We are pleased to invite applications for the AXA Climate and Health studentship in the field of vector-borne disease epidemiology, modelling and climate science. One studentship is available, funded by the AXA research foundation, starting in January 2024 for a duration of 3 years.

The award will cover a stipend (Y1 GBP 22,278, Y2 GBP 24,093, Y3 GBP 26,057), tuition fees (at the “home” rate only), a high-specification laptop computer, training courses and travel to international conferences.

The successful candidate will contribute to building the Global Dengue Observatory, a web-based system that uses up-to-date dengue case data and climate information to issue regional outbreak forecasts that governments can use to prevent outbreaks. Over the course of this PhD, you will develop computational and statistical expertise to extract, clean and manage data and implement forecasting models all with the potential to have a substantial and lasting impact on how dengue control is implemented worldwide.

The need

Dengue is one of the fastest growing climate-sensitive, emerging infectious diseases globally. From isolated outbreaks in the 1940s, dengue is now present in over 120 countries causing around 100 million cases a year often concentrated in outbreaks that overwhelm health systems. Early detection and rapid response is key to stopping epidemics and the high disease burden they impose. Fortunately, we can use dengue’s close link with climate to our advantage. For mosquito-transmitted diseases, the temperature and rainfall today are predictive of risk several weeks or months in advance because of their delayed effects on increasing mosquito populations. This provides an opportunity to forecast risk, however developing a global dengue forecasting system needs to overcome three main evidence gaps related to data, models, and communication.

Objective 1: Developing a global dengue database
We will build and maintain a global database of monthly dengue case counts for over 50 countries to ensure we always have up-to-date data. This will build on the work of the OpenDengue project (https:/OpenDengue.org) that has already assembled historical (1990-2022) data on dengue dynamics for countries across the dengue-endemic world.

You will be responsible for developing new automated methods to extract, process and standardise data from a variety of sources to enable routine data collection that ensures our database is always up to date. This will require a range of technical computational data management skills (implemented in automated, reusable scripts, ideally in R), but also clear communication skills with our collaborators at the World Health Organization and with country ministry of health personnel worldwide.

You will also be responsible for developing a “reporting delay” model that smooths out often noisy real-time dengue data (e.g. lags in reporting, weekend effects, etc) to ensure the data you collect it is immediately interpretable and re-usable by a range of stakeholders. This will require an understanding of statistics and regression modelling with some experience of machine learning desirable.

Objective 2: Training and testing nowcast and forecast models
A postdoctoral researcher will adapt proven Bayesian geospatial modelling frameworks to predict dengue incidence and outbreak risk for each country up to three months ahead. The database developed in WP1 will be combined with high resolution satellite-derived climate databases that include variables that we have shown to be highly predictive of dengue risk including temperature, precipitation, humidity, wind speed and sea surface anomalies (sourced from the ERA5 reanalysis products from ECMWF). A key feature of these models is the inclusion of spatial and temporally structured effects that make model predictions robust to gaps in data from certain months or countries. The postdoctoral researcher will be responsible for model development and initial testing, after which you will be responsible for routine running, checking, fine tuning and evaluating the predictions of the model. This model will produce a nowcast (ensuring we always know the current global situation even if some countries are late in reporting) and a probabilistic forecast of exceeding a pre-determined outbreak threshold. These forecasts will be continually validated across a range of metrics (including policy-relevant outcome metrics) both on historic and newly collected data. You will need to have some familiarity with probabilistic predictive models and data management to re-use their predictions. You will need to demonstrate creativity in data visualisation to summarise outputs for a range of different audiences, and will need to, over time, develop skills in code documentation (making use of systems like Rmarkdown and Github) that allows others to re-use your data processing approaches.

Objective 3: Issuing alerts and evaluating responses
After developing real-time data pipelines and testing the forecasting model, we will issue a series of monthly global reports and specific country outbreak warnings which we will disseminate to ministries of health via WHO to enable outbreak prevention. The monthly global dengue reports will summarise the comparable nowcast and forecast (Objective 2) to generate a publicly accessible situation report that identifies areas where climatic conditions are causing or will cause abnormally high dengue incidence over the coming months. While the system will primarily focus on making predictions in dengue-endemic countries where data is most abundant, we will also issue warnings for non-endemic regions in Europe and North America based on climatic suitability for dengue transmission and air travel connectivity with areas currently experiencing outbreaks. These reports will serve as advocacy tools, but also as an easily sharable advertisement for the more detailed global forecast which specialists (scientists, policy makers, funders) can then further explore through an open access website. These reports will build on the success of the Copernicus climate bulletins and bring infectious disease forecasting one step closer to the successes of weather forecasting. When we predict a high outbreak risk for a specific country, we will issue a direct outbreak warning to the country ministry of health via the WHO country office. This report will summarise in detail the forecast, but also the climate data that has informed the model so stakeholders can understand why a certain prediction has been made. A follow-up questionnaire will then ask whether and how this forecast informed communication and mosquito control actions over the coming months and how the outbreak report could be improved. We will design the format of these reports as a team, but over time you will be increasingly responsible for managing relationships with specific countries including answering questions from stakeholders and customising forecasts for specific country needs.

It is anticipated that your 3-4 PhD thesis chapters will be based around these objectives, but we can be flexible to the specific expertise and interests of the candidate and could adapt or extend work along one of these themes.

Key publications

Colón-González, F. J. et al. Probabilistic seasonal dengue forecasting in Vietnam using superensembles. PLoS Med. 18, e1003542 (2021).
Chen Y, Li N, Lourenço J, Wang L, Cazelles B, Dong L, … Brady OJ. Measuring the effects of COVID-19-related disruption on dengue transmission in southeast Asia and Latin America: a statistical modelling study. Lancet Infectious Diseases (2022) 22 (5), 657-667 10.1016/S1473-3099(22)00025-1.
Brady, OJ, Hay, SI, The Global Expansion of Dengue: How Aedes aegypti Mosquitoes Enabled the First Pandemic Arbovirus. Annual Reviews of Entomology: 65, 191-208

Who you will be working with

While a PhD primarily requires you to work independently and take ownership of your own work, you will also have access to a broad range of expertise at different levels. Your primary supervisor will be Dr. Oliver Brady who brings expertise in dengue epidemiology and secondary supervisor Dr. Yang Liu who specialises in modelling of infectious diseases.

Day-to-day you will work with the dengue observatory team (yourself, one postdoctoral researcher and Oliver Brady) and other members of the Dengue Mapping and Modelling Group (DMMG). We are a medium sized (6-9 people) collaborative and friendly group with a range of technical and applied expertise with approachable colleagues across a range of career stages. The DMMG group and this studentship are “remote friendly” with a required minimum in person attendance in London of 8 days a month.

You will also be part of the (~ 100 people) Centre for the Mathematical Modelling of Infectious Diseases (CMMID) and one of the largest infectious disease departments in Europe (Infectious Disease Epidemiology), allowing you to develop and specialise your modelling expertise and apply such skills to a broad range of applied public health issues.

We strongly encourage potential applicants to contact the supervisors (oliver.brady@lshtm.ac.uk, Yang.Liu@lshtm.ac.uk) for an informal discussion before applying to learn more about the role and their potential suitability. If you would like to know more about the working culture of the DMMG group or the wider experience of doing a PhD at LSHTM, please contact current PhD students Katie Tiley (katherine.tiley@lshtm.ac.uk) or Ciara Judge (Ciara.Judge@lshtm.ac.uk).

By the end of this PhD you will have:
An ability to implement complex data management and modelling analyses in R
An understanding of the state of the art of infectious disease forecasting methodology
Experience working with ministries of health worldwide and the World Health Organization
Experience independently managing large, multi-partner projects through to completion
Past group members developing similar skillsets have gone on to secure highly competitive jobs in academia, government health departments, health-focused non-governmental organisations, research funders and industry.

Eligibility requirements

While this studentship will expose the candidate to multidisciplinary research skills across climate and health fields, the core skills needed on a day-to-day basis for this studentship are computational and technical with a high attention to detail being critical. You should have an interest in developing automated code-based approaches to extract, standardise and process data at scale and a desire to make research outputs that are re-useable and immediately useful to a range of stakeholders. You will be comfortable taking ownership of your work and problem solving independently, but also know when to collaborate to achieve objectives most efficiently.

Essential criteria

You must hold, or expect to obtain before the start of the PhD, a relevant (typically quantitative science-based) MSc awarded with good grades, or have a combination of relevant qualifications and experience which demonstrates equivalent ability and attainment.
An ability to independently learn and problem solve in an efficient way that leads to long-term term development of new skills
An ability to take ownership of your own work, contribute new research ideas, and take responsibility for meeting deadlines

Desirable criteria

Computer coding expertise, preferably in R
Experience working with inconsistent and incomplete datasets
Understanding of predictive statistical and machine learning modelling techniques
An understanding of approaches to making your research replicable and reusable (including the use of GitHub, protocols, vignettes, etc)
An understanding of the basic concepts of epidemiology and the challenges of analysing observational epidemiological data
Experience working with ministries of health, particularly explaining complex interpretations of data or modelling analyses

How to apply

Information about the MPhil/PhD programme structure at LSHTM, as well as application guidance and a link to the portal, can be found on the School's Research Degrees and Doctoral College pages.

To apply for this studentship, applicants should submit an application for research degree study via the LSHTM application portal. Please write “AXA Climate and Health studentship” in the Funding Section on the application form. You must supply all of the supporting documents that are specified in the portal.

Instead of submitting a project proposal please give evidence of your ability to meet the essential and desirable criteria listed above. Answers should be 1-2 paragraphs long (per bullet point). Statements such as “see CV” will not be scored. Please type “Project proposal” at the top of your answer sheet(s) and upload to the portal

If invited for interview, there will be a short experimental design problem, shared in advance, that will be used to assess some of the technical aspects of your experimental approach to problems of relevance to this project.

Applications for this project will only be reviewed and processed after the deadline. All complete applications that are submitted before the deadline will be considered equally, regardless of submission date.

Only applications in the correct format will be considered.