Remote Sensing of Vibrio spp

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Remote Sensing of Vibrio spp Remote Sensing of Vibrio spp. bacteria in the Chesapeake Bay Estuary, MD Erin Urquhart1, Ben Zaitchik1, Seth Guikema1 1Johns Hopkins University

Vibrio in Chesapeake Bay V. cholerae V. parahaemolyticus V. vulnificus Vibrio in Chesapeake Bay Sunlight Climate ENVIRONMENT PHYSICAL PARAMETERS Precipitation Circulation Sea surface height BIOLOGICAL PARAMETERS SST Salinity Nutrients pH HUMANS Bacteria Copepods Shellfish Rec. water Untreated sewage Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

* V. vulnificus * V. parahaemolyticus Vibrio in Chesapeake Bay * V. vulnificus * V. parahaemolyticus Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Remote Sensing of Vibrio spp. in Chesapeake Bay z(V.v.)= -7.867 + (0.316 * Temp) + (-0.342 * (|Saln- 11.5|) Urquhart et al. (2012) RSE Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Percent Satellite Coverage by Month Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Percent Satellite Coverage by Month & Station Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Remote Sensing of Vibrio spp. in Chesapeake Bay z(V.v.)= -7.867 + (0.316 * Temp) + (-0.342 * (|Saln- 11.5|) Urquhart et al. (2013) RSE Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

z(V.v.)= -7.867 + (0.316 * Temp) + (-0.342 * (|Saln- 11.5|) z(V.c.)= -1.1939 + (0.1233 * Temp) – (0.1997 * Saln) – (0.0324 * (Temp * Saln) z(V.v.)= -7.867 + (0.316 * Temp) + (-0.342 * (|Saln- 11.5|) Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

V. vulnificus & V. parahaemolyticus Field Sampling Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Vibrio spp. Modeling in the Chesapeake Bay V. vulnificus and V. parahaemolyticus 148 surface samples Mar.-Sept. (2011 & 2012) Probability of presence algorithms Generalized Linear Model (GLM) Generalized Additive Model (GAM) Random Forest (RF) Optimal prediction point Bacteria abundance algorithms HYBRID abundance algorithms GAM/RF Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Vibrio spp. Modeling in the Chesapeake Bay Probability of presence V. vulnificus V. parahaemolyticus Model GLM GAM RF ACC 0.63 0.72 0.68 0.62 0.67 Abundance V. vulnificus V. parahaemolyticus Model GLM GAM RF MEAN MAE 4.69 4.79 3.87 4.39 7.43 7.51 5.76 6.34 HYBRID abundance V. vulnificus V. parahaemolyticus ABUNDANCE 3.87 4.39 5.76 6.34 HYBRID/P 2.79 4.30 4.36 5.83 HYBRID 2.94 3.44 5.26 6.12 Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Summary Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

Acknowledgments Johns Hopkins University, Rebecca Murphy, Matt Hoffman, Darryn Waugh Cornell University, Dr. Bruce Monger University of Delaware, Erick Geiger University of Maryland, Bradd Haley, Elisa Taviani, Arlene Chen, Rita Colwell, Anwar Huq NASA Goddard, Molly Brown, Carlos Del Castillo Funding Sources Johns Hopkins University, NASA, NSF, NIH

Satellite-derived Salinity Algorithms - MODIS-Aqua Ocean Color Standard Products 10 Remote sensing reflectances (visible) 2003-2010 - In situ – remote sensed measurement matchups 68 CBay Program in situ stations Single pass RS ocean color data 1km radius RS averaging Introduce NEURAL NETWORK A BIT HERE! - Salinity Prediction Models GLM CART GAM BCART ANN RF MARS BART - Cross- validation study

Satellite-derived Salinity Algorithms GAM ANN GLM CART BCART RF MEAN BART MARS MAE 1.82 1.85 1.93 2.39 2.38 2.06 3.72 2.04 1.98 RMSE 2.50 2.53 3.03 3.01 2.67 4.69 2.60 2.52 MAE GLM GAM ANN MEAN East for West 2.1 1.8 1.7 3.3 West for East 2.6 2.8 4.0 4.1 North for South 3.4 5.9 5.7 South for North 3.0 6.4 6.1 High for Low 2.3 4.2 Low for High 2.5 3.9 MENTION HERE ABOUT POOR ACCURACY IN UPPER BAY Top performing prediction models: GAM and ANN All models outperform MEAN GLM and GAM are fairly generalizable in a cross-validation study Urquhart et al. (2012). RSE