GH019
Monitoring, Modeling and Mapping of GeoHealth Indicators for Environmental Pathogens

Session ID#: 82082

Session Description:
Pathogens are ubiquitous in the environment and many of them are highly contagious and sensitive to changes in environmental conditions, posing increasing risks to human health due to changing climate and environmental conditions. This session is intended to improve our understanding of the occurrence, prevalence, and persistence of environment/climate sensitive pathogens (such as Vibrios, Norovirus, Naegleria fowleri etc.) in the environment ranging from sea, land to atmosphere by identifying GeoHealth Indicators (such as temperature, rainfall, and humidity) and predictive relationships for the pathogens. To that end, this session solicits contributions that would improve our understanding of the seasonal occurrence, temporal (particularly long-term climate change-induced) variations and spatial distributions of these environmental pathogens by presenting GeoHealth indicators, advanced monitoring and testing technologies (such as next-generation sequencing, biosensing and satellite remote sensing), effective nowcasting and forecasting tools (such as Artificial Intelligence-based and numerical modeling-based predictive tools), and geospatial mapping methods.
Index Terms:

0230 Impacts of climate change: human health [GEOHEALTH]
0240 Public health [GEOHEALTH]
0245 Vector born diseases [GEOHEALTH]


Primary Convener:  Zhiqiang Deng, Louisiana State University, Baton Rouge, LA, United States; Professor, Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA, United States
Convener:  S Sherchan, Tulane University of Louisiana, New Orleans, LA, United States
Primary Liaison:  Zhiqiang Deng, Professor, Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA, United States
 
Geospatial analysis of environmental risks for isolation prevalence of pulmonary nontuberculous mycobacteria in a population cohort (507951)
Elizabeth Hilborn, US Environmental Protection Agency Research Triangle Park, Durham, NC, United States, Stephanie Deflorio-Barker, 1., United States Environmental Protection Agency, Durham, United States, Genee S Smith, Johns Hopkins, Baltimore, United States, Mark Murphy, GDIT, Durham, United States, Jason Stout, Duke University, Durham, United States, Andy Ghio, Environmental Protection Agency Research Triangle Park, National Health and Environmental Effects Research Laboratory,, Research Triangle Park, NC, United States, Edward Hudgens, Us Environmental Protection Agency, Durham, NC, United States, Kyle Philip Messier, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, Jean-Marie Maillard, North Carolina Department of Health and Human Services, Raleigh, NC, United States and Andrey Egorov, US environmental Protection Agency, Durham, NC, United States
 
The DASH Portal: Supporting GeoHealth Research by Automating Geospatial Data Tasks (558954)
Heather Savoy1, Debra Peters1, Nathan Dylan Burruss2, Geovany A Ramirez3, Luis L Rodriguez4, Angela M. Pelzel-McCluskey5 and D. Scott McVey6, (1)USDA-ARS Jornada Exp. Range, Las Cruces, NM, United States, (2)New Mexico State University Main Campus, Las Cruces, NM, United States, (3)Jornada Basin LTER, New Mexico State University, Las Cruces, NM, United States, (4)USDA ARS, Plum Island Animal Disease Center, Orient Point, NY, United States, (5)USDA APHIS, Fort Collins, CO, United States, (6)USDA ARS, Manhattan, KS, United States
 
A Bayesian geospatial modeling framework for the synthesis of point prevalence and health facility catchment data (587844)
Andre Python, Ewan Cameron and Katherine Battle, University of Oxford, Nuffield Department of Medicine, Oxford, United Kingdom
 
Estimating E. coli concentrations in water using sensor and drone-based imagery data (603295)
Matthew Stocker, USDA ARS, BARC, Beltsbille, United States, Billie Morgan, USDA ARS, Beltsville, MD, United States, Javier Valdes-Abellan, Universidad de Alicante, Ingenieria Civil, San Vincente, Spain and Yakov A Pachepsky, USDA ARS, BARC, ANRI, Beltsville, MD, United States
 
Automated drone validation of satellite-based detection of water-based indicators of infectious disease (620910)
Samuel Malloy1, C.K. Shum2 and Jason Otero Torres2, (1)The Ohio State University, Battelle Center for Science, Engineering and Public Policy, Columbus, OH, United States, (2)The Ohio State University, Division of Geodetic Science, School of Earth Sciences, Columbus, OH, United States
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