A curve of thresholds governs plague epizootics in Central Asia

Authors

  • Jonas Reijniers,

    Corresponding author
    1. Department of Biology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
      E-mail: herwig.leirs@ua.ac.be
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  • Stephen Davis,

    1. Mathematical & Geospatial Sciences, RMIT University, 124 La Trobe Street, Melbourne, Victoria 3000, Australia
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  • Mike Begon,

    1. Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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  • Johan A. P. Heesterbeek,

    1. Theoretical Epidemiology, Faculty of Veterinary Medicine, University of Utrecht, Yalelaan 7, 3584 CL Utrecht, The Netherlands
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  • Vladimir S. Ageyev,

    1. M. Aikimbayev’s Kazakh Scientific Centre for Quarantine and Zoonotic Diseases, 14 Kapalskaya Street, Almaty 050054, Republic of Kazakhstan
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  • Herwig Leirs

    1. Department of Biology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
    2. Danish Pest Infestation Laboratory, University of Aarhus, Faculty of Agricultural Sciences, Department of Agro-ecology, Forsøgsvej 1, DK-4200 Slagelse, Denmark
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E-mail: herwig.leirs@ua.ac.be

Abstract

Ecology Letters (2012)

Abstract

A core concept of infectious disease epidemiology is the abundance threshold, below which an infection is unable to invade or persist. There have been contrasting theoretical predictions regarding the nature of this threshold for vector-borne diseases, but for infections with an invertebrate vector, it is common to assume a threshold defined by the ratio of vector and host abundances. Here, we show in contrast, both from field data and model simulations, that for plague (Yersinia pestis) in Kazakhstan, the invasion threshold quantity is based on the product of its host (Rhombomys opimus) and vector (mainly Xenopsylla spp.) abundances, resulting in a combined threshold curve with hyperbolic shape. This shape implies compensation between host and vector abundances in permitting infection, which has important implications for disease control. Realistic joint thresholds, supported by data, should promote improved understanding, prediction and management of disease occurrence in this and other vector-borne disease systems.

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