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Parasites & vectors2024; 17(1); 354; doi: 10.1186/s13071-024-06446-8

Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.

Abstract: Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factors contribute to their ability to live in a variety of habitats, which have the potential to change over the years as the climate changes. Therefore, as new habitats emerge, the risk for new introductions of these diseases of interest to occur increases. The aim of this study was to model distributions for two primary vectors for BT and AHS (Culicoides imicola and Culicoides bolitinos) using random forest (RF) machine learning and explore the relative importance of environmental and anthropological factors in a region of South Africa with frequent AHS and BT outbreaks. Methods: Culicoides capture data were collected between 1996 and 2022 across 171 different capture locations in the Western Cape. Predictor variables included climate-related variables (temperature, precipitation, humidity), environment-related variables (normalised difference vegetation index-NDVI, soil moisture) and farm-related variables (livestock densities). Random forest (RF) models were developed to explore the spatial distributions of C. imicola, C. bolitinos and a merged species map, where both competent vectors were combined. The maps were then compared to interpolation maps using the same capture data as well as historical locations of BT and AHS outbreaks. Results: Overall, the RF models performed well with 75.02%, 61.6% and 74.01% variance explained for C. imicola, C. bolitinos and merged species models respectively. Cattle density was the most important predictor for C. imicola and water vapour pressure the most important for C. bolitinos. Compared to interpolation maps, the RF models had higher predictive power throughout most of the year when species were modelled individually; however, when merged, the interpolation maps performed better in all seasons except winter. Finally, midge densities did not show any conclusive correlation with BT or AHS outbreaks. Conclusions: This study yielded novel insight into the spatial abundance and drivers of abundance of competent vectors of BT and AHS. It also provided valuable data to inform mathematical models exploring disease outbreaks so that Culicoides-transmitted diseases in South Africa can be further analysed.
© 2024. The Author(s).
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Publication Date: 2024-08-21 PubMed ID: 39169433PubMed Central: PMC11340078DOI: 10.1186/s13071-024-06446-8Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study used machine learning to model the geographic distribution of two main biting midge species that transmit bluetongue and African horse sickness viruses in South Africa’s Western Cape region.
  • The researchers identified key environmental and farm-related factors influencing the species’ presence and compared model predictions with historical outbreak data.

Introduction and Background

  • Culicoides biting midges are tiny flying insects playing a crucial role as vectors spreading viral diseases like bluetongue (BT) and African horse sickness (AHS) which affect livestock.
  • These midges have a worldwide distribution and live in diverse habitats influenced by environmental and human-related factors, which may shift due to climate change, potentially altering disease risk zones.
  • The primary vectors studied are Culicoides imicola and Culicoides bolitinos due to their importance in transmitting BT and AHS viruses.

Study Aim and Approach

  • Objective: To model the spatial distribution of C. imicola and C. bolitinos in the Western Cape region, an area frequently affected by BT and AHS outbreaks.
  • Method: Utilized a machine learning approach called random forest (RF) to analyze how environmental and farm-related variables relate to the abundance and distribution of these vectors.
  • Compared the RF model results with traditional spatial interpolation maps created from the same insect capture data and checked correlations with historical disease outbreak locations.

Data and Variables

  • Data Collection: Midge capture data accumulated over nearly 26 years (1996-2022) from 171 different locations in the Western Cape.
  • Predictor Variables:
    • Climate-related: temperature, precipitation, humidity, water vapour pressure.
    • Environment-related: Normalized Difference Vegetation Index (NDVI), which indicates vegetation density and health; soil moisture levels.
    • Farm-related: livestock densities, specifically cattle density, which relates to the availability of hosts.

Modelling and Analysis

  • Developed separate RF models for each midge species (C. imicola and C. bolitinos) and also a combined model merging both species’ data.
  • Assessed model performance using variance explained by the models:
    • C. imicola: 75.02% variance explained
    • C. bolitinos: 61.6% variance explained
    • Merged species model: 74.01% variance explained
  • Compared model outputs seasonally to spatial interpolation maps to evaluate predictive power throughout the year.

Key Findings

  • Important predictors:
    • Cattle density was the most important variable influencing C. imicola distribution, likely due to cattle being a key blood source.
    • Water vapour pressure was most significant for C. bolitinos, indicating sensitivity to atmospheric moisture conditions.
  • Model comparison:
    • RF models showed higher prediction accuracy than interpolation maps when modeling species individually for most seasons.
    • However, for the combined species model, interpolation maps outperformed RF models in all seasons except winter.
  • Disease outbreaks correlation: No clear, conclusive correlation was found between midge density predictions and the historical locations of BT and AHS outbreaks, suggesting other factors influence disease dynamics.

Conclusions and Implications

  • The study provides novel spatial insights into where competent vector midges are abundant in the Western Cape and which environmental and anthropogenic factors drive their distribution.
  • The findings improve understanding of vector ecology that can feed into mathematical and epidemiological models aiming to predict and manage BT and AHS outbreaks more effectively.
  • It highlights the value of machine learning approaches like random forests in vector distribution modeling over traditional interpolation techniques, with some limitations for combined species modeling.
  • While vector density alone did not explain outbreaks, the models facilitate future research incorporating additional ecological and epidemiological variables for comprehensive disease risk assessment.

Cite This Article

APA
de Klerk J, Tildesley M, Labuschagne K, Gorsich E. (2024). Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning. Parasit Vectors, 17(1), 354. https://doi.org/10.1186/s13071-024-06446-8

Publication

Parasites & vectors
ISSN: 1756-3305
NlmUniqueID: 101462774
Country: England
Language: English
Volume: 17
Issue: 1
Pages: 354
PII: 354

Researcher Affiliations

de Klerk, Joanna
  • The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK. jo.de-klerk@warwick.ac.uk.
Tildesley, Michael
  • The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.
Labuschagne, Karien
  • Epidemiology, Parasites and Vectors, Agricultural Research Council, Onderstepoort Veterinary Research, Onderstepoort, 0110, South Africa.
Gorsich, Erin
  • The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.

MeSH Terms

  • Animals
  • Cattle
  • African Horse Sickness / epidemiology
  • African Horse Sickness / transmission
  • African Horse Sickness / virology
  • Bluetongue / epidemiology
  • Bluetongue / transmission
  • Bluetongue / virology
  • Bluetongue virus
  • Ceratopogonidae / virology
  • Climate
  • Disease Outbreaks
  • Ecosystem
  • Horses
  • Insect Vectors / virology
  • Machine Learning
  • Random Forest
  • South Africa / epidemiology
  • Sheep

Grant Funding

  • BB/M01116X/1 / Biotechnology and Biological Sciences Research Council

Conflict of Interest Statement

The authors declare no competing interests.

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Citations

This article has been cited 2 times.
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