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Pinotti F, Lourenço J, Gupta S, Das Gupta S, Henning J, Blake D, Tomley F, Barnett T, Pfeiffer D, Hoque MA, Fournié G. EPINEST, an agent-based model to simulate epidemic dynamics in large-scale poultry production and distribution networks. PLoS Comput Biol 2024; 20:e1011375. [PMID: 38381804 PMCID: PMC10911595 DOI: 10.1371/journal.pcbi.1011375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 03/04/2024] [Accepted: 02/06/2024] [Indexed: 02/23/2024] Open
Abstract
The rapid intensification of poultry production raises important concerns about the associated risks of zoonotic infections. Here, we introduce EPINEST (EPIdemic NEtwork Simulation in poultry Transportation systems): an agent-based modelling framework designed to simulate pathogen transmission within realistic poultry production and distribution networks. We provide example applications to broiler production in Bangladesh, but the modular structure of the model allows for easy parameterization to suit specific countries and system configurations. Moreover, the framework enables the replication of a wide range of eco-epidemiological scenarios by incorporating diverse pathogen life-history traits, modes of transmission and interactions between multiple strains and/or pathogens. EPINEST was developed in the context of an interdisciplinary multi-centre study conducted in Bangladesh, India, Vietnam and Sri Lanka, and will facilitate the investigation of the spreading patterns of various health hazards such as avian influenza, Campylobacter, Salmonella and antimicrobial resistance in these countries. Furthermore, this modelling framework holds potential for broader application in veterinary epidemiology and One Health research, extending its relevance beyond poultry to encompass other livestock species and disease systems.
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Affiliation(s)
| | - José Lourenço
- Católica Biomedical Research, Católica Medical School, Universidade Católica Portuguesa, Lisbon, Portugal
| | | | - Suman Das Gupta
- School of Veterinary Science, The University of Queensland, Queensland, Australia
- Gulbali Institute, Charles Sturt University, Wagga Wagga, NSW, Australia
| | - Joerg Henning
- School of Veterinary Science, The University of Queensland, Queensland, Australia
| | - Damer Blake
- Royal Veterinary College, London, United Kingdom
| | - Fiona Tomley
- Royal Veterinary College, London, United Kingdom
| | - Tony Barnett
- Royal Veterinary College, London, United Kingdom
- The Firoz Lalji Centre for Africa, London School of Economics and Political Science, London, United Kingdom
| | - Dirk Pfeiffer
- Royal Veterinary College, London, United Kingdom
- City University of Hong Kong, Hong Kong SAR, Hong Kong
| | - Md. Ahasanul Hoque
- Chattogram Veterinary and Animal Sciences University, Chittagong, Bangladesh
| | - Guillaume Fournié
- Royal Veterinary College, London, United Kingdom
- INRAE, VetAgro Sup, UMR EPIA, Université de Lyon, Marcy l’Etoile, 69280, France
- INRAE, VetAgro Sup, UMR EPIA, Université Clermont Auvergne, Saint Genès Champanelle, 63122, France
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2
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Huang YH, Owen-Smith N, Henley MD, Kilian JW, Kamath PL, Ochai SO, van Heerden H, Mfune JKE, Getz WM, Turner WC. Variation in herbivore space use: comparing two savanna ecosystems with different anthrax outbreak patterns in southern Africa. MOVEMENT ECOLOGY 2023; 11:46. [PMID: 37525286 PMCID: PMC10392021 DOI: 10.1186/s40462-023-00385-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 04/16/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND The distribution of resources can affect animal range sizes, which in turn may alter infectious disease dynamics in heterogenous environments. The risk of pathogen exposure or the spatial extent of outbreaks may vary with host range size. This study examined the range sizes of herbivorous anthrax host species in two ecosystems and relationships between spatial movement behavior and patterns of disease outbreaks for a multi-host environmentally transmitted pathogen. METHODS We examined range sizes for seven host species and the spatial extent of anthrax outbreaks in Etosha National Park, Namibia and Kruger National Park, South Africa, where the main host species and outbreak sizes differ. We evaluated host range sizes using the local convex hull method at different temporal scales, within-individual temporal range overlap, and relationships between ranging behavior and species contributions to anthrax cases in each park. We estimated the spatial extent of annual anthrax mortalities and evaluated whether the extent was correlated with case numbers of a given host species. RESULTS Range size differences among species were not linearly related to anthrax case numbers. In Kruger the main host species had small range sizes and high range overlap, which may heighten exposure when outbreaks occur within their ranges. However, different patterns were observed in Etosha, where the main host species had large range sizes and relatively little overlap. The spatial extent of anthrax mortalities was similar between parks but less variable in Etosha than Kruger. In Kruger outbreaks varied from small local clusters to large areas and the spatial extent correlated with case numbers and species affected. Secondary host species contributed relatively few cases to outbreaks; however, for these species with large range sizes, case numbers positively correlated with outbreak extent. CONCLUSIONS Our results provide new information on the spatiotemporal structuring of ranging movements of anthrax host species in two ecosystems. The results linking anthrax dynamics to host space use are correlative, yet suggest that, though partial and proximate, host range size and overlap may be contributing factors in outbreak characteristics for environmentally transmitted pathogens.
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Affiliation(s)
- Yen-Hua Huang
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| | - Norman Owen-Smith
- Centre for African Ecology, School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Wits, 2050, South Africa
| | - Michelle D Henley
- Applied Behavioural Ecology and Ecosystem Research Unit, School of Environmental Sciences, University of South Africa, Florida, Johannesburg, 1710, South Africa
- Elephants Alive, Ekuthuleni Shareblock Ltd, Hoedspruit, 1380, South Africa
- Department of Philosophy, Faculty of Humanities, University of Johannesburg, Auckland Park, 2006, South Africa
| | - J Werner Kilian
- Etosha Ecological Institute (retired), Etosha National Park, Ministry of Environment, Forestry and Tourism, Okaukuejo, Namibia
| | - Pauline L Kamath
- School of Food and Agriculture, University of Maine, Orono, ME, 04469, USA
| | - Sunday O Ochai
- Department of Veterinary Tropical Diseases, University of Pretoria, Onderstepoort, South Africa
| | - Henriette van Heerden
- Department of Veterinary Tropical Diseases, University of Pretoria, Onderstepoort, South Africa
| | - John K E Mfune
- Department of Environmental Science, University of Namibia, Windhoek, Namibia
| | - Wayne M Getz
- Department of Environmental Science, Policy & Management, University of California, Berkeley, CA, 94704, USA
- School of Mathematical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Wendy C Turner
- Wisconsin Cooperative Wildlife Research Unit, U.S. Geological Survey, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA
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3
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Sanchez F, Galvis JA, Cardenas NC, Corzo C, Jones C, Machado G. Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States. Front Vet Sci 2023; 10:1158306. [PMID: 37456959 PMCID: PMC10340085 DOI: 10.3389/fvets.2023.1158306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms.
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Affiliation(s)
- Felipe Sanchez
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, United States
- Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, United States
| | - Jason A. Galvis
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, United States
| | - Nicolas C. Cardenas
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, United States
| | - Cesar Corzo
- Veterinary Population Medicine Department, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, United States
| | - Christopher Jones
- Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, United States
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, United States
- Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, United States
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4
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Ezanno P, Picault S, Bareille S, Beaunée G, Boender GJ, Dankwa EA, Deslandes F, Donnelly CA, Hagenaars TJ, Hayes S, Jori F, Lambert S, Mancini M, Munoz F, Pleydell DRJ, Thompson RN, Vergu E, Vignes M, Vergne T. The African swine fever modelling challenge: Model comparison and lessons learnt. Epidemics 2022; 40:100615. [PMID: 35970067 DOI: 10.1016/j.epidem.2022.100615] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/29/2022] [Accepted: 07/20/2022] [Indexed: 11/26/2022] Open
Abstract
Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health - the ASF Challenge - which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.
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Affiliation(s)
| | | | - Servane Bareille
- INRAE, Oniris, BIOEPAR, 44300 Nantes, France; INRAE, ENVT, IHAP, Toulouse, France
| | | | | | | | | | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom; Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, United Kingdom
| | | | - Sarah Hayes
- Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, United Kingdom
| | - Ferran Jori
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - Sébastien Lambert
- Centre for Emerging, Endemic and Exotic Diseases, Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, United Kingdom
| | - Matthieu Mancini
- INRAE, Oniris, BIOEPAR, 44300 Nantes, France; INRAE, ENVT, IHAP, Toulouse, France
| | - Facundo Munoz
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - David R J Pleydell
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - Robin N Thompson
- Mathematics Institute and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Elisabeta Vergu
- Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | - Matthieu Vignes
- School of Mathematical and Computational Sciences, Massey University, Palmerston North, New Zealand
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Kwan TH, Wong NS, Yeoh EK, Lee SS. Mining relationships between transmission clusters from contact tracing data: An application for investigating COVID-19 outbreak. J Am Med Inform Assoc 2021; 28:2385-2392. [PMID: 34498059 PMCID: PMC8499889 DOI: 10.1093/jamia/ocab175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 07/12/2021] [Accepted: 08/04/2021] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE Contact tracing of reported infections could enable close contacts to be identified, tested, and quarantined for controlling further spread. This strategy has been well demonstrated in the surveillance and control of COVID-19 (coronavirus disease 2019) epidemics. This study aims to leverage contact tracing data to investigate the degree of spread and the formation of transmission cascades composing of multiple clusters. MATERIALS AND METHODS An algorithm on mining relationships between clusters for network analysis is proposed with 3 steps: horizontal edge creation, vertical edge consolidation, and graph reduction. The constructed network was then analyzed with information diffusion metrics and exponential-family random graph modeling. With categorization of clusters by exposure setting, the metrics were compared among cascades to identify associations between exposure settings and their network positions within the cascade using Mann-Whitney U test. RESULTS Experimental results illustrated that transmission cascades containing or seeded by daily activity clusters spread faster while those containing social activity clusters propagated farther. Cascades involving work or study environments consisted of more clusters, which had a higher transmission range and scale. Social activity clusters were more likely to be connected, whereas both residence and healthcare clusters did not preferentially link to clusters belonging to the same exposure setting. CONCLUSIONS The proposed algorithm could contribute to in-depth epidemiologic investigation of infectious disease transmission to support targeted nonpharmaceutical intervention policies for COVID-19 epidemic control.
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Affiliation(s)
- Tsz Ho Kwan
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Ngai Sze Wong
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Eng-Kiong Yeoh
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Shui Shan Lee
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong
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6
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Galvis JA, Corzo CA, Prada JM, Machado G. Modelling the transmission and vaccination strategy for porcine reproductive and respiratory syndrome virus. Transbound Emerg Dis 2021; 69:485-500. [PMID: 33506620 DOI: 10.1111/tbed.14007] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 01/22/2021] [Accepted: 01/25/2021] [Indexed: 12/15/2022]
Abstract
Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between-farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between-farm animal movements (pig flow) and reinfection of sow farms (re-break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re-break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between-farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at-risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread.
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Affiliation(s)
- Jason A Galvis
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA
| | - Cesar A Corzo
- Veterinary Population Medicine Department, College of Veterinary Medicine, University of Minnesota, St Paul, MN, USA
| | - Joaquin M Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA
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7
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Galvis JA, Jones CM, Prada JM, Corzo CA, Machado G. The between-farm transmission dynamics of porcine epidemic diarrhoea virus: A short-term forecast modelling comparison and the effectiveness of control strategies. Transbound Emerg Dis 2021; 69:396-412. [PMID: 33475245 DOI: 10.1111/tbed.13997] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/11/2021] [Accepted: 01/18/2021] [Indexed: 01/10/2023]
Abstract
A limited understanding of the transmission dynamics of swine disease is a significant obstacle to prevent and control disease spread. Therefore, understanding between-farm transmission dynamics is crucial to developing disease forecasting systems to predict outbreaks that would allow the swine industry to tailor control strategies. Our objective was to forecast weekly porcine epidemic diarrhoea virus (PEDV) outbreaks by generating maps to identify current and future PEDV high-risk areas, and simulating the impact of control measures. Three epidemiological transmission models were developed and compared: a novel epidemiological modelling framework was developed specifically to model disease spread in swine populations, PigSpread, and two models built on previously developed ecosystems, SimInf (a stochastic disease spread simulations) and PoPS (Pest or Pathogen Spread). The models were calibrated on true weekly PEDV outbreaks from three spatially related swine production companies. Prediction accuracy across models was compared using the receiver operating characteristic area under the curve (AUC). Model outputs had a general agreement with observed outbreaks throughout the study period. PoPS had an AUC of 0.80, followed by PigSpread with 0.71, and SimInf had the lowest at 0.59. Our analysis estimates that the combined strategies of herd closure, controlled exposure of gilts to live viruses (feedback) and on-farm biosecurity reinforcement reduced the number of outbreaks. On average, 76% to 89% reduction was seen in sow farms, while in gilt development units (GDU) was between 33% to 61% when deployed to sow and GDU farms located in probabilistic high-risk areas. Our multi-model forecasting approach can be used to prioritize surveillance and intervention strategies for PEDV and other diseases potentially leading to more resilient and healthier pig production systems.
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Affiliation(s)
- Jason A Galvis
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA
| | - Chris M Jones
- Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA
| | - Joaquin M Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Cesar A Corzo
- Veterinary Population Medicine Department, College of Veterinary Medicine, University of Minnesota, St Paul, MN, USA
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA.,Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA
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8
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Pepin KM, Golnar A, Podgórski T. Social structure defines spatial transmission of African swine fever in wild boar. J R Soc Interface 2021; 18:20200761. [PMID: 33468025 DOI: 10.1098/rsif.2020.0761] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The spatial spread of infectious disease is determined by spatial and social processes such as animal space use and family group structure. Yet, the impacts of social processes on spatial spread remain poorly understood and estimates of spatial transmission kernels (STKs) often exclude social structure. Understanding the impacts of social structure on STKs is important for obtaining robust inferences for policy decisions and optimizing response plans. We fit spatially explicit transmission models with different assumptions about contact structure to African swine fever virus surveillance data from eastern Poland from 2014 to 2015 and evaluated how social structure affected inference of STKs and spatial spread. The model with social structure provided better inference of spatial spread, predicted that approximately 80% of transmission events occurred within family groups, and that transmission was weakly female-biased (other models predicted weakly male-biased transmission). In all models, most transmission events were within 1.5 km, with some rare events at longer distances. Effective reproductive numbers were between 1.1 and 2.5 (maximum values between 4 and 8). Social structure can modify spatial transmission dynamics. Accounting for this additional contact heterogeneity in spatial transmission models could provide more robust inferences of STKs for policy decisions, identify best control targets and improve transparency in model uncertainty.
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Affiliation(s)
- Kim M Pepin
- National Wildlife Research Center, USDA, APHIS, Wildlife Services, 4101 Laporte Avenue, Fort Collins, CO 80526, USA
| | - Andrew Golnar
- National Wildlife Research Center, USDA, APHIS, Wildlife Services, 4101 Laporte Avenue, Fort Collins, CO 80526, USA
| | - Tomasz Podgórski
- Mammal Research Institute, Polish Academy of Sciences, Stoczek 1, 17-230 Białowieża, Poland.,Department of Game Management and Wildlife Biology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kamýcká 129, 165 00 Praha 6, Czech Republic
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