A Stochastic Tick-Borne Disease Model: Exploring the Probability of Pathogen Persistence

Bull Math Biol. 2017 Sep;79(9):1999-2021. doi: 10.1007/s11538-017-0317-y. Epub 2017 Jul 13.

Abstract

We formulate and analyse a stochastic epidemic model for the transmission dynamics of a tick-borne disease in a single population using a continuous-time Markov chain approach. The stochastic model is based on an existing deterministic metapopulation tick-borne disease model. We compare the disease dynamics of the deterministic and stochastic models in order to determine the effect of randomness in tick-borne disease dynamics. The probability of disease extinction and that of a major outbreak are computed and approximated using the multitype Galton-Watson branching process and numerical simulations, respectively. Analytical and numerical results show some significant differences in model predictions between the stochastic and deterministic models. In particular, we find that a disease outbreak is more likely if the disease is introduced by infected deer as opposed to infected ticks. These insights demonstrate the importance of host movement in the expansion of tick-borne diseases into new geographic areas.

Keywords: Ehrlichiosis; Multitype branching process; Stochastic model; Tick-borne disease.

MeSH terms

  • Animals
  • Arachnid Vectors
  • Computer Simulation
  • Disease Outbreaks
  • Epidemics
  • Host-Pathogen Interactions
  • Humans
  • Markov Chains
  • Mathematical Concepts
  • Models, Biological*
  • Population Dynamics
  • Probability
  • Stochastic Processes
  • Tick-Borne Diseases / epidemiology
  • Tick-Borne Diseases / transmission*
  • Ticks