Modelling the spatial and temporal distribution of tsetse flies for targeted control

Research output: ThesisPhD Thesis - Research external, graduation UT

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Abstract

The control of tsetse flies, the sole biological vector of African trypanosomiasis, is crucial in efforts to eradicate and eventually eliminate the disease they transmit in Sub-Saharan Africa. Although methods for reducing tsetse populations are known, their effective implementation is often hindered by insufficient or unreliable information about tsetse distribution or numbers, which can hamper identifying priority areas for control. In this research, freely available satellite data and in-situ tsetse observations are used for sites in Kenya and Rwanda to 1) identify potential tsetse breeding and foraging sites; 2) assess the transferability of tsetse habitat models between geographically separated regions; 3) identify optimal locations and timing for targeted control based on temporal tsetse abundance dynamics; and 4) upscale spatial predictions of tsetse abundance to a national level using geographically-constrained tsetse counts.
The findings in this thesis show that environmental data allow to accurately identify the spatial distribution of young tsetse flies, representative of potential breeding sites. Furthermore, it was found that tsetse habitat models can effectively be transferred between environmentally similar regions, which can offer a cost-effective means to understand tsetse habitats in data-scarce areas. The thesis shows that periods with increased rainfall result in tsetse population spikes. For example, in Shimba Hills, tsetse numbers rise after a month of increased rainfall. As a high tsetse population is likely to result in increased contact rates with livestock and, subsequently, an increased risk of AAT, this information can assist policymakers in determining when to control the flies and when prophylaxis may be more effective. For upscaling tsetse abundance predictions nationally, limitations arise when the in-situ tsetse data used do not cover all environmental conditions, stressing the need and indicating priority areas for additional sampling efforts.
This research identifies factors affecting tsetse occurrences and numbers in changing environments but an essential aspect of the disease itself remains unaddressed. Therefore, I emphasize the need for further research to integrate the dynamics of tsetse flies and the diseases they transmit, which is crucial for providing more effective guidance on control measures and ultimately eradication of African trypanosomiasis.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
  • Faculty of Geo-Information Science and Earth Observation
Supervisors/Advisors
  • Skidmore, Andrew , Supervisor
  • Daniel, Masiga, Co-Supervisor, External person
  • Vrieling, Anton, Co-Supervisor
Thesis sponsors
Award date10 Apr 2024
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-6060-3
Electronic ISBNs978-90-365-6061-0
DOIs
Publication statusPublished - 10 Apr 2024

Keywords

  • Tsetse flies
  • Trypanosomiasis
  • Species distribution model (SDM)
  • Machine learning (ML)
  • Satellite data
  • Predictive modelling
  • Geostatistics
  • GeoHealth
  • Vector-borne diseases
  • environmental data
  • model transfer
  • similarity analysis
  • Spatial and temporal patterns
  • Seasonality
  • Habitat suitability
  • Random forest
  • Relative density
  • species abundance
  • Partial least squares regression
  • GLMs

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