ArticlePloS one2016
A Generic Quantitative Risk Assessment Framework for the Entry of Bat-Borne Zoonotic Viruses into the European Union.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- A user-friendly decision support tool to assist one-health risk assessors.One health (Amsterdam, Netherlands) · 2021Article
- Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases.BMJ global health · 2021Article
- Assessing training needs in infectious disease management at major ports, airports and ground-crossings in Europe.BMC public health · 2021Article
- Bananas, coffee and palm oil: The trade of agricultural commodities in the framework of the EU-Colombia free trade agreement.PloS one · 2021Article
- Assessing the aggregated probability of entry of a novel prion disease agent into the United Kingdom.Microbial risk analysis · 2020Article
- Toward a quantification of risks at the nexus of conservation and health: The case of bushmeat markets in Lao PDR.The Science of the total environment · 2019Article
- Using species distribution models to predict potential hot-spots for Rift Valley Fever establishment in the United Kingdom.PloS one · 2019Article
- Application of a quantitative entry assessment model to compare the relative risk of incursion of zoonotic bat-borne viruses into European Union Member States.Microbial risk analysis · 2017Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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