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Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model
BACKGROUND: African swine fever (ASF) is a serious contagious viral disease of pigs that affects the pig industry. This study aimed to evaluate the possible African swine fever (ASF) distribution using network analysis and a diffusion model through live pig, carcass, and pig product movement data. M...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178211/ https://www.ncbi.nlm.nih.gov/pubmed/37187529 http://dx.doi.org/10.7717/peerj.15359 |
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author | Poolkhet, Chaithep Kasemsuwan, Suwicha Thongratsakul, Sukanya Warrasuth, Nattachai Pamaranon, Nuttavadee Nuanualsuwan, Suphachai |
author_facet | Poolkhet, Chaithep Kasemsuwan, Suwicha Thongratsakul, Sukanya Warrasuth, Nattachai Pamaranon, Nuttavadee Nuanualsuwan, Suphachai |
author_sort | Poolkhet, Chaithep |
collection | PubMed |
description | BACKGROUND: African swine fever (ASF) is a serious contagious viral disease of pigs that affects the pig industry. This study aimed to evaluate the possible African swine fever (ASF) distribution using network analysis and a diffusion model through live pig, carcass, and pig product movement data. MATERIAL AND METHODS: Empirical movement data from Thailand for the year 2019 were used, and expert opinions were sought to evaluate network properties and the diffusion model. The networks were presented as live pig movement and carcass movement data at the provincial and district levels. For network analysis, a descriptive network analysis was performed using outdegree, indegree, betweenness, fragmentation, and power law distribution, and cutpoints were used to describe movement patterns. For the diffusion model, we simulated each network using spatially different infected locations, patterns, and initial infection sites. Based on expert opinions, the initial infection site, the probability of ASF occurrence, and the probability of the initial infected adopter were selected for the appropriated network. In this study, we also simulated networks under varying network parameters to predict the infection speed. RESULTS AND CONCLUSIONS: The total number of movements recorded was 2,594,364. These were divided into 403,408 (403,408/2,594,364; 15.55%) for live pigs and 2,190,956 (2,190,956/2,594,364; 84.45%) for carcasses. We found that carcass movement at the provincial level showed the highest outdegree (mean = 342.554, standard deviation (SD) = 900.528) and indegree values (mean = 342.554, SD = 665.509). In addition, the outdegree and indegree presented similar mean values and the degree distributions of both district networks followed a power-law function. The network of live pigs at provincial level showed the highest value for betweenness (mean = 0.011, SD = 0.017), and the network of live pigs at provincial level showed the highest value for fragmentation (mean = 0.027, SD = 0.005). Our simulation data indicated that the disease occurred randomly due to live pig and carcass movements along the central and western regions of Thailand, causing the rapid spread of ASF. Without control measures, it could spread to all provinces within 5- and 3-time units and in all districts within 21- and 30-time units for the network of live pigs and carcasses, respectively. This study assists the authorities to plan control and preventive measures and limit economic losses caused by ASF. |
format | Online Article Text |
id | pubmed-10178211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101782112023-05-13 Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model Poolkhet, Chaithep Kasemsuwan, Suwicha Thongratsakul, Sukanya Warrasuth, Nattachai Pamaranon, Nuttavadee Nuanualsuwan, Suphachai PeerJ Agricultural Science BACKGROUND: African swine fever (ASF) is a serious contagious viral disease of pigs that affects the pig industry. This study aimed to evaluate the possible African swine fever (ASF) distribution using network analysis and a diffusion model through live pig, carcass, and pig product movement data. MATERIAL AND METHODS: Empirical movement data from Thailand for the year 2019 were used, and expert opinions were sought to evaluate network properties and the diffusion model. The networks were presented as live pig movement and carcass movement data at the provincial and district levels. For network analysis, a descriptive network analysis was performed using outdegree, indegree, betweenness, fragmentation, and power law distribution, and cutpoints were used to describe movement patterns. For the diffusion model, we simulated each network using spatially different infected locations, patterns, and initial infection sites. Based on expert opinions, the initial infection site, the probability of ASF occurrence, and the probability of the initial infected adopter were selected for the appropriated network. In this study, we also simulated networks under varying network parameters to predict the infection speed. RESULTS AND CONCLUSIONS: The total number of movements recorded was 2,594,364. These were divided into 403,408 (403,408/2,594,364; 15.55%) for live pigs and 2,190,956 (2,190,956/2,594,364; 84.45%) for carcasses. We found that carcass movement at the provincial level showed the highest outdegree (mean = 342.554, standard deviation (SD) = 900.528) and indegree values (mean = 342.554, SD = 665.509). In addition, the outdegree and indegree presented similar mean values and the degree distributions of both district networks followed a power-law function. The network of live pigs at provincial level showed the highest value for betweenness (mean = 0.011, SD = 0.017), and the network of live pigs at provincial level showed the highest value for fragmentation (mean = 0.027, SD = 0.005). Our simulation data indicated that the disease occurred randomly due to live pig and carcass movements along the central and western regions of Thailand, causing the rapid spread of ASF. Without control measures, it could spread to all provinces within 5- and 3-time units and in all districts within 21- and 30-time units for the network of live pigs and carcasses, respectively. This study assists the authorities to plan control and preventive measures and limit economic losses caused by ASF. PeerJ Inc. 2023-05-09 /pmc/articles/PMC10178211/ /pubmed/37187529 http://dx.doi.org/10.7717/peerj.15359 Text en © 2023 Poolkhet et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Agricultural Science Poolkhet, Chaithep Kasemsuwan, Suwicha Thongratsakul, Sukanya Warrasuth, Nattachai Pamaranon, Nuttavadee Nuanualsuwan, Suphachai Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model |
title | Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model |
title_full | Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model |
title_fullStr | Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model |
title_full_unstemmed | Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model |
title_short | Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model |
title_sort | prediction of the spread of african swine fever through pig and carcass movements in thailand using a network analysis and diffusion model |
topic | Agricultural Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178211/ https://www.ncbi.nlm.nih.gov/pubmed/37187529 http://dx.doi.org/10.7717/peerj.15359 |
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