Precise forecasting in the first few days of an infectious disease outbreak is challenging. However, Oxford Mathematical Biologist Robin Thompson and colleagues at Cambridge University have used mathematical modelling to show that for accurate epidemic prediction, it is necessary to develop and deploy diagnostic tests that can distinguish between hosts that are healthy and those that are infected but not yet showing symptoms. The data derived from these tests must then be integrated into epidemic models.
“We used Ebola virus disease as the main case study in this paper" says co-author Nik Cunniffe, "since at the date of publication it was an important and very timely example of the type of disease we focus on in our research, i.e. one for which reporting is incomplete and where some epidemics die out naturally before infecting a large number of people.”
Robin is currently working on a probabilistic modelling framework for managing outbreaks of diseases such as bovine tuberculosis and foot-and-mouth disease: “I am investigating the optimal time to introduce control of a newly invading pathogen. Early control can be beneficial since the outbreak might be suppressed before the pathogen sweeps through the population. However, later control carries the advantage that it allows transmission parameters to be estimated more accurately and interventions to be optimised. Deciding when to initiate control is therefore an optimal stopping problem, and involves balancing the benefits of waiting against the potential costs of the pathogen becoming widespread.”
The team have won the PLoS Computational Biology Research Prize 2017 for the public impact of their work about diagnostic testing for Ebola. Robin is now a Junior Research Fellow at Christ Church in Oxford. He undertook this project as part of his PhD studies in Cambridge.