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===Background===

The 2022 global mpox outbreak underscored the interconnectedness of public health as a previously geographically-limited disease spread rapidly around the world. Understanding the drivers of this outbreak, and the factors that led to the epidemic receding, are critical to understanding the risk of a global mpox recurrence and preparing pharmaceutical and non-pharmaceutical intervention strategies for responding to such a recurrence. Moreover, understanding the dynamics of the outbreak in higher-income settings, where relatively good data may be available, may help to inform mpox response in endemic regions, thus contributing to our understanding of a neglected disease.

===Proposed project===

This project has three key objectives. First, to review the global epidemiology of the 2022 mpox outbreak by collecting and analysing key data streams. This will involve both a global data collection component and a targeted data collection component for a smaller number of countries with richer data. Specifically, the student will work with UKHSA to analyse more detailed UK-specific data on the outbreak, as well as data from at least one other country. As countries in Asia typically experienced later and smaller mpox outbreaks, analysis of data from Japan or another country in the region may serve as a useful contrast. Depending on the available resource and student’s interest, phylogenetic analysis of sequenced mpox genomes could also be used to help understand the epidemiology of the outbreak. Second, to model the 2022 global mpox outbreak to understand how the drivers of the end of the epidemic — depletion of high-risk susceptibles, behavioural change, and vaccination — impact upon the long-term risk of recurrence of an outbreak. Third, to explore potential intervention strategies, primarily vaccination of high-risk groups, to evaluate strategies for mitigating the risk of recurrence.

=== Supervisory team ===

LSHTM:

• Lead: Dr Nicholas Davies (Nicholas.Davies@lshtm.ac.uk)
• Prof. John Edmunds
  Nagasaki:
• Dr Akira Endo
• Prof. Koya Ariyoshi (TBC)

=== Key References ===

Endo A et al. 2022. Heavy-tailed sexual contact networks and monkeypox epidemiology in the global outbreak, 2022. Science 378:90–94. doi: 10.1126/science.add4507
Brand et al. 2023. The role of vaccination and public awareness in forecasts of Mpox incidence in the United Kingdom. Nature Communications 14: 4100. doi: 10.1038/s41467-023-38816-8
Endo A et al. 2022. Mpox emergence in Japan: ongoing risk of establishment in Asia. Lancet 401:1923–1924. doi: 10.1016/S0140-6736(23)00766-3
Borges V et al. 2023. Viral genetic clustering and transmission dynamics of the 2022 mpox outbreak in Portugal. Nature Medicine 29:2509–2517. doi: 10.1038/s41591-023-02542-x
Murayama H et al. 2023. Accumulation of immunity in heavy-tailed sexual contact networks shapes monkeypox outbreak sizes. J Infect Dis. doi:10.1093/infdis/jiad254

=== Further details ===

The selected student will conduct most of the research at LSHTM in London. It may be helpful for the student to spend a period of time based elsewhere to facilitate collection and analysis of data.

Nicholas Davies’ research is on modelling and inference of infectious disease transmission to better understand how behavioural changes, interventions, and pathogen evolution impact upon the risk of infection. He is working with Nigeria CDC on modelling endemic mpox.

John Edmunds’ research centres on understanding the spread of infectious diseases and how best to control them. This uses a variety of techniques, including mathematical, statistical, and economic models. He is particularly interested in using these methods to help produce evidence-based public health policy.

Akira Endo’s research has been focused on analysing the role of social structures and networks in shaping transmission dynamics of infectious diseases. His recent work includes modelling studies on the current global mpox outbreak.

====Particular prior educational requirements for a student undertaking this project ====

Essential:

• Basic understanding of infectious disease epidemiology
• Strong background in quantitative methods, e.g. mathematics, statistics, physics or epidemiology with quantitative elements
• Proficiency in at least one programming language (preferably R)

Desired:

• Experience in infectious disease modelling

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

• Modelling of network-based models of STI transmission
• Analysis of behavioural and epidemiological data
• Modelling of pharmaceutical and non-pharmaceutical interventions
• (Potentially) Phylodynamic analysis and transmission modelling