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Mathematical modelling of household data for real-time infectious disease surveillance

Title of PhD project

Household data for real-time infectious disease surveillance

Supervisory team

Nagasaki University 

Akira Endo (akiraendo@nagasaki-u.ac.jp) 

LSHTM 

Sebastian Funk (Faculty of Epidemiology and Population Health, sebastian.funk@lshtm.ac.uk) 

Brief description of project

The main aim of this project would be to analyse data from a large study of SARS-CoV-2 transmission in UK households. This will be used to answer questions such as which transmission-relevant parameters such as variant-specific incubation periods, generation times or transmission rates can be estimated from this unique data set, and how did transmission depend on age and transmission status. 

Throughout the PhD, these will be linked to broader questions of pandemic preparedness, such as how household studies should be designed to maximise utility early in an outbreak, how quickly information can be obtained from partially observed data sets, and how data from household studies are best combined with population-level surveillance data to maximise the available information. 

There will be scope to investigate the impact of household structure, and potentially to include phylogenetic data from the same study. If the student is also interested in within-household transmission dynamics in more general contexts, other data sources are also available including an influenza dataset among households of primary school students in Japan and a flu-like illness dataset among households of secondary school students in the UK. 

References: 

  1. Hart WS, Abbott S, Endo A, Hellewell J, Miller E, Andrews N, Maini PK, Funk S, Thompson RN. Inference of the SARS-CoV-2 generation time using UK household data. Elife. 2022 Feb 9;11:e70767. doi: 10.7554/eLife.70767. 

  1. Hart WS, Miller E, Andrews NJ, Waight P, Maini PK, Funk S, Thompson RN. Generation time of the alpha and delta SARS-CoV-2 variants: an epidemiological analysis. Lancet Infect Dis. 2022 May;22(5):603-610. doi: 10.1016/S1473-3099(22)00001-9. Epub 2022 Feb 14. PMID: 35176230; PMCID: PMC8843191. 

  1. Endo A, Uchida M, Kucharski AJ, Funk S. Fine-scale family structure shapes influenza transmission risk in households: Insights from primary schools in Matsumoto city, 2014/15. PLoS Comput Biol. 2019 Dec 26;15(12):e1007589. doi: 10.1371/journal.pcbi.1007589. PMID: 31877122; PMCID: PMC6959609. 

The role of LSHTM and NU in this collaborative project

The two supervisors will work collaboratively, with AE (Nagasaki) providing methodological advice based on past experience with analysing household data, and SF (LSHTM) additionally providing experience with the data set in question. There will be scope to join group meetings of SF’s group in London, and to spend extensive time in both locations. 

Particular prior educational requirements for a student undertaking this project

Some familiarity with R.

A good grasp of quantitative methods, involving a background in e.g. statistics, mathematical modelling, bioinformatics, physics or another quantitative subject, or e.g. epidemiology with a strong quantitative component. The project will likely involve modelling of stochastic transmission processes

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

  • Analysis of infectious disease data
  • Survival analysis 
  • Development and fitting of household transmission models