The Bloomsbury Colleges | PhD Studentships | Studentships 2021 | Combining data from genomics and epidemiology to understand and prevent yellow fever transmission
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Combining data from genomics and epidemiology to understand and prevent yellow fever transmission

Principal Supervisor at LSHTM: Dr. Oliver Brady

Principal Supervisor at RVC: Dr. Sarah Hill

Co-Supervisor: Prof. Oliver Pybus (RVC)

This fully funded PhD project aims to better understand how yellow fever virus (YFV) spreads using a unique combination of epidemiological and genetic statistical methods. YFV is thought to be maintained by “sylvatic” transmission cycles between non-human primates but spill-over events into humans are common and can lead to sustained human-to-human transmitted “urbanised” cycles with high case fatality rates. While only the sylvatic cycle is currently considered active in South America, since 2016 a series of major spill over events have occurred in the South-eastern region of Brazil and there is concern that urbanised cycles could re-initiate in the highly populous coastal cities. In Africa, it is expected that climate change will lead to more intense urban transmission of the virus. Once initiated, high human mobility increases the risk of introduction of YFV to new areas, where populations may be unvaccinated.

Understanding the routes and drivers of the emergence of YFV into new regions has been challenging because of the limited availability of human and non-human primate case data globally. Understanding how YFV has circulated in sylvatic cycles and how humans interacted with such cycles is critical for determining how the risk of YFV spill over from the sylvatic cycle is changing. Analysis of epidemiological and genomic data can provide complementary insights into current and historical transmission patterns and their combination can often compensate for temporal and spatial data gaps. This project aims to combine epidemiological and genomic data analysis techniques to answer three main objectives:

1)    How can we better understand and predict the movement of sylvatic yellow fever virus?

Depending on their interests, the applicant could address this goal through one of two approaches. Firstly, the candidate could contribute to developing new methods that allow better rapid integration of different types of data (e.g., virus genomic data, case data, environmental suitability) to understand the transmission dynamics of YFV in real-time.

Secondly, the candidate could contribute to real-time sequencing of yellow fever virus genomes through collaboration with international partners, and/or analysis of existing generated datasets of YFV genomes. Generated genomic information will be combined with environmental and ecological data in phylodynamic models to reconstruct the spread of YFV from past outbreaks.

Either approach will generate new understanding of how the virus is transmitted in different areas over time and could lead to the development of early warning systems that enable better vaccination targeting during YFV epizootic outbreaks.

2)    How is human interaction with the sylvatic cycle changing?

Spill over events require interaction between the sylvatic cycle, bridge mosquito vector species and humans. High resolution maps of each of these will be created by pairing available data with machine learning-based Ecological Niche Models. Maps of the human population will also take into account human mobility using a unique cell-phone-based dataset to try to understand which types of human movement are highest risk for YFV spillover events. By overlaying these maps high-risk sites and population risk groups for emergence of urban YFV transmission can be identified and prioritised for vaccination.

3)    Could changes in the age-sex case ratios for yellow fever give early warning of a new urbanised transmission cycle?

Through a statistical evaluation of age-sex ratios reported in YFV outbreaks from South America and Africa in the literature, we will determine whether monitoring of the age-sex ratio of cases could be used alongside genetic and other epidemiological data in an algorithm for early detection system for urban transmission.


The student will spend time training in complementary techniques at both LSHTM and the Royal Veterinary College (RVC). At RVC the student will develop skills in real-time genomic sequencing and virus genomic and phylodynamic analyses. At LSHTM the student will receive training on a range of mathematical and statistical modelling techniques as well as experience managing large multi-dimensional epidemiological datasets. This interdisciplinary PhD will require the student to develop and combine skills from both areas.

Required qualifications and skills:

We are looking for a highly-motivated candidate with strong quantitative and analytical skills with a desire to conduct research that makes meaningful improvements to public health policy. The candidate must have evidence of outstanding academic performance and must have or be predicted to obtain a Master’s degree in a quantitative science-based subject, ideally epidemiology, public health, computational data science, bioinformatics, pathogen evolutionary genetics or modelling. They should be able to demonstrate some early computational skills (e.g. baseline skills in computer coding). They also must demonstrate solid foundations in academic writing and presenting, and in independently organising aspects of their research.

Candidates wishing to conduct virus genomic sequencing should already have baseline molecular biology laboratory skills. Experience using genomic or statistical techniques to research zoonotic viruses or mosquito-borne pathogens would be desirable but not essential.

Subject Areas/Keywords

Infectious disease, yellow fever virus, zoonotic viruses, genomics, phylogenetics, phylogeography, modelling, ecology, statistical analysis, mosquitoes, vaccines.

Key References:

[1] Faria, Nuno R., et al. “Genomic and epidemiological monitoring of yellow fever virus transmission potential.” Science 61(6405) (2018): 894-899.

[2] Hill, Sarah C., et al. “Genomic Surveillance of Yellow Fever Virus Epizootic in São Paulo, Brazil, 2017-2018”. PLoS Pathogens. 16.8 (2020): e1008699.

[3] Shearer, Freya M., et al. "Existing and potential infection risk zones of yellow fever worldwide: a modelling analysis." The Lancet Global Health 6.3 (2018): e270-e278.

[4] Kraemer, Moritz UG, et al. "The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus." elife 4 (2015): e08347.

Further details about the project may be obtained from:

LSHTM: Oliver Brady (

RVC: Sarah Hill (

Further information about PhDs at LSHTM is available from:

Information about how to apply is available below:

Closing date for applications is:

09:00 1st March