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Hayes BH, Vergne T, Andraud M, Rose N. Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species. Front Vet Sci 2023; 10:1225446. [PMID: 37745209 PMCID: PMC10511766 DOI: 10.3389/fvets.2023.1225446] [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: 05/19/2023] [Accepted: 08/17/2023] [Indexed: 09/26/2023] Open
Abstract
Modeling of infectious diseases at the livestock-wildlife interface is a unique subset of mathematical modeling with many innate challenges. To ascertain the characteristics of the models used in these scenarios, a scoping review of the scientific literature was conducted. Fifty-six studies qualified for inclusion. Only 14 diseases at this interface have benefited from the utility of mathematical modeling, despite a far greater number of shared diseases. The most represented species combinations were cattle and badgers (for bovine tuberculosis, 14), and pigs and wild boar [for African (8) and classical (3) swine fever, and foot-and-mouth and disease (1)]. Assessing control strategies was the overwhelming primary research objective (27), with most studies examining control strategies applied to wildlife hosts and the effect on domestic hosts (10) or both wild and domestic hosts (5). In spatially-explicit models, while livestock species can often be represented through explicit and identifiable location data (such as farm, herd, or pasture locations), wildlife locations are often inferred using habitat suitability as a proxy. Though there are innate assumptions that may not be fully accurate when using habitat suitability to represent wildlife presence, especially for wildlife the parsimony principle plays a large role in modeling diseases at this interface, where parameters are difficult to document or require a high level of data for inference. Explaining observed transmission dynamics was another common model objective, though the relative contribution of involved species to epizootic propagation was only ascertained in a few models. More direct evidence of disease spill-over, as can be obtained through genomic approaches based on pathogen sequences, could be a useful complement to further inform such modeling. As computational and programmatic capabilities advance, the resolution of the models and data used in these models will likely be able to increase as well, with a potential goal being the linking of modern complex ecological models with the depth of dynamics responsible for pathogen transmission. Controlling diseases at this interface is a critical step toward improving both livestock and wildlife health, and mechanistic models are becoming increasingly used to explore the strategies needed to confront these diseases.
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Affiliation(s)
- Brandon H. Hayes
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
- Ploufragan-Plouzané-Niort Laboratory, The French Agency for Food, Agriculture and the Environment (ANSES), Ploufragan, France
| | | | - Mathieu Andraud
- Ploufragan-Plouzané-Niort Laboratory, The French Agency for Food, Agriculture and the Environment (ANSES), Ploufragan, France
| | - Nicolas Rose
- Ploufragan-Plouzané-Niort Laboratory, The French Agency for Food, Agriculture and the Environment (ANSES), Ploufragan, France
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Miller RS, Bevins SN, Cook G, Free R, Pepin KM, Gidlewski T, Brown VR. Adaptive risk-based targeted surveillance for foreign animal diseases at the wildlife-livestock interface. Transbound Emerg Dis 2022; 69:e2329-e2340. [PMID: 35490290 PMCID: PMC9790623 DOI: 10.1111/tbed.14576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 12/30/2022]
Abstract
Animal disease surveillance is an important component of the national veterinary infrastructure to protect animal agriculture and facilitates identification of foreign animal disease (FAD) introduction. Once introduced, pathogens shared among domestic and wild animals are especially challenging to manage due to the complex ecology of spillover and spillback. Thus, early identification of FAD in wildlife is critical to minimize outbreak severity and potential impacts on animal agriculture as well as potential impacts on wildlife and biodiversity. As a result, national surveillance and monitoring programs that include wildlife are becoming increasingly common. Designing surveillance systems in wildlife or, more importantly, at the interface of wildlife and domestic animals, is especially challenging because of the frequent lack of ecological and epidemiological data for wildlife species and technical challenges associated with a lack of non-invasive methodologies. To meet the increasing need for targeted FAD surveillance and to address gaps in existing wildlife surveillance systems, we developed an adaptive risk-based targeted surveillance approach that accounts for risks in source and recipient host populations. The approach is flexible, accounts for changing disease risks through time, can be scaled from local to national extents and permits the inclusion of quantitative data or when information is limited to expert opinion. We apply this adaptive risk-based surveillance framework to prioritize areas for surveillance in wild pigs in the United States with the objective of early detection of three diseases: classical swine fever, African swine fever and foot-and-mouth disease. We discuss our surveillance framework, its application to wild pigs and discuss the utility of this framework for surveillance of other host species and diseases.
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Affiliation(s)
- Ryan S. Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Services, Veterinary ServicesCenter for Epidemiology and Animal HealthFort CollinsColoradoUSA
| | - Sarah N. Bevins
- United States Department of Agriculture, Animal and Plant Health Inspection Services, Wildlife ServicesNational Wildlife Research CenterFort CollinsColoradoUSA
| | - Gericke Cook
- United States Department of Agriculture, Animal and Plant Health Inspection Services, Veterinary ServicesCenter for Epidemiology and Animal HealthFort CollinsColoradoUSA
| | - Ross Free
- United States Department of Agriculture, Animal and Plant Health Inspection Services, Veterinary ServicesSwine Commodity HealthRaleighNorth CarolinaUSA
| | - Kim M. Pepin
- United States Department of Agriculture, Animal and Plant Health Inspection Services, Wildlife ServicesNational Wildlife Research CenterFort CollinsColoradoUSA
| | - Thomas Gidlewski
- United States Department of Agriculture, Animal and Plant Health Inspection Services, Wildlife ServicesNational Wildlife Disease ProgramFort CollinsColoradoUSA
| | - Vienna R. Brown
- United States Department of Agriculture, Animal and Plant Health Inspection Services, Wildlife ServicesNational Feral Swine Damage Management ProgramFort CollinsColoradoUSA
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Manlove K, Wilber M, White L, Bastille‐Rousseau G, Yang A, Gilbertson MLJ, Craft ME, Cross PC, Wittemyer G, Pepin KM. Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace‐of‐life. Ecol Lett 2022; 25:1760-1782. [DOI: 10.1111/ele.14032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/21/2022] [Accepted: 05/03/2022] [Indexed: 12/20/2022]
Affiliation(s)
- Kezia Manlove
- Department of Wildland Resources and Ecology Center Utah State University Logan Utah USA
| | - Mark Wilber
- Department of Forestry, Wildlife, and Fisheries University of Tennessee Institute of Agriculture Knoxville Tennessee USA
| | - Lauren White
- National Socio‐Environmental Synthesis Center University of Maryland Annapolis Maryland USA
| | | | - Anni Yang
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA
- National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services National Wildlife Research Center Fort Collins Colorado USA
- Department of Geography and Environmental Sustainability University of Oklahoma Norman Oklahoma USA
| | - Marie L. J. Gilbertson
- Department of Veterinary Population Medicine University of Minnesota St. Paul Minnesota USA
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology University of Wisconsin–Madison Madison Wisconsin USA
| | - Meggan E. Craft
- Department of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota USA
| | - Paul C. Cross
- U.S. Geological Survey Northern Rocky Mountain Science Center Bozeman Montana USA
| | - George Wittemyer
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA
| | - Kim M. Pepin
- National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services National Wildlife Research Center Fort Collins Colorado USA
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Bacigalupo SA, Chang Y, Dixon LK, Gubbins S, Kucharski AJ, Drewe JA. The importance of fine-scale predictors of wild boar habitat use in an isolated population. Ecol Evol 2022; 12:e9031. [PMID: 35784084 PMCID: PMC9217887 DOI: 10.1002/ece3.9031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 12/30/2022] Open
Abstract
Predicting the likelihood of wildlife presence at potential wildlife-livestock interfaces is challenging. These interfaces are usually relatively small geographical areas where landscapes show large variation over small distances. Models of wildlife distribution based on coarse data over wide geographical ranges may not be representative of these interfaces. High-resolution data can help identify fine-scale predictors of wildlife habitat use at a local scale and provide more accurate predictions of species habitat use. These data may be used to inform knowledge of interface risks, such as disease transmission between wildlife and livestock, or human-wildlife conflict.This study uses fine-scale habitat use data from wild boar (Sus scrofa) based on activity signs and direct field observations in and around the Forest of Dean in Gloucestershire, England. Spatial logistic regression models fitted using a variant of penalized quasi-likelihood were used to identify habitat-based and anthropogenic predictors of wild boar signs.Our models showed that within the Forest of Dean, wild boar signs were more likely to be seen in spring, in forest-type habitats, closer to the center of the forest and near litter bins. In the area surrounding the Forest of Dean, wild boar signs were more likely to be seen in forest-type habitats and near recreational parks and less likely to be seen near livestock.This approach shows that wild boar habitat use can be predicted using fine-scale data over comparatively small areas and in human-dominated landscapes, while taking account of the spatial correlation from other nearby fine-scale data-points. The methods we use could be applied to map habitat use of other wildlife species in similar landscapes, or of movement-restricted, isolated, or fragmented wildlife populations.
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Affiliation(s)
| | - Yu‐mei Chang
- Royal Veterinary CollegeUniversity of LondonHatfieldUK
| | | | | | - Adam J. Kucharski
- London School of Hygiene & Tropical MedicineUniversity of LondonLondonUK
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Becker DJ, Washburne AD, Faust CL, Pulliam JRC, Mordecai EA, Lloyd-Smith JO, Plowright RK. Dynamic and integrative approaches to understanding pathogen spillover. Philos Trans R Soc Lond B Biol Sci 2019; 374:20190014. [PMID: 31401959 PMCID: PMC6711302 DOI: 10.1098/rstb.2019.0014] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2019] [Indexed: 12/23/2022] Open
Affiliation(s)
- Daniel J. Becker
- Department of Microbiology and Immunology, Montana State University, Bozeman, MT 59717, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
- Department of Biology, Indiana University, Bloomington, IN, USA
| | - Alex D. Washburne
- Department of Microbiology and Immunology, Montana State University, Bozeman, MT 59717, USA
| | - Christina L. Faust
- Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Juliet R. C. Pulliam
- South African Centre for Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | | | - James O. Lloyd-Smith
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Raina K. Plowright
- Department of Microbiology and Immunology, Montana State University, Bozeman, MT 59717, USA
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Sokolow SH, Nova N, Pepin KM, Peel AJ, Pulliam JRC, Manlove K, Cross PC, Becker DJ, Plowright RK, McCallum H, De Leo GA. Ecological interventions to prevent and manage zoonotic pathogen spillover. Philos Trans R Soc Lond B Biol Sci 2019; 374:20180342. [PMID: 31401951 PMCID: PMC6711299 DOI: 10.1098/rstb.2018.0342] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Spillover of a pathogen from a wildlife reservoir into a human or livestock host requires the pathogen to overcome a hierarchical series of barriers. Interventions aimed at one or more of these barriers may be able to prevent the occurrence of spillover. Here, we demonstrate how interventions that target the ecological context in which spillover occurs (i.e. ecological interventions) can complement conventional approaches like vaccination, treatment, disinfection and chemical control. Accelerating spillover owing to environmental change requires effective, affordable, durable and scalable solutions that fully harness the complex processes involved in cross-species pathogen spillover. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.
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Affiliation(s)
- Susanne H Sokolow
- Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA.,Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA.,Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | - Nicole Nova
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Kim M Pepin
- National Wildlife Research Center, USDA-APHIS, Fort Collins, CO 80521, USA
| | - Alison J Peel
- Environmental Futures Research Institute, Griffith University, Nathan, Queensland 4111, Australia
| | - Juliet R C Pulliam
- South African DST-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch 7600, South Africa
| | - Kezia Manlove
- Department of Wildland Resources and Ecology Center, Utah State University, Logan, UT 84321, USA
| | - Paul C Cross
- US Geological Survey, Northern Rocky Mountain Science Center, Bozeman, MT 59715, USA
| | - Daniel J Becker
- Department of Microbiology and Immunology, Montana State University, Bozeman, MT 59717, USA.,Department of Biology, Indiana University, Bloomington, IN 47403, USA
| | - Raina K Plowright
- Department of Microbiology and Immunology, Montana State University, Bozeman, MT 59717, USA
| | - Hamish McCallum
- Environmental Futures Research Institute, Griffith University, Nathan, Queensland 4111, Australia
| | - Giulio A De Leo
- Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA.,Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA.,Department of Biology, Stanford University, Stanford, CA 94305, USA
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