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A clarification of transmission terms in host-microparasite models: numbers, densities and areas

M Begon1, M Bennett, R G Bowers

  • 1Centre for Comparative Infectious Diseases, School of Biological Sciences, The University of Liverpool, UK.

Epidemiology and Infection
|September 5, 2002
PubMed

Insights

Understanding infectious disease transmission requires accurate transmission terms. This study clarifies these terms, recommending the use of density- and frequency-dependent transmission while advising against

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Disease Dynamics

Background:

  • Infectious disease dynamics are driven by transmission.
  • The 'transmission term' quantifies susceptible host conversion to infected hosts.
  • Current transmission term conventions are under scrutiny, necessitating clarification.

Purpose of the Study:

  • To review the derivation of transmission terms in infectious disease dynamics.
  • To explain the basis of confusion surrounding transmission term terminology.
  • To provide clarification and propose revised conventions for transmission terms.

Main Methods:

  • Review of existing literature on transmission term derivations.
  • Analysis of the conceptual basis for different transmission term formulations.
  • Conceptual clarification of host population area, host numbers, and host density in transmission.

Main Results:

  • Failure to account for host population area alongside host numbers and density underlies current confusion.
  • Density-dependent and frequency-dependent transmission terms are deemed valid and useful.
  • The terms 'mass action', 'true mass action', and 'pseudo mass action' are identified as unhelpful and should be discarded.

Conclusions:

  • Explicitly including host population area is crucial for accurate transmission term formulation.
  • Density-dependent and frequency-dependent transmission modes are distinct from population mixing patterns (homogeneous vs. heterogeneous).
  • Revised terminology for transmission terms will improve clarity and accuracy in infectious disease modeling.

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