Computation and inference

Figure: Epidemic size predicted using Approximate Bayesian Computation (R. Eggo 2018).

This theme provides a space for the exploration of ideas for efficient computation, to learn new methodologies for inference and to share knowledge across CMMI

In the CMMID we use mathematical and statistical tools to understand the dynamics and control of infection. Members use methods of inference to inform data based decisions which can account for large and/or complex data, models and questions. In addition, to deal with these complexities, there is a need for efficient computation. From methods to account for partial observation of cases and uncertainty in confirmation of cases, to tools for creating fast and reproducible code, challenges arise in both computation and inference that are common to many infectious disease research questions.


Amanda Minter (theme co-ordinator), Katherine Atkins, Lloyd Chapman, Sam Clifford, Nick Davies, Roz Eggo, Jon Emery, Akira EndoFlavio Finger, Stefan Flasche, Seb Funk, Alasdair Henderson, Chris Jarvis, Petra Klepac, Gwen Knight, Adam Kucharski, Yang Liu, Nicky McCreesh, Hannah Meredith, James Munday, Amy Pinsent, Kathleen O’Reilly, Alexis Robert, Tom Sumner, Moritz Wagner, Naomi Walker, Nayantara Wijayanandana, Kevin van Zandvoort

Previous events

  • October 2018 : Introduction to data augmentation for infectious disease modelling. Ali Henderson, Amanda Minter, Lloyd Chapman and Kath O’Reilly presented examples of data augmentation from their research (slides available here).
  • Sep 2018 : Tom Hladish from the University of Florida gave a “methods roundtable” on ABC-SMC (Approximate Bayesian Computation – Sequential Monte Carlo)


  • O’Reilly KM, Cori A, Durry E, Wadood MZ, Bosan A, Aylward RB, et al. (2015) A new method to estimate the coverage of mass vaccination campaigns against poliomyelitis from surveillance data. Am J Epidemiol.;182:961–970. pmid:26568569
  • Kucharski AJ, Edmunds WJ (2015) Characterizing the transmission potential of zoonotic infections from minor outbreaks. PLOS Comput Biol 11(4):e1004154
  • Kucharski AJ, Lessler J, Read JM, Zhu H, Jiang CQ et al. (2015) Estimating the life course of influenza A(H3N2) antibody responses from cross-sectional data. PLOS Biol 13(3):e1002082
  • Kucharski AJ, Mills HL, Pinsent A, Fraser C, Van Kerkhove MD et al. (2014) Distinguishing between reservoir exposure and human-to-human transmission for emerging pathogens using case onset data. PLOS Curr. 7:6


Resources including recommended books/journals for those new and/or interested in computation and inference are listed below.


Markov Chain Monte Carlo

Approximate Bayesian Computation

  • Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MPH. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J. R. Soc. Interface 6 187-202; DOI: 10.1098/rsif.2008.0172.
  • Hartig, F. , Calabrese, J. M., Reineking, B. , Wiegand, T. and Huth, A. (2011), Statistical inference for stochastic simulation models – theory and application. Ecology Letters, 14: 816-827. doi:1111/j.1461-0248.2011.01640.x
  • Sunnåker M, Busetto AG, Numminen E, Corander J, Foll M, et al. (2013) Approximate Bayesian Computation. PLOS Computational Biology 9(1): e1002803.
  • McKinley, Trevelyan J.; Vernon, Ian; Andrianakis, Ioannis; McCreesh, Nicky; Oakley, Jeremy E.; Nsubuga, Rebecca N.; Goldstein, Michael; White, Richard G. Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models. Statist. Sci. 33 (2018), no. 1, 4–18. doi:10.1214/17-STS618.

Comparison of MCMC and ABC

  • McKinley, T., Cook, A. R. and Deardon, R. (2009). Inference in epidemic models without likelihoods.  J. Biostat.5.


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