Cargando…
Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019
African swine fever (ASF) has affected Romania since July 2017, with considerable economic and social consequences, despite the implementation of control measures mainly based on stamping out of infected pig populations. On the basis of the 2973 cumulative recorded cases up to September 2019 among w...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822963/ https://www.ncbi.nlm.nih.gov/pubmed/33483559 http://dx.doi.org/10.1038/s41598-021-81329-x |
_version_ | 1783639747097264128 |
---|---|
author | Andraud, Mathieu Bougeard, Stéphanie Chesnoiu, Theodora Rose, Nicolas |
author_facet | Andraud, Mathieu Bougeard, Stéphanie Chesnoiu, Theodora Rose, Nicolas |
author_sort | Andraud, Mathieu |
collection | PubMed |
description | African swine fever (ASF) has affected Romania since July 2017, with considerable economic and social consequences, despite the implementation of control measures mainly based on stamping out of infected pig populations. On the basis of the 2973 cumulative recorded cases up to September 2019 among wild boars and domestic pigs, analysis of the epidemiological characteristics could help to identify the factors favoring the persistence and spread of ASF. A statistical framework, based on a random forest methodology, was therefore developed to assess the spatiotemporal features of the epidemics and their relationships with environmental, human, and agricultural factors. The landscape of Romania was associated with the infection dynamics, particularly concerning forested and wetland areas. Waterways were also identified as a pivotal factor, raising questions about possible waterborne transmission since these waterways are often used as a water supply for backyard holdings. However, human activity was clearly identified as the main risk factor for the spread of ASF. Although the situation in Romania cannot be directly transposed to intensive pig farming countries, the findings of this study highlight the need for strict biosecurity measures on farms, and during transportation, to avoid ASF transmission at large geographic and temporal scales. |
format | Online Article Text |
id | pubmed-7822963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78229632021-01-27 Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019 Andraud, Mathieu Bougeard, Stéphanie Chesnoiu, Theodora Rose, Nicolas Sci Rep Article African swine fever (ASF) has affected Romania since July 2017, with considerable economic and social consequences, despite the implementation of control measures mainly based on stamping out of infected pig populations. On the basis of the 2973 cumulative recorded cases up to September 2019 among wild boars and domestic pigs, analysis of the epidemiological characteristics could help to identify the factors favoring the persistence and spread of ASF. A statistical framework, based on a random forest methodology, was therefore developed to assess the spatiotemporal features of the epidemics and their relationships with environmental, human, and agricultural factors. The landscape of Romania was associated with the infection dynamics, particularly concerning forested and wetland areas. Waterways were also identified as a pivotal factor, raising questions about possible waterborne transmission since these waterways are often used as a water supply for backyard holdings. However, human activity was clearly identified as the main risk factor for the spread of ASF. Although the situation in Romania cannot be directly transposed to intensive pig farming countries, the findings of this study highlight the need for strict biosecurity measures on farms, and during transportation, to avoid ASF transmission at large geographic and temporal scales. Nature Publishing Group UK 2021-01-22 /pmc/articles/PMC7822963/ /pubmed/33483559 http://dx.doi.org/10.1038/s41598-021-81329-x Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Andraud, Mathieu Bougeard, Stéphanie Chesnoiu, Theodora Rose, Nicolas Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019 |
title | Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019 |
title_full | Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019 |
title_fullStr | Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019 |
title_full_unstemmed | Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019 |
title_short | Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019 |
title_sort | spatiotemporal clustering and random forest models to identify risk factors of african swine fever outbreak in romania in 2018–2019 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822963/ https://www.ncbi.nlm.nih.gov/pubmed/33483559 http://dx.doi.org/10.1038/s41598-021-81329-x |
work_keys_str_mv | AT andraudmathieu spatiotemporalclusteringandrandomforestmodelstoidentifyriskfactorsofafricanswinefeveroutbreakinromaniain20182019 AT bougeardstephanie spatiotemporalclusteringandrandomforestmodelstoidentifyriskfactorsofafricanswinefeveroutbreakinromaniain20182019 AT chesnoiutheodora spatiotemporalclusteringandrandomforestmodelstoidentifyriskfactorsofafricanswinefeveroutbreakinromaniain20182019 AT rosenicolas spatiotemporalclusteringandrandomforestmodelstoidentifyriskfactorsofafricanswinefeveroutbreakinromaniain20182019 |