Literature DB >> 23419785

Stochastic spatio-temporal modelling of African swine fever spread in the European Union during the high risk period.

Annette Nigsch1, Solenne Costard, Bryony A Jones, Dirk U Pfeiffer, Barbara Wieland.   

Abstract

African swine fever (ASF) is a notifiable viral pig disease with high mortality and serious socio-economic consequences. Since ASF emerged in Georgia in 2007 the disease has spread to several neighbouring countries and cases have been detected in areas bordering the European Union (EU). It is uncertain how fast the virus would be able to spread within the unrestricted European trading area if it were introduced into the EU. This project therefore aimed to develop a model for the spread of ASF within and between the 27 Member States (MS) of the EU during the high risk period (HRP) and to identify MS during that period would most likely contribute to ASF spread ("super-spreaders") or MS that would most likely receive cases from other MS ("super-receivers"). A stochastic spatio-temporal state-transition model using simulated individual farm records was developed to assess silent ASF virus spread during different predefined HRPs of 10-60 days duration. Infection was seeded into farms of different pig production types in each of the 27 MS. Direct pig-to-pig transmission and indirect transmission routes (pig transport lorries and professional contacts) were considered the main pathways during the early stages of an epidemic. The model was parameterised using data collated from EUROSTAT, TRACES, a questionnaire sent to MS, and the scientific literature. Model outputs showed that virus circulation was generally limited to 1-2 infected premises per outbreak (95% IQR: 1-4; maximum: 10) with large breeder farms as index case resulting in most infected premises. Seven MS caused between-MS spread due to intra-Community trade during the first 10 days after seeding infection. For a HRP of 60 days from virus introduction, movements of infected pigs will originate at least once from 16 MS, with 6 MS spreading ASF in more than 10% of iterations. Two thirds of all intra-Community spread was linked to six trade links only. Denmark, the Netherlands, Lithuania and Latvia were identified as "super-spreaders"; Germany and Poland as "super-receivers". In the sensitivity analysis, the total number of premises per country involved in intra-Community trade was found to be a key determinant for the between-MS spread dynamic and needs to be further investigated. It was concluded that spread during the HRP is likely to be limited, especially if the HRP is short. This emphasises the importance of having good disease awareness in all MS for early disease detection.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Mesh:

Year:  2013        PMID: 23419785     DOI: 10.1016/j.prevetmed.2012.11.003

Source DB:  PubMed          Journal:  Prev Vet Med        ISSN: 0167-5877            Impact factor:   2.670


  15 in total

1.  Emerging infectious diseases may spread across pig trade networks in Thailand once introduced: a network analysis approach.

Authors:  Anuwat Wiratsudakul; Phrutsamon Wongnak; Weerapong Thanapongtharm
Journal:  Trop Anim Health Prod       Date:  2022-06-10       Impact factor: 1.893

2.  Simulation of Spread of African Swine Fever, Including the Effects of Residues from Dead Animals.

Authors:  Tariq Halasa; Anette Boklund; Anette Bøtner; Nils Toft; Hans-Hermann Thulke
Journal:  Front Vet Sci       Date:  2016-02-02

3.  Spatial and Functional Organization of Pig Trade in Different European Production Systems: Implications for Disease Prevention and Control.

Authors:  Anne Relun; Vladimir Grosbois; José Manuel Sánchez-Vizcaíno; Tsviatko Alexandrov; Francesco Feliziani; Agnès Waret-Szkuta; Sophie Molia; Eric Marcel Charles Etter; Beatriz Martínez-López
Journal:  Front Vet Sci       Date:  2016-02-04

4.  Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn't Fit All.

Authors:  Peter Brommesson; Uno Wennergren; Tom Lindström
Journal:  PLoS One       Date:  2016-10-19       Impact factor: 3.240

5.  A user-friendly decision support tool to assist one-health risk assessors.

Authors:  Rob Dewar; Christine Gavin; Catherine McCarthy; Rachel A Taylor; Charlotte Cook; Robin R L Simons
Journal:  One Health       Date:  2021-05-14

6.  Introduction of African swine fever into the European Union through illegal importation of pork and pork products.

Authors:  Solenne Costard; Bryony Anne Jones; Beatriz Martínez-López; Lina Mur; Ana de la Torre; Marta Martínez; Fernando Sánchez-Vizcaíno; Jose-Manuel Sánchez-Vizcaíno; Dirk Udo Pfeiffer; Barbara Wieland
Journal:  PLoS One       Date:  2013-04-15       Impact factor: 3.240

7.  Estimation of the transmission dynamics of African swine fever virus within a swine house.

Authors:  J P Nielsen; T S Larsen; T Halasa; L E Christiansen
Journal:  Epidemiol Infect       Date:  2017-08-03       Impact factor: 4.434

8.  CD2v Interacts with Adaptor Protein AP-1 during African Swine Fever Infection.

Authors:  Daniel Pérez-Núñez; Eduardo García-Urdiales; Marta Martínez-Bonet; María L Nogal; Susana Barroso; Yolanda Revilla; Ricardo Madrid
Journal:  PLoS One       Date:  2015-04-27       Impact factor: 3.240

9.  How commercial and non-commercial swine producers move pigs in Scotland: a detailed descriptive analysis.

Authors:  Thibaud Porphyre; Lisa A Boden; Carla Correia-Gomes; Harriet K Auty; George J Gunn; Mark E J Woolhouse
Journal:  BMC Vet Res       Date:  2014-06-25       Impact factor: 2.741

10.  Modular framework to assess the risk of African swine fever virus entry into the European Union.

Authors:  Lina Mur; Beatriz Martínez-López; Solenne Costard; Ana de la Torre; Bryony A Jones; Marta Martínez; Fernando Sánchez-Vizcaíno; María Jesús Muñoz; Dirk U Pfeiffer; José Manuel Sánchez-Vizcaíno; Barbara Wieland
Journal:  BMC Vet Res       Date:  2014-07-03       Impact factor: 2.741

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.