Characterising the Behavioural Differences between Insecticide Susceptible and Insecticide Resistant Mosquito Species - 1=Sensors and instrumentation

Lead Research Organisation: University of Warwick
Department Name: Sch of Engineering


Mosquito-related diseases are responsible for approximately 1 million deaths every year and so research into reducing disease transmission is vital. Mosquitoes are a disease-vector for some of the deadliest viral and parasitic infections affecting both humans and animals, such as malaria, dengue, Chikungunya, West Nile Fever and many others [1]. Long-lasting insecticidal bed nets (LLINs) and indoor residual spraying (IRS) are the principle intervention methods currently used and have worked effectively to date [2]. However, there is growing concern of increasing insecticide resistance within mosquito populations. A report by the Center for Disease Control and Prevention found that resistance in mosquitoes has increased by over 1000-fold, which renders some current insecticides ineffective [3]. There has been some research into the physiological resistance of mosquitoes, but behavioural resistance is much less understood. Classifying and understanding the differences in behaviour between insecticide susceptible and insecticide resistant mosquito species will assist with understanding mosquito behaviour and further aid the development of novel effective intervention treatments.
The University of Warwick and the Liverpool of Tropical Medicine have previously developed systems to identify and track the flight of mosquitoes [2]. The proposed study would take this project further by utilising this experimental data to identify key behavioural flight features of insecticide susceptible (IS) and insecticide resistant (IR) mosquitoes and the differences between them. To perform this, machine learning models such as artificial neural networks, random forest decision trees and k-nearest neighbours would be developed, trained, and evaluated. A stretch target could use these classifiers to develop a system for real-time analysis of mosquito species to further assist with research.
The principal objectives of the PhD project are:
- To collect or obtain suitable training datasets of mosquito flight (2D or 3D tracks).
- To generate and select various features for analysis.
- To identify suitable machine learning classifiers, and to build, test and evaluate them.
- To determine the behavioural features that differ the most between the insecticide susceptible and insecticide resistant mosquito species.
- Stretch targets would be to develop a system for real-time classification using a low-cost camera system or explore (with LSTM) how the identified behavioural features could be exploited to improve the efficacy of intervention methods.
The project will begin by quantifying various features and associated statistics from measurements of mosquito flight. Features selection is then performed, using methods such as: filter methods, wrapper methods and embedded methods. A combination of these algorithms will be used, as each has various advantages and disadvantages. The result of this will be a set of features that are unique and hold relevant information that enable the model to classify the mosquitoes. The final stage of the project would include the classification, which entails building, testing, and evaluating the machine learning classifiers. The classifiers may include random forests, support vector machines and naïve bayes - which worked well in a study on the classification of birds [4]. However, further study into the set of machine learning models used would need to take place.

[1] 2021. Mosquito-borne diseases | World Mosquito Program. [online] Available at:
[2] J. Parker, N. et al Scientific Reports, vol. 5, no. 13392, 2015.
[3] Toé, K. H. et al., Emerging Infectious Diseases, vol. 20, no. 10, 2014.
[4] Atanbori, J et al Pattern Recognition Letters, 81, pp.53-62.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/T51794X/1 30/09/2020 29/09/2025
2539121 Studentship EP/T51794X/1 03/10/2021 30/03/2025 Yasser QURESHI