosquito
– Urban environments (e.g. sewer
catch basins)
Variable Selection
• 7-10 Eco-geographic variables (EGV) per
species
– 1 km --- 10,000 pixels
• General EGVs
– Elevation, Percent Urban, Distance to water
• Species Specific
– Mosquitoes—hydrographic; Birds—land cover
• Neighborhood layers will account for
species range size
– Mosquitoes—0.03-0.04 sq. km
– Jays--0.03 sq km; Crow--0.1-0.5 sq km
II. Presence/absence Methods
• Presence/absence
– Ex. regression
– Potentially more predictive power
– Reliable absences difficult to obtain for animals
• Species is present, but not detected
– Imperfect detectability of target species
• Species is absent, even though habitat is suitable
• Presence-only
– Ex. ENFA
– Trade off: predictive power vs. unreliable absences
II. Bayesian Model
• Conditional probabilities of Bayes Theorem
• Probability of WNV positive given a series of EGV
values
• Advantages
– Presence-only when EGVs known everywhere
– Easy to integrate new presences
• Presence and EGV data—rasters
– Matlab
• For each species, two sets of histograms
– Global—EGV values over entire study area
– Presence subset—EGV values at presence locations
• Multivariate probability density functions
II. Bayes Theorem
P(H1|EGV) =
P(H1) * P(EGV| H1)
_______________________________________
P(H1)*P(EGV| H1) P(EGV)
+ P(absence)*P(EGV|absence)
P(H1)
P(EGV| H1)
Denominator
= probability of WNV positive intermediate host of
species 1 over the entire study area
= probability of EGV value within WNV positive H1
subset
= probability of EGV value within global set
II. Simulation
• Problem: presence subset not exhaustive
– P(H1) not known
– P(EGV| H1) not fully characterized
– Needs to be augmented
• Total presence probability P(H1) estimated from focal
species range within study area
– Maximum WNV dispersal
• Additional presences simulated until threshold met
– Areas near presences are preferentially weighted
– P(EGV| H1) updated
• Presence probability map for simulated P(EGV| H1)
• Many simulations (n=1000)
– Distribution of presence probabilities for each pixel
II. Flowchart
• Composite probability map for each species
Prob. Crow (H1)
Prob. Host
Prob. Jay (H2)
Prob. WNV
Prob. Tars. (V1)
Prob. Vector
Prob. Quin. (V2)
• Combined using definition of P(WNV)
II. Presence/Absence Maps
• Convert WNV probability to binary presence/absence
• Receiver Operating Characteristic plots
– Determines threshold that most accurately separates 2 classes
• Requires validation dataset
– Sentinel data
• For each threshold, sensitivity-specificity pair is calculated
– Sensitivity: true positive fraction
• a / (a + c)
– 1 – Specificity: false positive fraction
• d / (b + d)
ROC plots
• Tangent line defines
optimal sensitivityspecificity pair
– Corresponding threshold
considered best separation
value
– Slope can be function of
false positive and false
negative costs
• AUC can be used as index
of overall model accuracy
• Use threshold to change
probability to binary map
Fielding and Bell 1997
III. Optimizing Surveillance
• Surveillance is expensive
– Improve efficiency
• Identify areas for additional sampling that
provide the most information on virus
activity
– Improve separation between presence/absence
classes
• Optimal site
– Ambiguous P(WNV)—near threshold
• Presence simulated and change quantified
– Change in AUC of ROC plot
III. Optimizing Surveillance
• Optimal sampling strategy
– Number and locations of surveillance points
• Loss function--monetary
• Surveillance costs
– Traps, testing, travel
• Surveillance benefits
– Improved efficiency of mosquito control
– Less human cases
• Suggestions to Kern Co. MVCD
Conclusion
• WNV endemic to US
– Public health significance
• Spatial aspect of WNV is important
– Direct surveillance
– Direct mitigation
• Research Objectives
– Provide a model for mapping zoonotic disease
– Method of relating presence-only data to EGV
– Assessing the value of additional surveillance
• Address academic and management issues
– GIS works for both
Acknowledgements
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Dr. Michael Goodchild
Dr. Phaedon Kyriakidis
Dr. Keith Clarke
Dr. Wayne Kramer
Questions ??