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Horpiencharoen W, Marshall JC, Muylaert RL, John RS, Hayman DTS. Impact of infectious diseases on wild bovidae populations in Thailand: insights from population modelling and disease dynamics. J R Soc Interface 2024; 21:20240278. [PMID: 38955228 DOI: 10.1098/rsif.2024.0278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 06/10/2024] [Indexed: 07/04/2024] Open
Abstract
The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times in a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, haemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.
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Affiliation(s)
- Wantida Horpiencharoen
- Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North 4472, New Zealand
| | - Jonathan C Marshall
- Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North 4472, New Zealand
| | - Renata L Muylaert
- Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North 4472, New Zealand
| | - Reju Sam John
- Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North 4472, New Zealand
| | - David T S Hayman
- Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North 4472, New Zealand
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Seger HL, Sanderson MW, White BJ, Lanzas C. Analysis of within-pen and between-pen fenceline temporal contact networks in confined feedlot cattle. Prev Vet Med 2024; 227:106210. [PMID: 38688092 PMCID: PMC11247509 DOI: 10.1016/j.prevetmed.2024.106210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 03/26/2024] [Accepted: 04/14/2024] [Indexed: 05/02/2024]
Abstract
Though contact networks are important for describing the dynamics for disease transmission and intervention applications, individual animal contact and barriers between animal populations, such as fences, are not often utilized in the construction of these models. The objective of this study was to use contact network analysis to quantify contacts within two confined pens of feedlot cattle and the shared "fenceline" area between the pens at varying temporal resolutions and contact duration to better inform the construction of network-based disease transmission models for cattle within confined-housing systems. Two neighboring pens of feedlot steers were tagged with Real-Time Location System (RTLS) tags. Within-pen contacts were defined with a spatial threshold (SpTh) of 0.71 m and a minimum contact duration (MCD) of either 10 seconds (10 s), 30 seconds (30 s), or 60 seconds (60 s). For the fenceline network location readings were included within an area extending from 1 m on either side of the shared fence. "Fenceline" contacts could only occur between a steer from each pen. Static, undirected, weighted contact networks for within-pen networks and the between-pen network were generated for the full study duration and for daily (24-h), 6-h period, and hourly networks to better assess network heterogeneity. For the full study duration network, the two within-pen networks were densely homogenous. The within-pen networks showed more heterogeneity when smaller timescales (6-h period and hourly) were applied. When contacts were defined with a MCD of 30 s or 60 s, the total number of contacts seen in each network decreased, indicating that most of the contacts observed in our networks may have been transient passing contacts. Cosine similarity was moderate and stable across days for within pen networks. Of the 90 total tagged steers between the two pens, 86 steers (46 steers from Pen 2 and 40 steers from Pen 3) produced at least one contact across the shared fenceline. The total network density for the network created across the shared fenceline between the two pens was 17%, with few contacts at shorter timescales and for MCD of 30 s or 60 s. Overall, the contact networks created here from high-resolution spatial and temporal contact observation data provide estimates for a contact network within commercial US feedlot pens and the contact network created between two neighboring pens of cattle. These networks can be used to better inform pathogen transmission models on social contact networks.
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Affiliation(s)
- H L Seger
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, United States
| | - M W Sanderson
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, United States.
| | - B J White
- Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, United States
| | - C Lanzas
- Department of Population Health and Pathobiology, North Carolina State University College of Veterinary Medicine, Raleigh, NC 27606, United States
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Exploring the predictive capability of machine learning models in identifying foot and mouth disease outbreak occurrences in cattle farms in an endemic setting of Thailand. Prev Vet Med 2022; 207:105706. [DOI: 10.1016/j.prevetmed.2022.105706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/09/2022] [Accepted: 07/01/2022] [Indexed: 11/20/2022]
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Coffman MS, Sanderson MW, Dodd CC, Arzt J, Renter DG. Estimation of foot-and-mouth disease windborne transmission risk from USA beef feedlots. Prev Vet Med 2021; 195:105453. [PMID: 34479032 DOI: 10.1016/j.prevetmed.2021.105453] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 07/09/2021] [Accepted: 08/04/2021] [Indexed: 11/17/2022]
Abstract
Windborne spread of foot-and-mouth disease (FMD) requires specific epidemiological and meteorological conditions, thus modeling the risk of windborne spread involves integrating epidemiological and meteorological models. The objective of this study was to investigate the potential risk of windborne spread of FMD from an infected US feedlot using an integrated modeling approach, and to identify factors that determine this risk. To address this objective, we integrated a within-herd epidemiological model and an advanced atmospheric dispersion model, and calculated infection risk dependent on exposed herd size. A previously developed epidemiological model was used to simulate the spread of FMD through a typical U.S. feedlot, while the National Oceanic and Atmospheric Administration's (NOAA) HYSPLIT atmospheric dispersion model, which has been validated for FMD modeling, was used to model virus dispersion. Infection risk for exposed herds was calculated as a binomial probability accounting for dose and exposed herd size. We modeled risk of windborne spread from a typical 4000 head feedlot in the U.S. state of Iowa (IA), and a typical 48,000 head feedlot in the U.S. state of Kansas (KS) during winter and summer seasons. The risk of windborne spread of FMD varied based on weather/season conditions, estimated average viral shedding rate per head, size of infected herd, and size of exposed herd. In the baseline Kansas scenario (KS/103/W), the median of the maximum daily risk of infecting a 1000-head exposed herd ranged from 58.16 % at 1 km to 0.78 % at 10 km (Table 4). In the baseline Iowa scenario (IA/103/W), the median of the maximum daily risk of infecting a 1000-head exposed herd ranged from 21.78 % at 1 km to 0.05 % at 10 km (Table 4). The minimum control area recommended by the United States Department of Agriculture (USDA) in an FMD outbreak is 10 km from the infected premise. Our results indicate that significant risk of windborne spread may extend beyond 10 km in certain situations. This is particularly a concern in areas where there are large feedlots in relatively close proximity, such as in southwestern Kansas. Our model may be useful as a research tool in the absence of an outbreak and may help direct surveillance and response efforts in the event of an outbreak.
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Affiliation(s)
- M S Coffman
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
| | - M W Sanderson
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States.
| | - C C Dodd
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
| | - J Arzt
- Foreign Animal Disease Research Unit, USDA-ARS, Plum Island Animal Disease Center, Orient Pt., NY, United States
| | - D G Renter
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
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Cabezas AH, Sanderson MW, Volkova VV. Modeling Intervention Scenarios During Potential Foot-and-Mouth Disease Outbreaks Within U.S. Beef Feedlots. Front Vet Sci 2021; 8:559785. [PMID: 33665214 PMCID: PMC7921729 DOI: 10.3389/fvets.2021.559785] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 01/25/2021] [Indexed: 12/03/2022] Open
Abstract
Foot-and-mouth disease (FMD) is a highly contagious disease of livestock and has severely affected livestock industries during the past two decades in previously FMD-free countries. The disease was eliminated in North America in 1953 but remains a threat for re-introduction. Approximately 44% of the on-feed beef cattle in the U.S. are concentrated in feedlots <32,000 heads, but little information is available on dynamics of FMD in large feedlots. Therefore, there is a need to explore possible management and intervention strategies that might be implemented during potential FMD outbreaks on feedlots. We used a within home-pen stochastic susceptible-latent-infectious-recovered (SLIR) FMD dynamics model nested in a meta-population model of home-pens in a feedlot. The combinatory model was previously developed to simulate foot-and-mouth disease virus (FMDv) transmission within U.S. beef feedlots. We evaluated three intervention strategies initiated on the day of FMD detection: stopping movements of cattle between home-pens and hospital-pen(s) (NH), barrier depopulation combined with NH (NH-BD), and targeted depopulation of at-risk home-pens combined with NH (NH-TD). Depopulation rates investigated ranged from 500 to 4,000 cattle per day. We evaluated the projected effectiveness of interventions by comparing them with the no-intervention FMD dynamics in the feedlot. We modeled a small-size (4,000 cattle), medium-size (12,000 cattle), and large-size (24,000 cattle) feedlots. Implementation of NH delayed the outbreak progression, but it did not prevent infection of the entire feedlot. Implementation of NH-BD resulted in depopulation of 50% of cattle in small- and medium-size feedlots, and 25% in large-size feedlots, but the intervention prevented infection of the entire feedlot in 40% of simulated outbreaks in medium-size feedlots, and in 8% in large-size feedlots. Implementation of NH-TD resulted in depopulation of up to 50% of cattle in small-size feedlots, 75% in medium-size feedlots, and 25% in large-size feedlots, but rarely prevented infection of the entire feedlot. Number of hospital-pens in the feedlot was shown to weakly impact the success of NH-TD. Overall, the results suggest that stopping cattle movements between the home-pens and hospital-pens, without or with barrier or targeted cattle depopulation, would not be highly effective to interrupt FMDv transmission within a feedlot.
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Affiliation(s)
- Aurelio H Cabezas
- Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States.,Center for Outcomes Research and Epidemiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
| | - Michael W Sanderson
- Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States.,Center for Outcomes Research and Epidemiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
| | - Victoriya V Volkova
- Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States.,Center for Outcomes Research and Epidemiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
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