A parasite vector-host epidemic model for TSE propagation

Med Sci Monit. 2007 Mar;13(3):BR59-66.

Abstract

Background: Transmissible spongiform encephalopathies (TSEs) are a family of diseases that infect mammals. They are explained by cross-contamination through an unknown route or from infection of food contaminated with prion proteins (PrPs), natural proteins that take an infectious form contributing to the slow destruction of the animal brain. While the extreme resistance of PrPs to denaturation and proteolysis accounts for a route from the mouth to the brain, the possible role of another route of contamination is explored here. Many diseases are spread by vectors, as seen in plague, typhus, malaria, or dengue. The situation where PrPs would be transmitted by a vector and, from the characteristics of outbreaks, proposed hypotheses about the biological nature of such vectors are explored.

Material/methods: The nontrivial situation where contamination by the vector prevents infection by making the host immune to further vector contamination was analyzed. To investigate the nature of a possible vector, the spread of a disease in a closed population of hosts and vectors where the number of hosts is constant and the vectors multiply in the host was modeled mathematically. In this model, the disease is caused by an infective agent and is spread by a vector, while direct host-to-host spread is not permitted.

Results: Concrete values of the parameters of the model were computed from simulation of the BSE outbreak in the UK as a possible example of the process.

Conclusions: Microbial vector-borne diseases might play an unexpected role in the spread of epidemics, warranting further exploration.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cattle
  • Disease Outbreaks*
  • Disease Vectors*
  • Host-Parasite Interactions
  • Models, Biological*
  • Parasites / physiology*
  • Prion Diseases / epidemiology
  • Prion Diseases / transmission*
  • Prion Diseases / veterinary*
  • Time Factors
  • United Kingdom / epidemiology