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– 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:

– Biotic factors

– Abiotic factors

– Genetics

– Population dynamics• Intraspecies and interspecies

• Often related to fundamental niche of species

Biological InformationGeographic Information

Deductive Approach

Inductive Approach

•Habitat Req.•Tolerance limits

Potential range map

•Presence/Absence•Land cover, etc.

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

• 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 birdCall hotline

–1-877-WNV-BIRD– Species and condition important– Volunteered geographic

information

Know Your WNV HostsA. B. C.

D. E. F.

Surveillance Links

• California– http://westnile.ca.gov/latest_activity.php

• National (CDC)– http://www.cdc.gov/ncidod/dvbid/westnile/

index.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

• 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– Aphelocoma californicus– More selective– Woodlands & shrublands—Oak– Residential urban areas

Vectors• Genus Culex

– Permanent water breeders– Bloodfeeding usually close to

breeding sites

• Culex tarsalis– Western encephalitis mosquito– Irrigation ditches, riparian

• Culex pipiens quinquefasciatus– Southern House mosquito– Urban environments (e.g. sewer

catch basins)

http://www.usask.ca

http://www.fehd.gov.hk

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(absence)*P(EGV|absence)  P(H1) = probability of WNV positive intermediate host of

species 1 over the entire study area

P(EGV| H1) = probability of EGV value within WNV positive H1

subsetDenominator = probability of EGV value within global set

P(EGV)

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

• Combined using definition of P(WNV)

Prob. Crow (H1)

Prob. Jay (H2)

Prob. Tars. (V1)

Prob. Quin. (V2)

Prob. Host

Prob. Vector

Prob. 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)

Fielding and Bell 1997

ROC plots• Tangent line defines

optimal sensitivity-specificity 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

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• Dr. Michael Goodchild

• Dr. Phaedon Kyriakidis

• Dr. Keith Clarke

• Dr. Wayne Kramer

Questions ??