Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019
Abstract 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 201...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-01-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-81329-x |
| _version_ | 1818851183190605824 |
|---|---|
| author | Mathieu Andraud Stéphanie Bougeard Theodora Chesnoiu Nicolas Rose |
| author_facet | Mathieu Andraud Stéphanie Bougeard Theodora Chesnoiu Nicolas Rose |
| author_sort | Mathieu Andraud |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-19T07:00:58Z |
| format | Article |
| id | doaj.art-22252cd2135740949bb0ccc955c1a5af |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-19T07:00:58Z |
| publishDate | 2021-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-22252cd2135740949bb0ccc955c1a5af2022-12-21T20:31:24ZengNature PortfolioScientific Reports2045-23222021-01-0111111210.1038/s41598-021-81329-xSpatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019Mathieu Andraud0Stéphanie Bougeard1Theodora Chesnoiu2Nicolas Rose3Epidemiology, Health and Welfare Research Unit, Ploufragan-Plouzané-Niort Laboratory, Anses, French Agency for Food, Environmental and Occupational Health & SafetyEpidemiology, Health and Welfare Research Unit, Ploufragan-Plouzané-Niort Laboratory, Anses, French Agency for Food, Environmental and Occupational Health & SafetyANSVSA (Autoritatea Națională Sanitară Veterinară și pentru Siguranța Alimentelor)Epidemiology, Health and Welfare Research Unit, Ploufragan-Plouzané-Niort Laboratory, Anses, French Agency for Food, Environmental and Occupational Health & SafetyAbstract 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.https://doi.org/10.1038/s41598-021-81329-x |
| spellingShingle | Mathieu Andraud Stéphanie Bougeard Theodora Chesnoiu Nicolas Rose Spatiotemporal clustering and Random Forest models to identify risk factors of African swine fever outbreak in Romania in 2018–2019 Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-021-81329-x |
| work_keys_str_mv | AT mathieuandraud spatiotemporalclusteringandrandomforestmodelstoidentifyriskfactorsofafricanswinefeveroutbreakinromaniain20182019 AT stephaniebougeard spatiotemporalclusteringandrandomforestmodelstoidentifyriskfactorsofafricanswinefeveroutbreakinromaniain20182019 AT theodorachesnoiu spatiotemporalclusteringandrandomforestmodelstoidentifyriskfactorsofafricanswinefeveroutbreakinromaniain20182019 AT nicolasrose spatiotemporalclusteringandrandomforestmodelstoidentifyriskfactorsofafricanswinefeveroutbreakinromaniain20182019 |