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Modelling the risk of mosquito spread and dengue emergence in the UK and Japan - NU/LSHTM project

Supervisory team

LSHTM

Nagasaki University

Department of Epidemiology, National Institute of Infectious Diseases, Japan Institute for Health Security

Project

Background

This PhD project aims to use machine learning and dynamic modelling to assess the risk of emergence of dengue in the UK and Japan. Dengue is one of the fastest growing infectious diseases spread by the invasive mosquito vectors Aedes aegypti and Ae. albopictus. While currently dengue free, the UK has recently detected arrival of Aedes mosquitoes for the first time and while Ae. albopictus continues to spread in Japan which has caused limited local outbreaks over the past decade. Growing cases in Europe and South East Asia, climate change and increased international travel all threaten to increase the risk of emergence of dengue in the UK and Japan.

Proposed project

In this project we aim to estimate the current and future risk of dengue importation and local transmission across the UK and Japan at different times of the year and model the effectiveness of different surveillance and control strategies. This will be achieved through three objectives:

  1. Predict the risk of importation and spread of Aedes Aegypti and Ae. albopictus in the UK and Japan using machine-learning methods. Establishment of local mosquito vectors are a required before dengue viruses can be locally transmitted. Following methods developed by Dr. Brady's group [1] we will use ecological niche models, high resolution climate datasets and mosquito occurrence data from initiative such as MosquitoAlert to map seasonally-varying environmental suitability for these vector species in the UK and Japan. Mosquito spread models [1] will then be fit to predict the rate and path of spread of these species over historical (~2000-2024) and future (up to 2050) years. These predictions can be used to target mosquito surveillance and control and bring new insights into how these mosquitoes are spreading including ecological and dispersal differences between the UK and Japan.
  2. Predict the risk of future dengue outbreaks in the UK and Japan. After establishment of a competent vector population importation of dengue viruses from endemic regions is still required to cause an outbreak. By pairing a temperature-driven dynamic dengue transmission model with international flight data and data from dengue-endemic countries from Dr. Brady's OpenDengue project we will be able to model dengue importation and emergence [2]. Calibration of the model to dengue and chikungunya outbreaks in Europe and Japan will bring new insights into how vector-borne diseases emerge and provide a tool for ministries of health to assess the risk of single cases developing into outbreaks.
  3. Modelling surveillance and response interventions to predict chances of containment. Mosquito control intervention effectiveness and costs will be added to the model from Objective 2 and a range of detection and response strategies modelled. These will assess improvements in cost and effectiveness of targeting surveillance to high mobility transit hubs and ports. This will help design sustainable cost-effective monitoring systems that are still capable of containing mosquito importation and dengue outbreak response.

Combined this work will provide timely advice to public health agencies in the UK and Japan on how to contain an emerging global health threat and develop new scientific understanding of how emerging infectious diseases spread.

References

  • [1] Kraemer, M.U.G., Reiner, R.C., Brady, O.J. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat Microbiol 4, 854–863 (2019). https://doi.org/10.1038/s41564-019-0376-y
  • [2] Harish, V., Colón-González, F.J., Moreira, F.R.R. et al. Human movement and environmental barriers shape the emergence of dengue. Nat Commun 15, 4205 (2024). https://doi.org/10.1038/s41467-024-48465-0

The role of LSHTM and NU in this collaborative project

A core element of this project is to compare and contrast dengue emergence in the UK and Japan. The student spending time in the UK and Japan is essential to access an understand the mosquito data and communicate to users of the model outputs (principally public health agencies). This project leverages the expertise of dengue epidemiology and statistical risk mapping at LSHTM (Brady, Objectives 1 and 2) with the intervention modelling and economic evaluation expertise in Nagasaki (Abbas, Objectives 3), combined with the experience of working with Japanese infectious disease surveillance data of Sophearen Ith. We expect this project to develop new capacities in machine learning and modelling of climate sensitive diseases in both universities - an area of substantial growth.

While Dr Brady and Abbas will have primary supervisory responsibility for the student we will involve wider academic and government collaborators in the UK and Japan and have included Sophearen Ith at the National Institute of Infectious Diseases as a third supervisor and have begun discussions at UKHSA and APHA in the UK. 

Particular prior educational requirements for a student undertaking this project

  • Essential: Applicants must have a basic level of ability to write and adapt computer code for data analysis with a preference for R and Python languages.
  • Desirable: An understanding of Japanese language.

Skills we expect a student to develop/acquire whilst pursuing this project

Mathematical and statistical modelling, development and use of machine learning and artificial intelligence, experience working with complex high dimensional climate and epidemiological datasets, an understanding of the role of modelling in health policy making, develop understanding of climate change impacts and adaptation measures.