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A Generic Quantitative Risk Assessment Framework for the Entry of Bat-Borne Zoonotic Viruses into the European Union.

A Generic Quantitative Risk Assessment Framework for the Entry of Bat-Borne Zoonotic Viruses into the European Union.
Author Information (click to view)

Simons RR, Horigan V, Gale P, Kosmider RD, Breed AC, Snary EL,


Simons RR, Horigan V, Gale P, Kosmider RD, Breed AC, Snary EL, (click to view)

Simons RR, Horigan V, Gale P, Kosmider RD, Breed AC, Snary EL,

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PloS one 2016 Oct 2711(10) e0165383 doi 10.1371/journal.pone.0165383
Abstract

Bat-borne viruses have been linked to a number of zoonotic diseases; in 2014 there have been human cases of Nipah virus (NiV) in Bangladesh and Ebola virus in West and Central Africa. Here we describe a model designed to provide initial quantitative predictions of the risk of entry of such viruses to European Union (EU) Member States (MSs) through four routes: human travel, legal trade (e.g. fruit and animal products), live animal movements and illegal importation of bushmeat. The model utilises available datasets to assess the movement via these routes between individual countries of the world and EU MSs. These data are combined with virus specific data to assess the relative risk of entry between EU MSs. As a case study, the model was parameterised for NiV. Scenario analyses showed that the selection of exporting countries with NiV and potentially contaminated trade products were essential to the accuracy of all model outputs. Uncertainty analyses of other model parameters identified that the model expected number of years to an introduction event within the EU was highly susceptible to the prevalence of NiV in bats. The relative rankings of the MSs and routes, however, were more robust. The UK, the Netherlands and Germany were consistently the most likely points of entry and the ranking of most MSs varied by no more than three places (maximum variation five places). Legal trade was consistently the most likely route of entry, only falling below human travel when the estimate of the prevalence of NiV in bats was particularly low. Any model-based calculation is dependent on the data available to feed into the model and there are distinct gaps in our knowledge, particularly in regard to various pathogen/virus as well as host/bat characteristics. However, the strengths of this model lie in the provision of relative comparisons of risk among routes and MSs. The potential for expansion of the model to include other routes and viruses and the possibility of rapid parameterisation demonstrates its potential for use in an outbreak situation.

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