Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

Journal article

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.

Authors: Nicholson, G; Blangiardo, M; Briers, M; Diggle, PJ; Fjelde, TE

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Mathematical Statistics
• Journal: Statistical Science
• Volume: 37
• Issue: 2
• Pages: 183-206
• Publication date: 2022-05-01
• DOI: 10.1214/22-sts854
• EISSN: 2168-8745
• ISSN: 0883-4237
• Language: English
• Keywords: Artificial intelligence; Data science; Statistical model; Software engineering; Software deployment; Preparedness; Disease; Pandemic; Engineering; Computer science; World Wide Web; Medicine; Interoperability; Coronavirus disease 2019 (COVID-19); Probabilistic logic; Infectious disease (medical specialty); Set (abstract data type); Operations research; Political science