Big data–model integration and AI for vector‐borne disease prediction-Reference-Cited by-同舟云学术

Big data–model integration and AI for vector‐borne disease prediction

Published:2020-06 Issue:6 Volume:11 Page:
ISSN:2150-8925
Container-title:Ecosphere
language:en
Short-container-title:Ecosphere

Author:

Peters Debra P. C.¹^ORCID,McVey D. Scott²,Elias Emile H.¹,Pelzel‐McCluskey Angela M.³,Derner Justin D.⁴,Burruss N. Dylan⁵,Schrader T. Scott¹,Yao Jin¹,Pauszek Steven J.⁶,Lombard Jason³,Rodriguez Luis L.⁶

Affiliation:

1. US Department of Agriculture Agricultural Research Service Jornada Experimental Range Unit, and Jornada Basin Long Term Ecological Research Program New Mexico State University Las Cruces New Mexico 88003 USA

2. US Department of Agriculture Agricultural Research Service Center for Grain and Animal Health Research Arthropod‐Borne Animal Diseases Research Unit Manhattan Kansas 66506 USA

3. US Department of Agriculture, Animal and Plant Health Inspection Service Veterinary Services Fort Collins Colorado 80526 USA

4. US Department of Agriculture Agricultural Research Service Rangeland Resources and Systems Research Unit Cheyenne Wyoming 82009 USA

5. Jornada Experimental Range New Mexico State University Las Cruces New Mexico 88003 USA

6. US Department of Agriculture, Agricultural Research Service Plum Island Animal Disease Center Orient Point New York 11957 USA

Funder

USDA-ARS CRIS Projects at the Jornada Experimental Range

Plum Island Animal Disease Center

Center for Grain and Animal Health Research

Rangeland Resources and Systems Research Unit

National Science Foundation to New Mexico State University

Publisher

Wiley

Subject

Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.3157

Reference58 articles.

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3. Synchrony of Sylvatic Dengue Isolations: A Multi-Host, Multi-Vector SIR Model of Dengue Virus Transmission in Senegal

4. Potential for Zika Virus to Establish a Sylvatic Transmission Cycle in the Americas

5. Climate Change and Infectious Diseases: From Evidence to a Predictive Framework

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