1. Modeling West Nile virus Distribution from Surveillance Data Josh Bader 16 February 2009 University of California-Santa Barbara Department of Geography

2. Outline Biogeography Background WNV Background My Research –Conceptual model –Predicting WNV distribution –Identify optimal surveillance location

3. Source: birds.cornell.edu

4. 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)?

5. 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

6. Biological Information Geographic Information Deductive Approach Inductive Approach Habitat Req. Tolerance limits Potential range map Presence/Absence Land cover, etc.

7. Biogeography Links California Wildlife Habitat Relationships –http://www.dfg.ca.gov/bdb/html/cwhr.htmlhttp://www.dfg.ca.gov/bdb/html/cwhr.html GAP Analysis –California: http://www.biogeog.ucsb.edu/projects/gap/gap_ proj.html http://www.biogeog.ucsb.edu/projects/gap/gap_ proj.html –National: http://gapanalysis.nbii.gov/http://gapanalysis.nbii.gov/

8. http://www.biogeog.ucsb.edu/projects/gap/gap_proj.html

9. Outline Biogeography Background WNV Background My Research –Conceptual model –Predicting WNV distribution –Identify optimal surveillance location

10. 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

13. Source: http://www.cdc.gov/ncidod/dvbid/westnile/Mapsactivity/surv&control06Maps.htm

14. http://www.cdc.gov/ncidod/dvbid/westnile/Mapsactivity/surv&control07Maps.htm

15. http://www.cdc.gov/ncidod/dvbid/westnile/Mapsactivity/surv&control08Maps.htm

16. http://www.cdc.gov/ncidod/dvbid/westnile/index.htm

17. Surveillance Human –Mandatory reporting to CDC –Blood donations –Point of infection difficult to ascertain Mosquito –Set trap locations –Trap placement important

18. 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/

19. 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

20. Know Your WNV Hosts A.B.C. D.E. F.

21. Surveillance Links California –http://westnile.ca.gov/latest_activity.phphttp://westnile.ca.gov/latest_activity.php National (CDC) –http://www.cdc.gov/ncidod/dvbid/westnile/inde x.htmhttp://www.cdc.gov/ncidod/dvbid/westnile/inde x.htm National (USGS) –http://diseasemaps.usgs.gov/http://diseasemaps.usgs.gov/

22. http://westnile.ca.gov/

24. http://diseasemaps.usgs.gov/

25. 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

26. 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

27. Outline Biogeography Background WNV Background My Research –Conceptual model –Predicting WNV distribution –Identify optimal surveillance location

28. 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

29. 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

30. 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

31. 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(H 1 U H 2 ) = P(H 1 ) + P(H 2 ) – P(H 1 ∩ H 2 ) P(V) = P(V 1 U V 2 ) = P(V 1 ) + P(V 2 ) – P(V 1 ∩ V 2 ) P(WNV) = P(H ∩ V)

32. 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

33. 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

34. 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

36. 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

38. 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

39. 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

40. II. Bayes Theorem P(H 1 |EGV) = P(H 1 ) * P(EGV| H 1 ) _______________________________________ P(H 1 )*P(EGV| H 1 ) + P(absence)*P(EGV|absence) P(H 1 ) = probability of WNV positive intermediate host of species 1 over the entire study area P(EGV| H 1 ) = probability of EGV value within WNV positive H 1 subset Denominator= probability of EGV value within global set P(EGV)

41. II. Simulation Problem: presence subset not exhaustive –P(H 1 ) not known –P(EGV| H 1 ) not fully characterized –Needs to be augmented Total presence probability P(H 1 ) 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| H 1 ) updated Presence probability map for simulated P(EGV| H 1 ) Many simulations (n=1000) –Distribution of presence probabilities for each pixel

42. II. Flowchart Composite probability map for each species Combined using definition of P(WNV) Prob. Crow (H 1 ) Prob. Jay (H 2 ) Prob. Tars. (V 1 ) Prob. Quin. (V 2 ) Prob. Host Prob. Vector Prob. WNV

43. 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)

44. 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

45. 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

46. 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

47. 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

48. Acknowledgements Dr. Michael Goodchild Dr. Phaedon Kyriakidis Dr. Keith Clarke Dr. Wayne Kramer Questions ??