Project Title: Modelling to Optimize Vector Elimination Destabilising Mosquito Population.
Project Description: Control of vector-borne diseases from Chagas to Malaria to Dengue largely relies on reducing or eliminating the arthropod vector populations. These public health initiatives routinely lead to at least initial declines in vector populations. The challenge is that as populations decline, unexpected evolutionary (such as insecticide resistance) and ecological changes (such as population fragmentation and altered density-dependence) can occur that might facilitate or undermine control efforts. However, the relative importance of these ecological intra- and... Control of vector-borne diseases from Chagas to Malaria to Dengue largely relies on reducing or eliminating the arthropod vector populations. These public health initiatives routinely lead to at least initial declines in vector populations. The challenge is that as populations decline, unexpected evolutionary (such as insecticide resistance) and ecological changes (such as population fragmentation and altered density-dependence) can occur that might facilitate or undermine control efforts. However, the relative importance of these ecological intra- and inter-specific processes in regulating vector populations is almost unknown, which hinders the prediction of vector population dynamics and how different interventions might be most effectively deployed to sustainably suppress vectors. Although vector surveillance has generated extensive high-resolution time series datasets to assess the factors that underpin population persistence and regulation, the cutting-edge analytical tools required to overcome the complexity of these data have been mostly developed by ecologists and have rarely been applied in medical entomology. Filling both these knowledge and methodological gaps will require closer integration of public health science, medical entomology and ecology that I intend to deliver through this proposal. Our team will contribute in developing and applying sophisticated state-space models to longitudinal vector surveillance data from Tanzania and probably four other malaria endemic countries.
Principal Investigator : Samson Kiware
Department Name : EHES
Time frame: (2020-02-01) - (2024-08-31)
The University Court of the University of Glasgow (UoG) (Normal)
External Collaborating Partners
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