Modelling integrated strategies for flavivirus diseases

Project Overview

With the releases of Wolbachia infected mosquitoes in many countries around the World, the elimination of life-threatening vector-borne diseases like dengue is on the horizon. The time is right to explore how attacking one vector species can lead to gains in the control of multiple diseases. Also, it is important to project the expected programmatic impact of the roll out of the vector control approaches currently under trial. Questions remain about the ideal strategy for rolling out new interventions, how frequently, in what order, how they work in concert with other interventions both current and future (e.g. vaccination). An in silico simulation environment to combine all knowledge on the settings, biology, trial data and costs would be ideal to answer some of these questions. To understand the epidemiological setting, a platform will be created which maps the historical and current spatial distribution of the environment, vectors and cases for a target areas or country. Overlaid on this, will be a bespoke, spatially explicit transmission-dynamic model to be applied at various levels of resolution. The structure will be used to predict the impact of current and future prevention and control strategies.

The aim is to use dynamic spatial modelling to support trial and implementation strategy design of interventions against the flaviviruses.

The objectives are:

  1. Development of individual and multiple species models for the transmission and control of the flaviviruses
  2. Model-driven exploration of a range of intervention strategies
  3. A review and analysis of all publicly available data on the spatial distribution of relevant environmental factors, vector abundance and surveillance data
  4. Development of a trial and implementation simulation environment

This project would be most suited to a candidate with a strong background in statistics, dynamical systems and/or spatial modelling applied to infectious diseases. Experience of programming in a high-level language is also desirable.

Training Opportunities

The successful candidate will be provided with a bespoke training package with online and residential courses to support their research project and fill in knowledge gaps designed in partnership with the project supervisors.


Tropical Medicine & Global Health and Clinical Trials & Epidemiology


Project reference number: 1043

Funding and admissions information


Name Department Institution Country Email
Lisa White Big Data Institute Oxford University, Bangkok THA
Jose Lourenco Zoology Univeristy of Oxford GBR
Penny Hancock NDPH University of Oxford GBR

There are no publications listed for this DPhil project.