Home > Research > Publications & Outputs > Interoperability of Statistical Models in Pande...

Links

Text available via DOI:

View graph of relations

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
  • George Nicholson
  • Marta Blangiardo
  • Mark Briers
  • Peter J. Diggle
  • Tor Erlend Fjelde
  • Hong Ge
  • Robert J. B. Goudie
  • Radka Jersakova
  • Ruairidh E. King
  • Brieuc C. L. Lehmann
  • Ann-Marie Mallon
  • Tullia Padellini
  • Yee Whye Teh
  • Chris Holmes
  • Sylvia Richardson
Close
<mark>Journal publication date</mark>1/05/2022
<mark>Journal</mark>Statistical Science
Issue number2
Volume37
Number of pages24
Pages (from-to)183-206
Publication StatusPublished
<mark>Original language</mark>English

Abstract

We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.