A machine learning approach to integrating genetic and ecological data in tsetse flies ( Glossina pallidipes ) for spatially explicit vector control planning-Reference-Cited by-同舟云学术

A machine learning approach to integrating genetic and ecological data in tsetse flies ( Glossina pallidipes ) for spatially explicit vector control planning

Published:2021-05-05 Issue:7 Volume:14 Page:1762-1777
ISSN:1752-4571
Container-title:Evolutionary Applications
language:en
Short-container-title:Evol Appl

Author:

Bishop Anusha P.¹²^ORCID,Amatulli Giuseppe³^ORCID,Hyseni Chaz⁴^ORCID,Pless Evlyn¹⁵^ORCID,Bateta Rosemary⁶^ORCID,Okeyo Winnie A.⁶⁷^ORCID,Mireji Paul O.⁶⁸^ORCID,Okoth Sylvance⁶^ORCID,Malele Imna⁹^ORCID,Murilla Grace⁶,Aksoy Serap¹⁰^ORCID,Caccone Adalgisa¹^ORCID,Saarman Norah P.¹¹¹^ORCID

Affiliation:

1. Department of Ecology and Evolutionary Biology Yale University New Haven CT USA

2. Department of Environmental Science, Policy, & Management University of California Berkeley CA USA

3. School of the Environment Yale University New Haven CT USA

4. Department of Ecology and Genetics Uppsala University Uppsala Sweden

5. Department of Anthropology University of California Davis CA USA

6. Biotechnology Research Institute Kenya Agricultural and Livestock Research Organization Kikuyu, Nairobi Kenya

7. Department of Biomedical Sciences and Technology School of Public Health and Community Development Maseno University Maseno, Kisumu Kenya

8. Centre for Geographic Medicine Research Coast Kenya Medical Research Institute Kilifi Kenya

9. Vector and Vector Borne Diseases Research Institute Tanzania Veterinary Laboratory Agency Tanga Tanzania

10. Department of Epidemiology of Microbial Diseases Yale School of Public Health New Haven CT USA

11. Department of Biology Utah State University Logan UT USA

Funder

Foundation for the National Institutes of Health

Publisher

Wiley

Subject

General Agricultural and Biological Sciences,Genetics,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/eva.13237

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