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Environment, vector, or host? Using machine learning to untangle the mechanisms driving arbovirus outbreaks


Alkhamis, MA and Fountain-Jones, NM and Aguilar-Vega, C and Sanchez-Vizcaino, JM, Environment, vector, or host? Using machine learning to untangle the mechanisms driving arbovirus outbreaks, Ecological Applications, 31, (7) Article e02407. ISSN 1051-0761 (2021) [Refereed Article]


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Copyright 2021 The Authors Licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

DOI: doi:10.1002/eap.2407


Climatic, landscape, and host features are critical components in shaping out-breaks of vector-borne diseases. However, the relationship between the outbreaks of vector-borne pathogens and their environmental drivers is typically complicated, nonlinear, and mayvary by taxonomic units below the species level (e.g., strain or serotype). Here, we aim tountangle how these complex forces shape the risk of outbreaks of Bluetongue virus (BTV); avector-borne pathogen that is continuously emerging and re-emerging across Europe, with sev-ere economic implications. We tested if the ecological predictors of BTV outbreak risk wereserotype-specific by examining the most prevalent serotypes recorded in Europe (1, 4, and 8).We used a robust machine learning (ML) pipeline and 23 relevant environmental features to fitpredictive models to 24,245 outbreaks reported in 25 European countries between 2000 and2019. Our ML models demonstrated high predictive performance for all BTV serotypes (accu-racies>0.87) and revealed strong nonlinear relationships between BTV outbreak risk andenvironmental and host features. Serotype-specific analysis suggests, however, that each of themajor serotypes (1, 4, and 8) had a unique outbreak risk profile. For example, temperature andmidge abundance were as the most important characteristics shaping serotype 1, whereas forserotype 4 goat density and temperature were more important. We were also able to identifystrong interactive effects between environmental and host characteristics that were also sero-type specific. Our ML pipeline was able to reveal more in-depth insights into the complex epi-demiology of BTVs and can guide policymakers in intervention strategies to help reduce theeconomic implications and social cost of this important pathogen.

Item Details

Item Type:Refereed Article
Keywords:bluetongue virus, Culicoides, disease, game theory, midges, species distribution models, vector-borne pathogens.
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Veterinary sciences
Research Field:Veterinary parasitology
Objective Division:Environmental Management
Objective Group:Marine systems and management
Objective Field:Control of pests, diseases and exotic species in marine environments
UTAS Author:Fountain-Jones, NM (Dr Nicholas Fountain-Jones)
ID Code:151292
Year Published:2021
Web of Science® Times Cited:1
Deposited By:Mathematics
Deposited On:2022-07-26
Last Modified:2022-08-23
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