GH018
Statistical Methods, Machine Learning, and Big Data in GeoHealth
Session ID#: 159329
Session Description:
The unique nature of studying GeoHealth causes researchers to pull in disparate data sources and search for signals often in noisy or incomplete data. Researchers are often fusing datasets from the environment, the climate, human population and demographics, and disease surveillance programs—each with their own uncertainties and spatiotemporal scales. Often, researchers are seeking the environmental drivers behind health outcomes, leading to analyzing numerous datasets: a big data problem. This amount of data and merging of multiple data streams sets the stage for advanced statistical and machine learning methods in GeoHealth. We invite talks that use cross-disciplinary statistical methods, machine learning techniques, or other exploration approaches for analyzing big data in any discipline of GeoHealth.
Index Terms:
0230 Impacts of climate change: human health [GEOHEALTH]
0240 Public health [GEOHEALTH]
0299 General or miscellaneous [GEOHEALTH]
Primary Convener: Kimberly A. Kaufeld, Los Alamos National Laboratory, Statistical Sciences, Los Alamos, NM, United States
Convener: Morgan E. Gorris, Los Alamos National Laboratory, Los Alamos, NM, United States
Student/Early Career Convener: Laura Albrecht, Colorado School of Mines, Applied Mathematics and Statistics, Golden, CO, United States; Los Alamos National Laboratory, Statistical Sciences, Los Alamos, United States
Assessing the Utility of Google Health Trends for Forecasting Dengue in Brazil (1094180)
Julie Allison Spencer1, Martha Barnard1,2, Nidhi Parikh3, Caroline Franco4, Kaitlyn Michelle Martinez1, Carrie Manore5, Erik J Scully6 and Anthony L Nguy-Robertson7, (1)Los Alamos National Laboratory, (A-1) Information Systems & Modeling, Los Alamos, United States, (2)University of Minnesota Twin Cities, School of Public Health, Department of Biostatistics, Minneapolis, United States, (3)Los Alamos National Laboratory, Los Alamos, United States, (4)University of Oxford, Nuffield Department of Medicine, Oxford, United Kingdom, (5)Los Alamos National Laboratory, (T-6) Theoretical Biology & Biophysics, Los Alamos, United States, (6)National Geospatial Intelligence Agency, Springfield, United States, (7)University of Nebraska Lincoln, Lincoln, NE, United States
A Novel Geospatial-First Machine Learning Approach to Modeling Heating Loads in Alaska: When Perfect (Data) are the Enemy of Good (Data) (1096717)
Shamsi Soltani1, Vidisha Chowdhury2, Philippe Schicker2, Madelyn Gaumer3, Nicholas Bolten4 and Erin Trochim5, (1)Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, United States, (2)Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, United States, (3)Department of Applied Mathematics, University of Washington, Seattle, United States, (4)Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, United States, (5)University of Alaska Fairbanks, Fairbanks, AK, United States
Modeling the Spread of a Livestock Disease With Semi-Supervised Spatiotemporal Deep Neural Networks (1108901)
Michael Alcorn, USDA Agricultural Research Service, Charleston, United States; USDA ARS, Charleston, United States, Kerrie Geil, USDA ARS, New Orleans, United States, Brian Stucky, USDA ARS, Gainesville, United States and Debra Peters, USDA-ARS Jornada Exp. Range, Las Cruces, NM, United States
Interpretable and Scalable Spatiotemporal Gaussian Process Regression for Multivariate and Censored Environmental Data (1110591)
Kyle Philip Messier1, Deepak Mav2, Eric Bair2, Ruchir R Shah2, Melissa E Lowe3, Myeongjong Kang4, Matthias Katzfuss4 and Matthew W Wheeler1, (1)National Institutes of Health, National Institute of Environmental Health Sciences, Durham, United States, (2)Sciome, LLC, Durham, United States, (3)Duke University, Duke Cancer Institute, Durham, United States, (4)Texas A&M University, Department of Statistics, College Station, United States
A Spatio-temporal Model to Assess Environmental Impacts on Coccidioidomycosis in the Southwestern United States (Invited) (1115008)
Staci Hepler, Wake Forest University, Statistical Sciences, Winston-Salem, United States, David Kline, Wake Forest University School of Medicine, Biostatistics and Data Science, Winston Salem, United States, Kimberly A. Kaufeld, Los Alamos National Laboratory, Statistical Sciences, Los Alamos, NM, United States, Morgan E. Gorris, Los Alamos National Laboratory, Los Alamos, NM, United States and Andrew Greene, Los Alamos National Laboratory, Los Alamos, United States
Data-Driven Bird Population Models using Statistical and Dynamical Surrogate Model Methods (1116422)
Humberto C Godinez, Los Alamos National Laboratory, Los Alamos, NM, United States, Kimberly A. Kaufeld, Los Alamos National Laboratory, Statistical Sciences, Los Alamos, NM, United States and Carrie Manore, Los Alamos National Laboratory, (T-6) Theoretical Biology & Biophysics, Los Alamos, United States
Key drivers of mold growth and human respiratory illness after a hurricane: Lessons from Hurricane Ida (1116972)
Zhengxiao Yan1, Parham Azimi2, Zahra Keshavar2, Christian Caballero1, Ebrahim Ahmadisharaf1,3 and Joseph G Allen4, (1)Florida State University, Tallahassee, United States, (2)Harvard University, Cambridge, United States, (3)Virginia Polytechnic Institute and State University, Biological Systems Engineering, Blacksburg, United States, (4)Harvard University, Environmental Health, Cambridge, United States
Integrating Traffic Pollution Dispersion into Spatiotemporal High-Resolution NO2 Prediction (1135951)
Jianzhao Bi1, Yunhan Wu2, Adam A. Szpiro2 and Joel D. Kaufman1, (1)University of Washington, Department of Environmental and Occupational Health Sciences, Seattle, United States, (2)University of Washington, Department of Biostatistics, Seattle, United States
Deep Learning Detection of COVID-19, Temporal Variation and it's Link with Temperature in Nigeria (1143807)
Ebiendele Precious, University of Tsukuba, Graduate School of Systems and Information Engineering, Tsukuba, Japan, Dairaku Koji, University of Tsukuba Graduate School of Systems and Information Engineering, Tsukuba, Japan and Paul Adigun, University of Tsukuba Graduate School of Systems and Information Engineering, Ibaraki, Japan
A Spatio-temporal Model to Estimate West Nile Virus Cases in Ontario (1155585)
Laura Albrecht, Colorado School of Mines, Applied Mathematics and Statistics, Golden, CO, United States; Los Alamos National Laboratory, Statistical Sciences, Los Alamos, United States and Kimberly A. Kaufeld, Los Alamos National Laboratory, Statistical Sciences, Los Alamos, NM, United States
A Case Study using Machine Learning to Predict Dengue Forecasting Model Performance (1169934)
Nidhi Parikh, Los Alamos National Laboratory, Los Alamos, United States and Lauren Castro, Los Alamos National Laboratory, (A-1) Analytics, Intelligence, and Technology Division, Los Alamos, NM, United States
Incorporating Statistical Models for Groundwater Contamination to Modernize State Environmental Assessment Programs (1177194)
Franziska Landes1, Lorraine Brown2, Sarah Van Glubt1, Kayla Jones3, Kelsi Evans4, Heather Gosack4 and Paul Seidel1, (1)Oregon Department of Environmental Quality, Portland, United States, (2)Exa Data & Mapping Services, Sequim, United States, (3)Apex Companies, Greensburg, United States, (4)Apex Companies, Portland, United States
Leveraging public health data to develop an impact-relevant heat wave definition for climate risk assessment (1180039)
Samantha Lapp, University of Chicago, Department of Physics, Chicago, United States, James A Franke, University Of Chicago, Department of the Geophysical Sciences, Center for Robust Decision-making on Climate and Energy Policy, Chicago, United States and Elisabeth J Moyer, University of Chicago, Department of the Geophysical Sciences, Center for Robust Decision-making on Climate and Energy Policy, Chicago, IL, United States
Overwintering or Re-Invasion? Interrogating Genomes and Geography to Unravel Multiyear Vesicular Stomatitis Epizootics (1181738)
John Humphreys1, Miranda R Bertram2, Phillip Shults3, Lauro Velazquez-Salinas2, Debra P C Peters4 and Luis L Rodriguez2, (1)USDA ARS, Sidney, United States, (2)USDA ARS, Orient Point, United States, (3)USDA ARS, Manhattan, United States, (4)USDA-ARS, Las Cruces, NM, United States
Temperature-Dependent Life History Traits in Culex Mosquitoes and Applications in Disease Modeling (1073778)
Sarah Kane Moser1, Martha Barnard2,3, Rachel Marie Frantz2,4, Julie Allison Spencer2, Katie A Rodarte1, Isabel Karen Crooker5, Andrew W. Bartlow1, Ethan Romero-Severson6 and Carrie Manore7, (1)Los Alamos National Laboratory, (B-10) Biosecurity & Public Health, Los Alamos, United States, (2)Los Alamos National Laboratory, (A-1) Information Systems & Modeling, Los Alamos, United States, (3)University of Minnesota Twin Cities, School of Public Health, Department of Biostatistics, Minneapolis, United States, (4)Utah State University, Department of Mathematics and Statistics, Logan, UT, United States, (5)Cornell University, Department of Biology, Ithaca, United States, (6)Los Alamos National Laboratory, (T-6) Theoretical Biology & Biophysics, Los Alamos, NM, United States, (7)Los Alamos National Laboratory, (T-6) Theoretical Biology & Biophysics, Los Alamos, United States
