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Related Experiment Videos

Interval estimates for epidemic thresholds in two-sex network models.

Mark S Handcock1, James Holland Jones

  • 1Center for Statistics and the Social Sciences, University of Washington, Seattle, WA 98195-4320, USA. handcock@stat.washington.edu

Theoretical Population Biology
|May 23, 2006
PubMed
Summary

In highly connected populations, small epidemic thresholds mean diseases spread easily. Conventional control methods may fail, highlighting the need for targeted interventions in sexually transmitted disease (STD) network models.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Modeling

Background:

  • Heterogeneous populations with skewed contact distributions can have very small epidemic thresholds.
  • When above this threshold, epidemics are inevitable, and standard control measures may be ineffective.
  • Sexually transmitted diseases (STDs) pose unique challenges due to social network structures.

Purpose of the Study:

  • To derive expressions for the basic reproductive number (R(0)) in a two-sex network model for STDs.
  • To estimate epidemic thresholds in human populations based on sexual behavior data.
  • To assess the implications of population structure on STD epidemic potential.

Main Methods:

  • Developed a two-sex network model with random mixing conditional on degree distribution.

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  • Derived R(0) formulas incorporating degree distribution characteristics and transmissibility.
  • Calculated epidemic threshold estimates using stochastic process models and survey data from Uganda, Sweden, and the USA.

    • Main Results:

    • Derived expressions for R(0) in one- and heterogeneous two-population models.
    • Epidemic thresholds were estimated for Uganda, Sweden, and the USA.
    • Uganda and Sweden showed statistically significant positive epidemic thresholds; the USA's interval included zero.

    Conclusions:

    • Network structure significantly influences STD epidemic potential.
    • Small epidemic thresholds in highly connected populations challenge conventional control strategies.
    • Findings underscore the importance of considering population heterogeneity and network structure in STD modeling and control.