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Epidemic modelling with online model assessment: value of information and conflict

  • Full or part time
    Dr A Presanis
    Dr C Jackson
    Dr D DeAngelis
  • Application Deadline
    Tuesday, January 07, 2020
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

For a fast-moving infectious disease such as influenza, whether seasonal or pandemic, or a newly emergent infectious disease, the ability to quickly evaluate the current spread of disease is crucial for understanding and forecasting the burden, particularly severe burden, on public health services such as GPs and hospitals. Evidence synthesis approaches to influenza transmission and severity estimation (e.g. Birrell et al (2011, 2018), Presanis et al (2014)) lead to complex hierarchical models, possibly non-linear, based on multiple data sources that are observational, may be subject to selection and other biases, and may be arriving daily in the midst of an outbreak. Such data sources might include GP consultations for influenza-like-illness and sentinel surveillance of virological testing and hospital admissions, for example.

In such a context, it is important to understand how each data source influences the estimates from the models, where the most important sources of uncertainty are, and what further data should be collected to improve the accuracy and precision of the results. The project will use and adapt Value of Information methods for evidence synthesis (Jackson et al, 2018) to the more complex and computationally-expensive models necessary for transmission modelling. A particular focus will be on prioritising improvements to models that inform decision-making for epidemic control in real time (Probert et al, 2018).

A second focus will be in the area of detecting and measuring conflict between data sources and assessing the consistency of the model with each data source. Understanding such potential conflict, e.g. between model predictions and observations, is even more crucial when modelling transmission in real-time, to allow for rapid model adaptation and development to account for such conflict. Methods to assess predictive ability such as scoring rules (Held et al, 2017), which are highly related to the “conflict p-value” (Presanis et al (2013, 2018)), a posterior predictive approach to measure conflict, based on cross-validation approaches separating evidence into independent partitions, will be explored in the context of real-time estimation (Birrell et al (2018), Nemeth et al (2014)).

Tying together these two strands will be an investigation of the relationship between power to detect conflict and methods measuring the value of collecting more information. Further possible directions to investigate, in application to transmission modelling, include: adapting approximate computational approaches (e.g. INLA, Ferkingstad et al (2017)) or efficient model-building methods (Goudie et al, 2018) to the problem of systematic conflict assessment; or adapting conflict assessment approaches to complex models where the likelihood is intractable, as is common in the epidemic literature (Nott et al, 2019).

This project would suit a student motivated by developing methods to solve substantive public health problems, that will have a direct impact on public health policy. The project will be carried out in close collaboration with the Respiratory and Statistics departments at Public Health England.

Funding Notes

The MRC Biostatistics Unit offers 4 fulltime PhDs funded by the Medical Research Council for commencement in April 2020 (UK applicants only) or October 2020 (all applicants). Academic and Residence eligibility criteria apply.

In order to be formally considered all applicants must complete a University of Cambridge application form. Informal enquiries are welcome to

Applications received via the University application system will all be considered as a gathered field after the closing date 7th January 2020

For all queries see our website for details View Website

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