Searching for Superspreaders: Identifying Epidemic Patterns Associated with Superspreading Events in Stochastic Models

Christina J. Edholm, Blessing O. Emerenini, Anarina L. Murillo, Omar Saucedo, Nika Shakiba, Xueying Wang, Linda J.S. Allen, Angela Peace

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

The importance of host transmissibility in disease emergence has been demonstrated in historical and recent pandemics that involve infectious individuals, known as superspreaders, who are capable of transmitting the infection to a large number of susceptible individuals. To investigate the impact of superspreaders on epidemic dynamics, we formulate deterministic and stochastic models that incorporate differences in superspreaders versus nonsuperspreaders. In particular, continuous-time Markov chain models are used to investigate epidemic features associated with the presence of superspreaders in a population. We parameterize the models for two case studies, Middle East respiratory syndrome (MERS) and Ebola. Through mathematical analysis and numerical simulations, we find that the probability of outbreaks increases and time to outbreaks decreases as the prevalence of superspreaders increases in the population. In particular, as disease outbreaks occur more rapidly and more frequently when initiated by superspreaders, our results emphasize the need for expeditious public health interventions.

Original languageEnglish
Title of host publicationAssociation for Women in Mathematics Series
PublisherSpringer
Pages1-29
Number of pages29
DOIs
StatePublished - 2018

Publication series

NameAssociation for Women in Mathematics Series
Volume14
ISSN (Print)2364-5733
ISSN (Electronic)2364-5741

Keywords

  • Deterministic model
  • Ebola
  • Host heterogeneity
  • Middle East respiratory syndrome
  • Stochastic model
  • Superspreader

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