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Disease extinction and community size: modeling the persistence of measles

M J Keeling1, B T Grenfell

  • 1Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK.

Science (New York, N.Y.)
|January 3, 1997
PubMed

Insights

Ecological models for infectious disease extinction, like measles, were improved by incorporating realistic infection duration. This better predicts critical community size and explains disease incidence patterns.

Area of Science:

  • Ecology
  • Epidemiology
  • Mathematical Biology

Background:

  • Understanding the relationship between population size and extinction is fundamental in ecology.
  • The critical community size (CCS) is a key concept, representing the minimum population needed for a disease, such as measles, to persist.
  • Existing stochastic models often overestimate CCS for measles, failing to accurately predict infection persistence.

Purpose of the Study:

  • To address the overestimation of critical community size (CCS) for measles by current stochastic models.
  • To develop a more biologically realistic model for infection duration to improve CCS predictions.
  • To explain observed high-frequency oscillations in measles incidence.

Main Methods:

  • Utilized stochastic modeling approaches in population ecology and epidemiology.
  • Incorporated a refined model for the duration of infectiousness.
  • Compared model outputs with observed data on measles incidence and critical community size.

Main Results:

  • The revised model, accounting for realistic infection duration, provided a significantly closer fit to the observed critical community size for measles.
  • The new model successfully explained previously unexplained high-frequency oscillations in measles incidence.
  • Demonstrated that infection duration is a critical factor in accurately modeling disease persistence.

Conclusions:

  • A more biologically realistic representation of infection duration is crucial for accurate ecological modeling of infectious diseases.
  • The study refines our understanding of the factors determining disease persistence in populations.
  • Findings have implications for predicting and managing infectious disease outbreaks in human communities.

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