GH005
Early Warning Systems for Infectious Disease Based on Climate and Environmental Variability
Session ID#: 158389
Session Description:
There are multiple environmental pathways that impact human, animal, and plant health. Increasing climatic variability, including extreme weather events, coupled with human-environmental interactions leads to increased risks of disease outbreaks including vector- (e.g. Zika, Dengue, Chikungunya, Malaria, Rift Valley Fever), water- (e.g. Cholera, Dysentery, Typhoid) and air-borne (e.g. Coronavirus, Influenza) diseases. These phenomena have a spatiotemporal distribution driven by the interactions of climate and environmental variables (e.g. precipitation, specific humidity, runoff, vegetation indices) with that that of the vectors and hosts of each individual disease. This session is seeking research that advances the state-of-the-art in disease early warning. This can range from developing the system for which these disease models can reside to advancing the science behind individual routes of transmission using climatic, weather, and remote sensing data products.
Index Terms:
0230 Impacts of climate change: human health [GEOHEALTH]
0245 Vector-borne diseases [GEOHEALTH]
1616 Climate variability [GLOBAL CHANGE]
4341 Early warning systems [NATURAL HAZARDS]
Primary Convener: Moiz Usmani, University of Florida, Ft Walton Beach, FL, United States
Conveners: Anthony L Nguy-Robertson, National Geospatial-Intelligence Agency, St Louis, MO, United States and Thanh H Nguyen, University of Illinois at Urbana Champaign, Urbana, IL, United States
The Climate Integrated Model of Mosquito-Borne Infectious Disease: a large-scale, mechanistic approach to science and global forecasting under climate change (1110461)
Ethan Romero-Severson, Los Alamos National Laboratory, (T-6) Theoretical Biology & Biophysics, Los Alamos, NM, United States, Chonggang Xu, Los Alamos National Lab, Los Alamos, United States, Jeanne M. Fair, Los Alamos National Laboratory, Los Alamos, NM, United States, Carrie Manore, Los Alamos National Laboratory, (T-6) Theoretical Biology & Biophysics, Los Alamos, United States and CIMMID Team
The Planetary Child Health Observatory – from Earth Observation-informed predictive mapping to early warning systems for enteric infectious diseases in low- and middle-income countries. (1133618)
Josh Michael Colston, University of Virginia, Charlottesville, United States, Benjamin F Zaitchik, Johns Hopkins University, Earth and Planetary Sciences, Baltimore, United States, Hamada S Badr, Johns Hopkins University, Earth and Planetary Sciences, Baltimore, MD, United States and Margaret N Kosek, University of Virginia, Medicine, Charlottesville, United States
Using Graph Neural Networks to Forecast West Nile Virus with Trap and Weather Data (1134786)
Adam Tonks1, Trevor Harris2, Bo Li1, William M. Brown3 and Rebecca L. Smith3, (1)University of Illinois at Urbana Champaign, Department of Statistics, Urbana, United States, (2)Texas A&M, Department of Statistics, College Station, United States, (3)University of Illinois at Urbana Champaign, Department of Pathobiology, Urbana, United States
Predicting malaria cases using a hybrid machine learning algorithm based on swarm intelligence (1137322)
Abhilash Singh1, Manish Mehra1, Amit Kumar1, M Niranjan Naik1, Dev Priya2 and Kumar Gaurav1, (1)Indian Institute of Science Education and Research Bhopal, Earth and Environmental Sciences, Bhopal, India, (2)Patent Facilitation Centre, Technology Information Forecasting and Assessment Council, New Delhi, India. CIP LEGIT, Gurugram, Haryana, India, New Delhi, India
Drought Characteristics Constraining Spread of the Vector-Borne Livestock Disease Vesicular Stomatitis (1147184)
Amy Hudson1, Debra P C Peters2, Lee Cohnstaedt3, Justin D Derner4, John M. Humphreys5, Angela M. Pelzel-McCluskey6, Dustin Swanson3 and Luis L Rodriguez7, (1)USDA-ARS ORISE, Las Cruces, NM, United States, (2)USDA-ARS, Las Cruces, NM, United States, (3)USDA ARS, Manhattan, KS, United States, (4)USDA ARS, Cheyenne, WY, United States, (5)USDA ARS, Sidney, MT, United States, (6)USDA APHIS, Fort Collins, CO, United States, (7)USDA ARS, Orient Point, United States
Predicting Vector-Borne Disease Forecasting Skill for Streamlining Model Development (1157330)
Lauren Castro, Los Alamos National Laboratory, (A-1) Analytics, Intelligence, and Technology Division, Los Alamos, NM, United States and Nidhi Parikh, Los Alamos National Laboratory, Los Alamos, United States
Age-structured partial differential equation model for Culex mosquito abundance (1165624)
Rachel Marie Frantz1,2, Humberto C Godinez3, Kaitlyn Michelle Martinez2 and Carrie Manore4, (1)Utah State University, Department of Mathematics and Statistics, Logan, UT, United States, (2)Los Alamos National Laboratory, (A-1) Information Systems & Modeling, Los Alamos, United States, (3)Los Alamos National Laboratory, Los Alamos, NM, United States, (4)Los Alamos National Laboratory, (T-6) Theoretical Biology & Biophysics, Los Alamos, United States
Climate and Environmental Predictors of Vector Mosquito Abundance (1183491)
Caio Martinelle B. de Franca1, Jett Boyd1, Josh John1, Mikayla Johnson2, Daniel Gomes Rocha1 and Erika Podest3, (1)Southern Nazarene University, Biology, Bethany, United States, (2)Southern Nazarene University, Computer Science, Bethany, United States, (3)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
A time-varying occupancy model for Histoplasma in the United States (1186410)
Kimberly A. Kaufeld, Los Alamos National Laboratory, Statistical Sciences, Los Alamos, NM, United States, Jihyeon Kwon, Los Alamos National Laboratory, Los Alamos, United States, 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 and Morgan E. Gorris, University of California Irvine, Irvine, CA, United States
Large scale anomalous climate conditions found to co-occur with infectious diseases and pandemics. A correlation analysis. (1193910)
Subarna Bhattacharyya, Climformatics, Inc, Fremont, United States, Detelina Ivanova, Climformatics, Inc, La Jolla, CA, United States and Debasish Chattopadhyay, University of Alabama, School of Medicine, Department of Chemistry, Birmingham, AL, United States
Drastic Reduction in Dengue Transmission in Sri Lanka following Covid lockdowns from 2020 to 2021 September is followed by Rise to Typical Values from December 2021 to July 2022 (1197486)
Shirin Sasna, Foundation for Environment, Climate and Technology, Akurana, Sri Lanka, Phd Kusumawathie, Senior Research Scientist, Kandy, Sri Lanka, Lareef Zubair, Federation for Environment Climate and Technology, Akurana, Sri Lanka and Nipuni Alahakoon, Tropical Climate Guarantee, Akurana, Sri Lanka
