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Modelling the distribution of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competitive effects

Bingyi Yang, Brooke A. Borgert, Barry W. Alto, Carl K. Boohene, Peter Brabant, Joe Brew, Kelly Deutsch, James T. DeValerio, Rhoel R. Dinglasan, Daniel Dixon, Joseph M. Faella, Sandra L. Fisher-Grainger, Gregory E. Glass, Reginald Hayes, David F. Hoel, Austin Horton, Agne Janusauskaite, Bill Kellner, Moritz U.G. Kraemer, Eric Leveen, Keira J. Lucas, Johana Medina, Rachel Morreale, William Petrie, Robert C. Reiner Jr., Michael T. Riles, Henrik Salje, David L. Smith, John P. Smith, Amy Solis, Jason Stuck, Chalmers Vasquez, Katie F. Williams, Rui-De Xue, Derek A.T. Cummings
doi: https://doi.org/10.1101/498238
Bingyi Yang
1 Department of Biology and Emerging Pathogens Institute, University of Florida;
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  • For correspondence: byyang@connect.hku.hk
Brooke A. Borgert
1 Department of Biology and Emerging Pathogens Institute, University of Florida;
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Barry W. Alto
2 Department of Entomology and Nematology, University of Florida;
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Carl K. Boohene
3 Polk County, Florida, Mosquito Control;
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Peter Brabant
4 South Walton County Mosquito Control District;
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Joe Brew
5 Institut de Salut Global de Barcelona, Barcelona, Catalonia, Spain;
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Kelly Deutsch
6 Orange County Government, Florida, Orange County Mosquito Control Division;
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James T. DeValerio
7 University of Florida Institute of Food and Agricultural Sciences, Bradford County Extension;
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Rhoel R. Dinglasan
8 Department of Infectious Diseases and Immunology;Emerging Pathogens Institute, University of Florida;
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Daniel Dixon
9 Anastasia Mosquito Control District, Florida;
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Joseph M. Faella
10 Brevard County, Florida, Mosquito Control;
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Sandra L. Fisher-Grainger
11 Hernando County, Florida, Mosquito Control;
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Gregory E. Glass
12 Emerging Pathogens Institute; Department of Geography, University of Florida;
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Reginald Hayes
13 Palm Beach County, Florida, Mosquito Control;
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David F. Hoel
14 Lee County Mosquito Control District;
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Austin Horton
15 Gulf County, Florida, Mosquito Control;
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Agne Janusauskaite
16 Pasco County Mosquito Control District;
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Bill Kellner
17 Citrus County Mosquito Control District;
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Moritz U.G. Kraemer
18 Harvard Medical School; Boston Children's Hospital; Department of Zoology, University of Oxford;
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Eric Leveen
19 Florida Department of Agriculture and Consumer Services;
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Keira J. Lucas
20 Collier Mosquito Control District, Naples FL;
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Johana Medina
21 Miami-Dade County, Florida, Mosquito Control;
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Rachel Morreale
14 Lee County Mosquito Control District;
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William Petrie
21 Miami-Dade County, Florida, Mosquito Control;
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Robert C. Reiner Jr.
22 Institute for Health Metrics and Evaluation, University of Washington;
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Michael T. Riles
23 Beach Mosquito Control District;
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Henrik Salje
24 Mathematical Modelling Unit, Institut Pasteur, Paris, France;
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David L. Smith
22 Institute for Health Metrics and Evaluation, University of Washington;
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John P. Smith
25 Florida State University, Panama City, FL;
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Amy Solis
26 Clarke: Aquatic and Mosquito Control Services and Products;
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Jason Stuck
27 Pinellas County, Florida, Mosquito Control;
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Chalmers Vasquez
21 Miami-Dade County, Florida, Mosquito Control;
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Katie F. Williams
9 Anastasia Mosquito Control District, Florida;
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Rui-De Xue
28 Anastasia Mosquito Control District
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Derek A.T. Cummings
1 Department of Biology and Emerging Pathogens Institute, University of Florida;
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  • For correspondence: datc@ufl.edu
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Abstract

Florida faces the challenge of repeated introduction and autochthonous transmission of arboviruses transmitted by Aedes aegypti and Aedes albopictus. Empirically-based predictive models of the spatial distribution of these species would aid surveillance and vector control efforts. To predict the occurrence and abundance of these species, we fit mixed-effects zero-inflated negative binomial regression to a mosquito surveillance dataset with records from more than 200,000 trap days, covering 73% of the land area and ranging from 2004 to 2018 in Florida. We found an asymmetrical competitive interaction between adult populations of Aedes aegypti and Aedes albopictus for the sampled sites. Wind speed was negatively associated with the occurrence and abundance of both vectors. Our model predictions show high accuracy (72.9% to 94.5%) in the validation tests leaving out a random 10% subset of sites and data from 2018, suggesting a potential for predicting the distribution of the two Aedes vectors.

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Posted December 17, 2018.
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Modelling the distribution of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competitive effects
Bingyi Yang, Brooke A. Borgert, Barry W. Alto, Carl K. Boohene, Peter Brabant, Joe Brew, Kelly Deutsch, James T. DeValerio, Rhoel R. Dinglasan, Daniel Dixon, Joseph M. Faella, Sandra L. Fisher-Grainger, Gregory E. Glass, Reginald Hayes, David F. Hoel, Austin Horton, Agne Janusauskaite, Bill Kellner, Moritz U.G. Kraemer, Eric Leveen, Keira J. Lucas, Johana Medina, Rachel Morreale, William Petrie, Robert C. Reiner Jr., Michael T. Riles, Henrik Salje, David L. Smith, John P. Smith, Amy Solis, Jason Stuck, Chalmers Vasquez, Katie F. Williams, Rui-De Xue, Derek A.T. Cummings
bioRxiv 498238; doi: https://doi.org/10.1101/498238
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Modelling the distribution of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competitive effects
Bingyi Yang, Brooke A. Borgert, Barry W. Alto, Carl K. Boohene, Peter Brabant, Joe Brew, Kelly Deutsch, James T. DeValerio, Rhoel R. Dinglasan, Daniel Dixon, Joseph M. Faella, Sandra L. Fisher-Grainger, Gregory E. Glass, Reginald Hayes, David F. Hoel, Austin Horton, Agne Janusauskaite, Bill Kellner, Moritz U.G. Kraemer, Eric Leveen, Keira J. Lucas, Johana Medina, Rachel Morreale, William Petrie, Robert C. Reiner Jr., Michael T. Riles, Henrik Salje, David L. Smith, John P. Smith, Amy Solis, Jason Stuck, Chalmers Vasquez, Katie F. Williams, Rui-De Xue, Derek A.T. Cummings
bioRxiv 498238; doi: https://doi.org/10.1101/498238

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