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Constrained expectation-maximisation for inference of social graphs explaining online user–user interactions. SOCIAL NETWORK ANALYSIS AND MINING 2023. [DOI: 10.1007/s13278-023-01037-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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2
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Administration of the antiviral agent T-1105 fully protects pigs from foot-and-mouth disease infection. Antiviral Res 2022; 208:105425. [DOI: 10.1016/j.antiviral.2022.105425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 11/23/2022]
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3
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Cardenas NC, Sykes AL, Lopes FPN, Machado G. Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions. Vet Res 2022; 53:14. [PMID: 35193675 PMCID: PMC8862288 DOI: 10.1186/s13567-022-01031-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 01/26/2022] [Indexed: 11/12/2022] Open
Abstract
Infectious diseases in livestock are well-known to infect multiple hosts and persist through a combination of within- and between-host transmission pathways. Uncertainty remains about the epidemic dynamics of diseases being introduced on farms with more than one susceptible host species. Here, we describe multi-host contact networks and elucidate the potential of disease spread through farms with multiple hosts. Four years of between-farm animal movement among all farms of a Brazilian state were described through a static and monthly snapshot of network representations. We developed a stochastic multilevel model to simulate scenarios in which infection was seeded into single host and multi-host farms to quantify disease spread potential, and simulate network-based control actions used to evaluate the reduction of secondarily infected farms. We showed that the swine network was more connected than cattle and small ruminants in both the static and monthly snapshots. The small ruminant network was highly fragmented, however, contributed to interconnecting farms, with other hosts acting as intermediaries throughout the networks. When a single host was initially infected, secondary infections were observed across farms with all other species. Our stochastic multi-host model demonstrated that targeting the top 3.25% of the farms ranked by degree reduced the number of secondarily infected farms. The results of the simulation highlight the importance of considering multi-host dynamics and contact networks while designing surveillance and preparedness control strategies against pathogens known to infect multiple species.
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Affiliation(s)
- Nicolas C Cardenas
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA
| | - Abagael L Sykes
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA
| | - Francisco P N Lopes
- Departamento de Defesa Agropecuária, Secretaria da Agricultura, Pecuária e Desenvolvimento Rural (SEAPDR), Porto Alegre, Brazil
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
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Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review. Pathogens 2022; 11:pathogens11020252. [PMID: 35215195 PMCID: PMC8875843 DOI: 10.3390/pathogens11020252] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 11/17/2022] Open
Abstract
In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.
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Greening SS, Zhang J, Midwinter AC, Wilkinson DA, Fayaz A, Williamson DA, Anderson MJ, Gates MC, French NP. Transmission dynamics of an antimicrobial resistant Campylobacter jejuni lineage in New Zealand's commercial poultry network. Epidemics 2021; 37:100521. [PMID: 34775297 DOI: 10.1016/j.epidem.2021.100521] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 08/05/2021] [Accepted: 11/07/2021] [Indexed: 11/26/2022] Open
Abstract
Understanding the relative contribution of different between-farm transmission pathways is essential in guiding recommendations for mitigating disease spread. This study investigated the association between contact pathways linking poultry farms in New Zealand and the genetic relatedness of antimicrobial resistant Campylobacter jejuni Sequence Type 6964 (ST-6964), with the aim of identifying the most likely contact pathways that contributed to its rapid spread across the industry. Whole-genome sequencing was performed on 167C. jejuni ST-6964 isolates sampled from across 30 New Zealand commercial poultry enterprises. The genetic relatedness between isolates was determined using whole genome multilocus sequence typing (wgMLST). Permutational multivariate analysis of variance and distance-based linear models were used to explore the strength of the relationship between pairwise genetic associations among the C. jejuni isolates and each of several pairwise distance matrices, indicating either the geographical distance between farms or the network distance of transportation vehicles. Overall, a significant association was found between the pairwise genetic relatedness of the C. jejuni isolates and the parent company, the road distance and the network distance of transporting feed vehicles. This result suggests that the transportation of feed within the commercial poultry industry as well as other local contacts between flocks, such as the movements of personnel, may have played a significant role in the spread of C. jejuni. However, further information on the historical contact patterns between farms is needed to fully characterise the risk of these pathways and to understand how they could be targeted to reduce the spread of C. jejuni.
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Affiliation(s)
- Sabrina S Greening
- Epicentre, School of Veterinary Science, Massey University, Palmerston North, New Zealand.
| | - Ji Zhang
- mEpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand; New Zealand Food Safety Science and Research Centre, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
| | - Anne C Midwinter
- mEpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - David A Wilkinson
- mEpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand; New Zealand Food Safety Science and Research Centre, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
| | - Ahmed Fayaz
- mEpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - Deborah A Williamson
- Microbiological Diagnostic Unit and Public Health Laboratory, University of Melbourne, Parkville, Victoria, Australia
| | - Marti J Anderson
- New Zealand Institute for Advanced Study, Massey University, Auckland, New Zealand
| | - M Carolyn Gates
- Epicentre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - Nigel P French
- mEpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand; New Zealand Food Safety Science and Research Centre, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
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6
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Bradhurst R, Garner G, Hóvári M, de la Puente M, Mintiens K, Yadav S, Federici T, Kopacka I, Stockreiter S, Kuzmanova I, Paunov S, Cacinovic V, Rubin M, Szilágyi J, Kókány ZS, Santi A, Sordilli M, Sighinas L, Spiridon M, Potocnik M, Sumption K. Development of a transboundary model of livestock disease in Europe. Transbound Emerg Dis 2021; 69:1963-1982. [PMID: 34169659 PMCID: PMC9545780 DOI: 10.1111/tbed.14201] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 06/01/2021] [Indexed: 12/03/2022]
Abstract
Epidemiological models of notifiable livestock disease are typically framed at a national level and targeted for specific diseases. There are inherent difficulties in extending models beyond national borders as details of the livestock population, production systems and marketing systems of neighbouring countries are not always readily available. It can also be a challenge to capture heterogeneities in production systems, control policies, and response resourcing across multiple countries, in a single transboundary model. In this paper, we describe EuFMDiS, a continental‐scale modelling framework for transboundary animal disease, specifically designed to support emergency animal disease planning in Europe. EuFMDiS simulates the spread of livestock disease within and between countries and allows control policies to be enacted and resourced on a per‐country basis. It provides a sophisticated decision support tool that can be used to look at the risk of disease introduction, establishment and spread; control approaches in terms of effectiveness and costs; resource management; and post‐outbreak management issues.
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Affiliation(s)
- Richard Bradhurst
- Centre of Excellence for Biosecurity Risk Analysis, School of BioSciences, University of Melbourne, Melbourne, Australia
| | - Graeme Garner
- European Commission for the Control of Foot-and-Mouth Disease, FAO, Rome, Italy
| | - Márk Hóvári
- European Commission for the Control of Foot-and-Mouth Disease, FAO, Rome, Italy
| | - Maria de la Puente
- European Commission for the Control of Foot-and-Mouth Disease, FAO, Rome, Italy
| | - Koen Mintiens
- European Commission for the Control of Foot-and-Mouth Disease, FAO, Rome, Italy
| | - Shankar Yadav
- European Commission for the Control of Foot-and-Mouth Disease, FAO, Rome, Italy
| | - Tiziano Federici
- European Commission for the Control of Foot-and-Mouth Disease, FAO, Rome, Italy
| | - Ian Kopacka
- Division for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Graz, Austria
| | - Simon Stockreiter
- Division for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Graz, Austria
| | | | | | - Vladimir Cacinovic
- Veterinary Inspection and Control of Food Safety Sector, State Inspectorate, Zagreb, Croatia
| | - Martina Rubin
- Veterinary and Food Safety Directorate, Ministry of Agriculture, Zagreb, Croatia
| | | | | | - Annalisa Santi
- Veterinary Epidemiology Unit, Istituto Zooprofilattico della Lombardia e dell'Emilia-Romagna
| | - Marco Sordilli
- Istituto Zooprofilattico Sperimentale del Lazio e della Toscana, Rome, Italy
| | - Laura Sighinas
- National Sanitary Veterinary and Food Safety Authority, Bucharest, Romania
| | - Mihaela Spiridon
- National Sanitary Veterinary and Food Safety Authority, Bucharest, Romania
| | - Marko Potocnik
- Animal Health and Animal Welfare Division Administration of the Republic of Slovenia for Food Safety, Veterinary Sector and Plant Protection, Ljubljana, Slovenia
| | - Keith Sumption
- European Commission for the Control of Foot-and-Mouth Disease, FAO, Rome, Italy
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Elucidating the Local Transmission Dynamics of Highly Pathogenic Avian Influenza H5N6 in the Republic of Korea by Integrating Phylogenetic Information. Pathogens 2021; 10:pathogens10060691. [PMID: 34199439 PMCID: PMC8230294 DOI: 10.3390/pathogens10060691] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 05/28/2021] [Accepted: 05/28/2021] [Indexed: 11/25/2022] Open
Abstract
Highly pathogenic avian influenza (HPAI) virus is one of the most virulent and infectious pathogens of poultry. As a response to HPAI epidemics, veterinary authorities implement preemptive depopulation as a controlling strategy. However, mass culling within a uniform radius of the infection site can result in unnecessary depopulation. Therefore, it is useful to quantify the transmission distance from infected premises (IPs) before determining the optimal area for preemptive depopulation. Accordingly, we analyzed the transmission risk within spatiotemporal clusters of IPs using transmission kernel estimates derived from phylogenetic clustering information on 311 HPAI H5N6 IPs identified during the 2016–2017 epidemic, Republic of Korea. Subsequently, we explored the impact of varying the culling radius on the local transmission of HPAI given the transmission risk estimates. The domestic duck farm density was positively associated with higher transmissibility. Ring culling over a radius of 3 km may be effective for areas with high dense duck holdings, but this approach does not appear to significantly reduce the risk for local transmission in areas with chicken farms. This study provides the first estimation of the local transmission dynamics of HPAI in the Republic of Korea as well as insight into determining an effective ring culling radius.
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Notsu K, Wiratsudakul A, Mitoma S, Daous HE, Kaneko C, El-Khaiat HM, Norimine J, Sekiguchi S. Quantitative Risk Assessment for the Introduction of Bovine Leukemia Virus-Infected Cattle Using a Cattle Movement Network Analysis. Pathogens 2020; 9:pathogens9110903. [PMID: 33126749 PMCID: PMC7693104 DOI: 10.3390/pathogens9110903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/27/2020] [Accepted: 10/27/2020] [Indexed: 11/18/2022] Open
Abstract
The cattle industry is suffering economic losses caused by bovine leukemia virus (BLV) and enzootic bovine leukosis (EBL), the clinical condition associated with BLV infection. This pathogen spreads easily without detection by farmers and veterinarians due to the lack of obvious clinical signs. Cattle movement strongly contributes to the inter-farm transmission of BLV. This study quantified the farm-level risk of BLV introduction using a cattle movement analysis. A generalized linear mixed model predicting the proportion of BLV-infected cattle was constructed based on weighted in-degree centrality. Our results suggest a positive association between weighted in-degree centrality and the estimated number of introduced BLV-infected cattle. Remarkably, the introduction of approximately six cattle allowed at least one BLV-infected animal to be added to the farm in the worst-case scenario. These data suggest a high risk of BLV infection on farms with a high number of cattle being introduced. Our findings indicate the need to strengthen BLV control strategies, especially along the chain of cattle movement.
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Affiliation(s)
- Kosuke Notsu
- Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, Miyazaki 889-1692, Japan; (K.N.); (S.M.); (H.E.D.)
| | - Anuwat Wiratsudakul
- Department of Clinical Sciences and Public Health, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom 73170, Thailand;
- The Monitoring and Surveillance Center for Zoonotic Diseases in Wildlife and Exotic Animals, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom 73170, Thailand
| | - Shuya Mitoma
- Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, Miyazaki 889-1692, Japan; (K.N.); (S.M.); (H.E.D.)
| | - Hala El Daous
- Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, Miyazaki 889-1692, Japan; (K.N.); (S.M.); (H.E.D.)
- Faculty of Veterinary Medicine, Benha University, Toukh 13736, Egypt;
| | - Chiho Kaneko
- Center for Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan; (C.K.); (J.N.)
| | - Heba M. El-Khaiat
- Faculty of Veterinary Medicine, Benha University, Toukh 13736, Egypt;
| | - Junzo Norimine
- Center for Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan; (C.K.); (J.N.)
- Department of Veterinary Science, Faculty of Agriculture, University of Miyazaki, Miyazaki 889-2192, Japan
| | - Satoshi Sekiguchi
- Center for Animal Disease Control, University of Miyazaki, Miyazaki 889-2192, Japan; (C.K.); (J.N.)
- Department of Veterinary Science, Faculty of Agriculture, University of Miyazaki, Miyazaki 889-2192, Japan
- Correspondence: ; Tel.: +81-0985-58-7676
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