Modelling disease - A guide for the cattle practitioner

L. Green*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)


The two key approaches to modelling disease are statistical and mathematical. Statistical disease modelling attempts to explain disease and associated risks using a linear explanation. Typically statistical models use real samples from populations of cattle and ultimately a clinical trial would enable us to test for apparent risks and assess whether they are true risks for disease. Statistical modelling is more robustly used with non-infectious disease but can be used, carefully to understand aspects of infectious diseases. Mathematical models use non-linear approaches to explain mechanisms for infectious processes and assist with our understanding of the biology of the process. They may be used to identify areas of disease transmission or of the host or parasite that we do not understand (i.e. they target that more research is required). They may lead to new hypotheses for disease transmission. They may be used to test out scenarios for change in disease control policy and their effect on infectious disease. Results from mathematical and statistical models may be counter intuitive and these results need thought and openness to consider what may be occurring in the infectious process.

Original languageEnglish
Pages (from-to)243-248
Number of pages6
JournalCattle Practice
Issue number3
Publication statusPublished - Oct 2005


  • Bovine tuberculosis
  • Foot and mouth disease
  • Mathematical models
  • Statistical models

ASJC Scopus subject areas

  • Animal Science and Zoology


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