Interoperability of statistical models in pandemic preparedness: principles and reality

TL;DR

This paper proposes interoperability as a framework for designing adaptable statistical models to improve pandemic preparedness and response, demonstrated through COVID-19 case studies in England.

Contribution

It introduces interoperability principles for statistical modeling in pandemics and illustrates their application with real-world COVID-19 data analysis.

Findings

Interoperability aids in integrating diverse datasets for pandemic modeling.

The framework supports adaptable and probabilistic disease surveillance systems.

Case studies demonstrate effective inference of COVID-19 prevalence and reproduction numbers.

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 spatial-temporal coronavirus disease 2019 (COVID-19) prevalence and reproduction numbers in England.