DEPARTMENT FOR BUSINESS, ENERGY & INDUSTRIAL STRATEGY

Ecology of insecticide resistant vectors: consequences for the effectiveness of malaria control strategies

Project disclaimer

Disclaimer: The data for this page has been produced from IATI data published by DEPARTMENT FOR BUSINESS, ENERGY & INDUSTRIAL STRATEGY. Please contact them (Show Email Address) if you have any questions about their data.

Description

Insecticides impregnated into bednets is the most widespread strategy to control and eliminate malaria worldwide. Insecticides work by killing mosquitoes that can transmit malaria and have been extremely successful in reducing malaria cases throughout sub-Saharan Africa. However, mosquitos are increasingly resistant to insecticides, threatening to reduce their effectiveness. Despite the gravity of this impending threat, the extent of insecticide resistance (IR) and its consequences for public health remain poorly understood. A fundamental assumption is that resistant mosquitoes are identical to susceptible ones in all aspects other than their response to insecticides. However, there are several reasons why this may not be the case. For example, malaria transmission is known to be more sensitive to variation in the long-term survival and behaviour of mosquitoes than to their overall abundance. These features, in addition to other mosquito life-history traits such as their reproductive success, may be altered in resistant mosquitoes. Together this could result in insecticide-based control methods retaining a higher than expected degree of efficacy, even in areas where IR levels are high, which would suggest the possibility of developing control methods to mitigate the consequences of resistance i.e. "resistance busting strategies". Thus understanding the ecology and behaviour of IR mosquitoes and how their life history is affected in the short and long-term by control measures is crucial for prediction of the consequences of resistance. Unfortunately, these parameters are difficult to directly measure under natural field conditions, and may be poorly reflected in laboratory bioassays. However, recent developments in ecological modelling have delivered breakthrough, but as yet rarely applied, methods for deriving such hidden (latent) information from the multiple types of data that are routinely collected in mosquito surveillance. I propose to use these new methods to investigate the population dynamics and ecology of malaria mosquitoes in an area of high IR, and to quantify the impacts of both traditional and novel control methods on mosquito life history under field operational conditions. I will focus on the Banfora district of Burkina Faso, a region of high IR, and will make use of mosquito surveillance data, including mosquito abundance, infectiousness, IR status and behaviour, being collected by my collaborators in the AvecNet programme. This data collection is part of a 2-year 90-villages large-scale intervention trial of two alternative malaria control methods for which laboratory assays predict will reduce mosquito populations through two different routes: 1) traditional bednets (LLIN) that are coated with the insecticide pyrethroid, which works by killing adult female mosquitos; and 2) new Olyset DUO nets that are coated with a pyrethroid to reduce adult mosquito survival but also with pyriproxifen, an insect juvenile hormone that affects fecundity and longevity. However, it is unclear how reliably these expected demographic impacts occur when applied within natural populations. To address these issues I will apply novel analytical tools that integrate the various types of data generated from widely-used surveillance techniques with clinical incidence data, in order to reconstruct the hidden population dynamics and life history-traits of IR mosquito vectors, and use it to 1) determine how the ecology of IR mosquitoes modulates their malaria transmission potential, 2) quantify the impacts of current and novel control methods on these mosquitoes and 3) design optimal deployment of future intervention methods in areas of high IR.

Objectives

The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world.

---

Location

The country, countries or regions that benefit from this Programme.

Burkina Faso

Disclaimer: Country borders do not necessarily reflect the UK Government's official position.