One model to rule them all? Modelling approaches across OneHealth for human, animal and plant epidemics

Adam Kleczkowski, Andy Hoyle, Paul McMenemy

Research output: Contribution to journalReview article

2 Citations (Scopus)


One hundred years after the 1918 influenza outbreak, are we ready for the next pandemic? This paper addresses the need to identify and develop collaborative, interdisciplinary and cross-sectoral approaches to modelling of infectious diseases including the fields of not only human and veterinary medicine, but also plant epidemiology. Firstly, the paper explains the concepts on which the most common epidemiological modelling approaches are based, namely the division of a host population into susceptible, infected and removed (SIR) classes and the proportionality of the infection rate to the size of the susceptible and infected populations. It then demonstrates how these simple concepts have been developed into a vast and successful modelling framework that has been used in predicting and controlling disease outbreaks for over 100 years. Secondly, it considers the compartmental models based on the SIR paradigm within the broader concept of a 'disease tetrahedron' (comprising host, pathogen, environment and man) and uses it to review the similarities and differences among the fields comprising the 'OneHealth' approach. Finally, the paper advocates interactions between all fields and explores the future challenges facing modellers. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Original languageEnglish
Article number20180255
Number of pages8
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Issue number1775
Publication statusPublished - 6 May 2019



  • bio-economic models
  • compartmental models
  • epidemiological data
  • infectious disease
  • OneHealth
  • plant pathogens

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