Tooling-up for infectious disease transmission modelling

Baguelin, M., Medley, G. F., Nightingale, E. S., O'Reilly, K. M., Rees, E. M. , Waterlow, N. R. and Wagner, M. (2020) Tooling-up for infectious disease transmission modelling. Epidemics, 32, 100395. (doi: 10.1016/j.epidem.2020.100395) (PMID:32405321) (PMCID:PMC7219405)

Abstract

In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heterogeneity, advanced computing and inference, and complexity and application to the real-world. Infectious disease dynamics and heterogeneity dominated until the 1980s where the use of analytical models illustrated fundamental concepts such as herd immunity. The second revolution embraced the integration of data with models and the increased use of computing. From the turn of the century an emergence of novel datasets enabled improved modelling of real-world complexity. The emergence of more complex data that reflect the real-world heterogeneities in transmission resulted in the development of improved inference methods such as particle filtering. Each of these three revolutions have always kept the understanding of infectious disease spread as its motivation but have been developed through the use of new techniques, tools and the availability of data. We conclude by providing a commentary on what the next revoluition in infectious disease modelling may be.

• Item Type: Articles
• Additional Information: The authors thank the UK National Institute for Health Research Health Protection Research Unit (NIHRHPRU) in Modelling Methodology at Imperial College London in partnership with Public Health England (PHE) for funding (grant HPRU-2012–10080). MW was supported by a PhD scholarship from the Biotechnology and Biological Sciences Research Council (grant number BB/M009513/1), NW and ER were supported by PhD scholarships from the Medical Research Council (MR/N013638/1), EM was supported by a grant from the Bill and Melinda Gates Foundation (OPP1183986), and KO was supported by a grant from the Bill and Melinda Gates Foundation (OPP1191821).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Rees, Dr Eleanor
• Authors: Baguelin, M., Medley, G. F., Nightingale, E. S., O'Reilly, K. M., Rees, E. M., Waterlow, N. R., and Wagner, M.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
• Journal Name: Epidemics
• Publisher: Elsevier
• ISSN: 1755-4365
• ISSN (Online): 1878-0067
• Published Online: 13 May 2020
• Copyright Holders: Copyright © 2020 Published by Elsevier B.V.
• First Published: First published in Epidemics 32:100395
• Publisher Policy: Reproduced under a Creative Commons license