This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
Interoperability of statistical models in pandemic preparedness: principles and reality (preprint)
arxiv; 2021.
Preprint
in English
| PREPRINT-ARXIV | ID: ppzbmed-2109.13730v1
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.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-ARXIV
Main subject:
Coronavirus Infections
/
COVID-19
Language:
English
Year:
2021
Document Type:
Preprint
Similar
MEDLINE
...
LILACS
LIS