Bulletin of Mathematical Biology

, Volume 67, Issue 4, pp 855–873 | Cite as

Novel moment closure approximations in stochastic epidemics

  • Isthrinayagy Krishnarajah
  • Alex Cook
  • Glenn Marion
  • Gavin Gibson


Moment closure approximations are used to provide analytic approximations to non-linear stochastic population models. They often provide insights into model behaviour and help validate simulation results. However, existing closure schemes typically fail in situations where the population distribution is highly skewed or extinctions occur. In this study we address these problems by introducing novel second-and third-order moment closure approximations which we apply to the stochastic SI and SIS epidemic models. In the case of the SI model, which has a highly skewed distribution of infection, we develop a second-order approximation based on the beta-binomial distribution. In addition, a closure approximation based on mixture distribution is developed in order to capture the behaviour of the stochastic SIS model around the threshold between persistence and extinction. This mixture approximation comprises a probability distribution designed to capture the quasi-equilibrium probabilities of the system and a probability mass at 0 which represents the probability of extinction. Two third-order versions of this mixture approximation are considered in which the log-normal and the beta-binomial are used to model the quasi-equilibrium distribution. Comparison with simulation results shows: (1) the beta-binomial approximation is flexible in shape and matches the skewness predicted by simulation as shown by the stochastic SI model and (2) mixture approximations are able to predict transient and extinction behaviour as shown by the stochastic SIS model, in marked contrast with existing approaches. We also apply our mixture approximation to approximate a likehood function and carry out point and interval parameter estimation.


Moment Closure Saddlepoint Approximation Subcritical Region Mixture Approximation Moment Closure Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Society for Mathematical Biology 2005

Authors and Affiliations

  • Isthrinayagy Krishnarajah
    • 1
    • 2
  • Alex Cook
    • 1
    • 2
  • Glenn Marion
    • 2
  • Gavin Gibson
    • 1
  1. 1.Department of Actuarial Mathematics and StatisticsHeriot-Watt UniversityEdinburghUK
  2. 2.Biomathematics and Statistics Scotland, JCMBEdinburghUK

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