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Seibel RL, Meadows AJ, Mundt C, Tildesley M. Modeling target-density-based cull strategies to contain foot-and-mouth disease outbreaks. PeerJ 2024; 12:e16998. [PMID: 38436010 PMCID: PMC10909358 DOI: 10.7717/peerj.16998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
Total ring depopulation is sometimes used as a management strategy for emerging infectious diseases in livestock, which raises ethical concerns regarding the potential slaughter of large numbers of healthy animals. We evaluated a farm-density-based ring culling strategy to control foot-and-mouth disease (FMD) in the United Kingdom (UK), which may allow for some farms within rings around infected premises (IPs) to escape depopulation. We simulated this reduced farm density, or "target density", strategy using a spatially-explicit, stochastic, state-transition algorithm. We modeled FMD spread in four counties in the UK that have different farm demographics, using 740,000 simulations in a full-factorial analysis of epidemic impact measures (i.e., culled animals, culled farms, and epidemic length) and cull strategy parameters (i.e., target farm density, daily farm cull capacity, and cull radius). All of the cull strategy parameters listed above were drivers of epidemic impact. Our simulated target density strategy was usually more effective at combatting FMD compared with traditional total ring depopulation when considering mean culled animals and culled farms and was especially effective when daily farm cull capacity was low. The differences in epidemic impact measures among the counties are likely driven by farm demography, especially differences in cattle and farm density. To prevent over-culling and the associated economic, organizational, ethical, and psychological impacts, the target density strategy may be worth considering in decision-making processes for future control of FMD and other diseases.
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Affiliation(s)
- Rachel L. Seibel
- Mathematics Institute, University of Warwick, Coventry, West Midlands, United Kingdom
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Amanda J. Meadows
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
- Ginkgo Bioworks, San Bruno, California, United States
| | - Christopher Mundt
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Michael Tildesley
- Mathematics Institute, University of Warwick, Coventry, West Midlands, United Kingdom
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2
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Li Y, Mayberry D, Jemberu W, Schrobback P, Herrero M, Chaters G, Knight-Jones T, Rushton J. Characterizing Ethiopian cattle production systems for disease burden analysis. Front Vet Sci 2023; 10:1233474. [PMID: 37885617 PMCID: PMC10598381 DOI: 10.3389/fvets.2023.1233474] [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: 06/02/2023] [Accepted: 08/17/2023] [Indexed: 10/28/2023] Open
Abstract
This paper addresses knowledge gaps in the biomass, productivity and value of livestock for the pastoral, mixed crop-livestock and specialized dairy systems in Ethiopia. Population size, reproductive performance, mortality, offtake and productivity of cattle were calculated from official statistics and a meta-analysis of data available in the published literature. This information was then used to estimate biomass and output value for 2020 using a herd dynamics model. The mixed-crop livestock system dominates the Ethiopian cattle sector, with 55 million cattle (78% total population) and contributing 8.52 billion USD to the economy through the provision of meat, milk, hides and draft power in 2021. By comparison, the pastoral (13.4 million head) and specialized dairy (1.8 million head) systems are much smaller. Productivity varied between different production systems, with differences in live body weight, productivity and prices from different sources. The estimated total cattle biomass was 14.8 billion kg in 2021, i.e., 11.3 billion kg in the mixed crop-livestock system, 2.60 billion kg in the pastoral system and 0.87 billion kg in the specialized dairy system. The total economic asset values of cattle in the mixed crop-livestock, pastoral and specialized dairy systems were estimated as 24.8, 5.28 and 1.37 billion USD, respectively. The total combined output value (e.g., beef, milk and draft power) of cattle production was 11.9 billion USD, which was 11.2% of the GDP in Ethiopia in 2021. This work quantifies the importance of cattle in the Ethiopian economy. These estimates of herd structure, reproductive performance, productivity, biomass, and economic value for cattle production systems in Ethiopia can be used to inform high-level policy, revealing under-performance and areas to prioritize and provide a basis for further technical analysis, such as disease burden.
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Affiliation(s)
- Yin Li
- Global Burden of Animal Diseases Programme, Liverpool, United Kingdom
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Brisbane, QLD, Australia
- School of Veterinary Medicine and Centre for Biosecurity and One Health, Harry Butler Institute, Murdoch University, Perth, WA, Australia
| | - Dianne Mayberry
- Global Burden of Animal Diseases Programme, Liverpool, United Kingdom
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Brisbane, QLD, Australia
| | - Wudu Jemberu
- Global Burden of Animal Diseases Programme, Liverpool, United Kingdom
- International Livestock Research Institute, Addis Ababa, Ethiopia
- Faculty of Veterinary Medicine, University of Gondar, Gondar, Ethiopia
| | - Peggy Schrobback
- Global Burden of Animal Diseases Programme, Liverpool, United Kingdom
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Brisbane, QLD, Australia
| | - Mario Herrero
- Global Burden of Animal Diseases Programme, Liverpool, United Kingdom
- College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
| | - Gemma Chaters
- Global Burden of Animal Diseases Programme, Liverpool, United Kingdom
- Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
| | - Theodore Knight-Jones
- Global Burden of Animal Diseases Programme, Liverpool, United Kingdom
- International Livestock Research Institute, Addis Ababa, Ethiopia
| | - Jonathan Rushton
- Global Burden of Animal Diseases Programme, Liverpool, United Kingdom
- Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
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Bauzile B, Durand B, Lambert S, Rautureau S, Fourtune L, Guinat C, Andronico A, Cauchemez S, Paul MC, Vergne T. Impact of palmiped farm density on the resilience of the poultry sector to highly pathogenic avian influenza H5N8 in France. Vet Res 2023; 54:56. [PMID: 37430292 PMCID: PMC10334606 DOI: 10.1186/s13567-023-01183-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/22/2023] [Indexed: 07/12/2023] Open
Abstract
We analysed the interplay between palmiped farm density and the vulnerability of the poultry production system to highly pathogenic avian influenza (HPAI) H5N8. To do so, we used a spatially-explicit transmission model, which was calibrated to reproduce the observed spatio-temporal distribution of outbreaks in France during the 2016-2017 epidemic of HPAI. Six scenarios were investigated, in which the density of palmiped farms was decreased in the municipalities with the highest palmiped farm density. For each of the six scenarios, we first calculated the spatial distribution of the basic reproduction number (R0), i.e. the expected number of farms a particular farm would be likely to infect, should all other farms be susceptible. We also ran in silico simulations of the adjusted model for each scenario to estimate epidemic sizes and time-varying effective reproduction numbers. We showed that reducing palmiped farm density in the densest municipalities decreased substantially the size of the areas with high R0 values (> 1.5). In silico simulations suggested that reducing palmiped farm density, even slightly, in the densest municipalities was expected to decrease substantially the number of affected poultry farms and therefore provide benefits to the poultry sector as a whole. However, they also suggest that it would not have been sufficient, even in combination with the intervention measures implemented during the 2016-2017 epidemic, to completely prevent the virus from spreading. Therefore, the effectiveness of alternative structural preventive approaches now needs to be assessed, including flock size reduction and targeted vaccination.
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Affiliation(s)
- Billy Bauzile
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Benoit Durand
- Laboratory for Animal Health, French Agency for Food, Environmental and Occupational Health and Safety (ANSES), University Paris-Est, 14 rue Pierre et Marie Curie, 94700, Maisons-Alfort, France
| | | | | | - Lisa Fourtune
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Claire Guinat
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Alessio Andronico
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris Cité, CNRS UMR2000, 75015, Paris, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris Cité, CNRS UMR2000, 75015, Paris, France
| | | | - Timothée Vergne
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France.
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4
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Puspitarani GA, Fuchs R, Fuchs K, Ladinig A, Desvars-Larrive A. Network analysis of pig movement data as an epidemiological tool: an Austrian case study. Sci Rep 2023; 13:9623. [PMID: 37316653 PMCID: PMC10267221 DOI: 10.1038/s41598-023-36596-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.
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Affiliation(s)
- Gavrila A Puspitarani
- Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria.
- Complexity Science Hub Vienna, Josefstaedter Strasse 39, 1080, Vienna, Austria.
| | - Reinhard Fuchs
- Department for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Zinzendorfgasse 27/1, 8010, Graz, Austria
- Institute of Systems Sciences, Innovation and Sustainability Research, University of Graz, Merangasse 18/1, 8010, Graz, Austria
| | - Klemens Fuchs
- Department for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Zinzendorfgasse 27/1, 8010, Graz, Austria
| | - Andrea Ladinig
- University Clinic for Swine, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria
| | - Amélie Desvars-Larrive
- Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria
- Complexity Science Hub Vienna, Josefstaedter Strasse 39, 1080, Vienna, Austria
- VetFarm, University of Veterinary Medicine Vienna, Kremesberg 13, 2563, Pottenstein, Austria
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5
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Chakraborty D, Guinat C, Müller NF, Briand F, Andraud M, Scoizec A, Lebouquin S, Niqueux E, Schmitz A, Grasland B, Guerin J, Paul MC, Vergne T. Phylodynamic analysis of the highly pathogenic avian influenza H5N8 epidemic in France, 2016-2017. Transbound Emerg Dis 2022; 69:e1574-e1583. [PMID: 35195353 PMCID: PMC9790735 DOI: 10.1111/tbed.14490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 01/14/2022] [Accepted: 02/15/2022] [Indexed: 12/30/2022]
Abstract
In 2016-2017, France experienced a devastating epidemic of highly pathogenic avian influenza (HPAI) H5N8, with more than 400 outbreaks reported in poultry farms. We analyzed the spatiotemporal dynamics of the epidemic using a structured-coalescent-based phylodynamic approach that combined viral genomic data (n = 196; one viral genome per farm) and epidemiological data. In the process, we estimated viral migration rates between départements (French administrative regions) and the temporal dynamics of the effective viral population size (Ne) in each département. Viral migration rates quantify viral spread between départements and Ne is a population genetic measure of the epidemic size and, in turn, is indicative of the within-département transmission intensity. We extended the phylodynamic analysis with a generalized linear model to assess the impact of multiple factors-including large-scale preventive culling and live-duck movement bans-on viral migration rates and Ne. We showed that the large-scale culling of ducks that was initiated on 4 January 2017 significantly reduced the viral spread between départements. No relationship was found between the viral spread and duck movements between départements. The within-département transmission intensity was found to be weakly associated with the intensity of duck movements within départements. Together, these results indicated that the virus spread in short distances, either between adjacent départements or within départements. Results also suggested that the restrictions on duck transport within départements might not have stopped the viral spread completely. Overall, we demonstrated the usefulness of phylodynamics in characterizing the dynamics of a HPAI epidemic and assessing control measures. This method can be adapted to investigate other epidemics of fast-evolving livestock pathogens.
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Affiliation(s)
| | - Claire Guinat
- Department of Biosystems Science and EngineeringETH ZürichMattenstrasseBaselSwitzerland
- Swiss Institute of Bioinformatics (SIB)LausanneSwitzerland
| | - Nicola F. Müller
- Vaccine and Infectious DiseaseFred Hutchinson Cancer Research CentreSeattleWashingtonUSA
| | - Francois‐Xavier Briand
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES) Laboratory of Ploufragan‐Plouzané‐NiortPloufraganFrance
| | - Mathieu Andraud
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES) Laboratory of Ploufragan‐Plouzané‐NiortPloufraganFrance
| | - Axelle Scoizec
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES) Laboratory of Ploufragan‐Plouzané‐NiortPloufraganFrance
| | - Sophie Lebouquin
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES) Laboratory of Ploufragan‐Plouzané‐NiortPloufraganFrance
| | - Eric Niqueux
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES) Laboratory of Ploufragan‐Plouzané‐NiortPloufraganFrance
| | - Audrey Schmitz
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES) Laboratory of Ploufragan‐Plouzané‐NiortPloufraganFrance
| | - Beatrice Grasland
- French Agency for Food, Environmental and Occupational Health & Safety (ANSES) Laboratory of Ploufragan‐Plouzané‐NiortPloufraganFrance
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Abstract
AbstractThis review addresses ways to prepare for and to mitigate effects of biohazards on primary production of crops and livestock. These biohazards can be natural or intentional introductions of pathogens, and they can cause major economic damage to farmers, the agricultural industry, society, and international trade. Agroterrorism is the intentional introduction of animal or plant pathogens into agricultural production systems with the intention to cause socioeconomic harm and generate public fear. Although few acts of agroterrorism are reported, the threat of agroterrorism in Europe is real. New concerns about threats arise from the rapid advancements in biotechnology and emerging technologies. FORSA, an analytical framework for risk and vulnerability analysis, was used to review how to prepare for and mitigate the possible effects of natural or intentional biohazards in agricultural production. Analyzing the effects of a biohazard event involves multiple scientific disciplines. A comprehensive analysis of biohazards therefore requires a systems approach. The preparedness and ability to manage events are strengthened by bolstered farm biosecurity, increased monitoring and laboratory capacity, improved inter-agency communication and resource allocation. The focus of this review is on Europe, but the insights gained have worldwide applications. The analytical framework used here is compared to other frameworks. With climate change, Covid-19 and the war in Ukraine, the supply chains are challenged, and we foresee increasing food prices associated with social tensions. Our food supply chain becomes more fragile with more unknowns, thereby increasing the needs for risk and vulnerability analyses, of which FORSA is one example.
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7
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Bartlett H, Holmes MA, Petrovan SO, Williams DR, Wood JLN, Balmford A. Understanding the relative risks of zoonosis emergence under contrasting approaches to meeting livestock product demand. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211573. [PMID: 35754996 PMCID: PMC9214290 DOI: 10.1098/rsos.211573] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/30/2022] [Indexed: 05/03/2023]
Abstract
It has been argued that intensive livestock farming increases the risk of pandemics of zoonotic origin because of long-distance livestock movements, high livestock densities, poor animal health and welfare, low disease resistance and low genetic diversity. However, data on many of these factors are limited, and analyses to date typically ignore how land use affects emerging infectious disease (EID) risks, and how these risks might vary across systems with different yields (production per unit area). Extensive, lower yielding practices typically involve larger livestock populations, poorer biosecurity, more workers and more area under farming, resulting in different, but not necessarily lower, EID risks than higher yielding systems producing the same amount of food. To move this discussion forward, we review the evidence for each of the factors that potentially link livestock production practices to EID risk. We explore how each factor might vary with yield and consider how overall risks might differ across a mix of production systems chosen to reflect in broad terms the current livestock sector at a global level and in hypothetical low- and high-yield systems matched by overall level of production. We identify significant knowledge gaps for all potential risk factors and argue these shortfalls in understanding mean we cannot currently determine whether lower or higher yielding systems would better limit the risk of future pandemics.
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Affiliation(s)
- Harriet Bartlett
- Department of Zoology, University of Cambridge, Cambridge, UK
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Mark A. Holmes
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Silviu O. Petrovan
- Department of Zoology, University of Cambridge, Cambridge, UK
- BioRISC (Biosecurity Research Initiative at St Catharine's), St Catharine's College, Cambridge, UK
| | - David R. Williams
- Sustainability Research Institute, School of Earth and Environment, University of Leeds, Leeds, UK
| | - James L. N. Wood
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Andrew Balmford
- Department of Zoology, University of Cambridge, Cambridge, UK
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8
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Grover E, Paull S, Kechris K, Buchwald A, James K, Liu Y, Carlton EJ. Predictors of bovine Schistosoma japonicum infection in rural sichuan, china. Int J Parasitol 2022; 52:485-496. [DOI: 10.1016/j.ijpara.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 11/05/2022]
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9
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Si R, Zhang X, Yao Y, Liu L, Lu Q. Influence of contract commitment system in reducing information asymmetry, and prevention and control of livestock epidemics: Evidence from pig farmers in China. One Health 2021; 13:100302. [PMID: 34430696 PMCID: PMC8365388 DOI: 10.1016/j.onehlt.2021.100302] [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: 05/11/2021] [Revised: 07/21/2021] [Accepted: 08/02/2021] [Indexed: 11/30/2022] Open
Abstract
The prevention and control of infectious diseases in livestock is of great significance for maintaining the food and health of people. The main bottleneck in preventing and controlling the epidemic is asymmetrical information between farmers and the livestock department regarding dead livestock. In this pursuit, China has levied the contract commitment system to ensure farmers to cooperate with livestock departments, cooperative organizations, and other farmers by proper contract in order to combat the livestock epidemic by reporting the status of dead livestock on time. Based on the data of 514 pig farmers in Hebei, Henan, and Hubei, this research employed the Heckprobit model to explore the contract commitment system's effect on pig farmers' behavior in reporting the status of dead livestock. The outcome showed that the contract commitment system encouraged the farmers to report dead pig information promptly. Moreover, modern information channels such as mobile phones or the Internet further enhanced the contract commitment system's effectiveness. Besides, the impacts of the contract commitment system on different scale farmers are found substantially heterogeneous. Based on the empirical findings, it is confirmed that the contract commitment system should not exclude government regulatory measures and economic incentive policies. It is a useful remedy to encourage farmers to report dead livestock information on time and supports in preventing and controlling livestock epidemics. Additionally, the government should enhance and strengthen the contract commitment system, establish the channels and platforms required to deliver necessary information about epidemics, and implement differentiated policy programs for different scale farmers. More importantly, these countermeasures can also provide important guidelines for other developing countries, facing livestock epidemics.
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Affiliation(s)
- Ruishi Si
- School of Public Administration, Xi'an University of Architecture and Technology, Xi'an 710055, China
| | - Xueqian Zhang
- School of Public Administration, Xi'an University of Architecture and Technology, Xi'an 710055, China
| | - Yumeng Yao
- School of Public Administration, Xi'an University of Architecture and Technology, Xi'an 710055, China
| | - Li Liu
- Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100083, China
| | - Qian Lu
- College of Economics and Management, Northwest A&F University, Yangling 712100, China
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Patyk KA, McCool-Eye MJ, South DD, Burdett CL, Maroney SA, Fox A, Kuiper G, Magzamen S. Modelling the domestic poultry population in the United States: A novel approach leveraging remote sensing and synthetic data methods. GEOSPATIAL HEALTH 2020; 15. [PMID: 33461269 DOI: 10.4081/gh.2020.913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/27/2020] [Indexed: 06/12/2023]
Abstract
Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.
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Affiliation(s)
- Kelly A Patyk
- United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health.
| | - Mary J McCool-Eye
- United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health.
| | - David D South
- United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health.
| | - Christopher L Burdett
- Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO.
| | - Susan A Maroney
- Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO.
| | - Andrew Fox
- United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health.
| | - Grace Kuiper
- Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO.
| | - Sheryl Magzamen
- Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO.
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11
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Zaheer MU, Salman MD, Steneroden KK, Magzamen SL, Weber SE, Case S, Rao S. Challenges to the Application of Spatially Explicit Stochastic Simulation Models for Foot-and-Mouth Disease Control in Endemic Settings: A Systematic Review. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:7841941. [PMID: 33294003 PMCID: PMC7700052 DOI: 10.1155/2020/7841941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 10/20/2020] [Accepted: 10/30/2020] [Indexed: 11/17/2022]
Abstract
Simulation modeling has become common for estimating the spread of highly contagious animal diseases. Several models have been developed to mimic the spread of foot-and-mouth disease (FMD) in specific regions or countries, conduct risk assessment, analyze outbreaks using historical data or hypothetical scenarios, assist in policy decisions during epidemics, formulate preparedness plans, and evaluate economic impacts. Majority of the available FMD simulation models were designed for and applied in disease-free countries, while there has been limited use of such models in FMD endemic countries. This paper's objective was to report the findings from a study conducted to review the existing published original research literature on spatially explicit stochastic simulation (SESS) models of FMD spread, focusing on assessing these models for their potential use in endemic settings. The goal was to identify the specific components of endemic FMD needed to adapt these SESS models for their potential application in FMD endemic settings. This systematic review followed the PRISMA guidelines, and three databases were searched, which resulted in 1176 citations. Eighty citations finally met the inclusion criteria and were included in the qualitative synthesis, identifying nine unique SESS models. These SESS models were assessed for their potential application in endemic settings. The assessed SESS models can be adapted for use in FMD endemic countries by modifying the underlying code to include multiple cocirculating serotypes, routine prophylactic vaccination (RPV), and livestock population dynamics to more realistically mimic the endemic characteristics of FMD. The application of SESS models in endemic settings will help evaluate strategies for FMD control, which will improve livestock health, provide economic gains for producers, help alleviate poverty and hunger, and will complement efforts to achieve the Sustainable Development Goals.
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Affiliation(s)
- Muhammad Usman Zaheer
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
- FMD Project Office, Food and Agriculture Organization of the United Nations, ASI Premises, NARC Gate # 2, Park Road, Islamabad 44000, Pakistan
| | - Mo D. Salman
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
| | - Kay K. Steneroden
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
| | - Sheryl L. Magzamen
- Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
| | - Stephen E. Weber
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
| | - Shaun Case
- Department of Civil and Environmental Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins CO 80521, USA
| | - Sangeeta Rao
- Animal Population Health Institute, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins CO 80523, USA
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12
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Bastard J, Andraud M, Chauvin C, Glaser P, Opatowski L, Temime L. Dynamics of livestock-associated methicillin resistant Staphylococcus aureus in pig movement networks: Insight from mathematical modeling and French data. Epidemics 2020; 31:100389. [PMID: 32146319 DOI: 10.1016/j.epidem.2020.100389] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 12/17/2019] [Accepted: 02/07/2020] [Indexed: 12/15/2022] Open
Abstract
Livestock-associated methicillin resistant Staphylococcus aureus (LA-MRSA) colonizes livestock animals worldwide, especially pigs and calves. Although frequently carried asymptomatically, LA-MRSA can cause severe infections in humans. It is therefore important to better understand LA-MRSA spreading dynamics within pig farms and over pig movement networks, and to compare different strategies of control and surveillance. For this purpose, we propose a stochastic meta-population model of LA-MRSA spread along the French pig movement network (n = 10,542 farms), combining within- and between-farm dynamics, based on detailed data on breeding practices and pig movements between holdings. We calibrate the model using French epidemiological data. We then identify farm-level factors associated with the spreading potential of LA-MRSA in the network. We also show that, assuming control measures applied in a limited (n = 100) number of farms, targeting farms depending on their centrality in the network is the only way to significantly reduce LA-MRSA global prevalence. Finally, we investigate the scenario of emergence of a new LA-MRSA strain, and find that the farms with the highest indegree would be the best sentinels for a targeted surveillance of such a strain's introduction.
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Affiliation(s)
- Jonathan Bastard
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, F-78180, Montigny-Le-Bretonneux, France; Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion unit, F-75015, Paris, France; MESuRS Laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003 Paris, France; PACRI Unit, Conservatoire National des Arts et Métiers, Institut Pasteur, Paris, France; Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
| | - Mathieu Andraud
- Ploufragan Plouzané Niort Laboratory, Epidemiology, Health and Welfare Research Unit, Anses, BP 53, 22440 Ploufragan, France; Université Bretagne Loire, Cité internationale, 1 place Paul Ricoeur CS 54417, 35044 Rennes, France
| | - Claire Chauvin
- Ploufragan Plouzané Niort Laboratory, Epidemiology, Health and Welfare Research Unit, Anses, BP 53, 22440 Ploufragan, France; Université Bretagne Loire, Cité internationale, 1 place Paul Ricoeur CS 54417, 35044 Rennes, France
| | - Philippe Glaser
- Ecology and Evolution of Antibiotics Resistance (EERA) Unit, CNRS UMR 3525, Institut Pasteur, AP-HP, Université Paris-Sud, 28 Rue du Docteur Roux, 75015 Paris, France
| | - Lulla Opatowski
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, F-78180, Montigny-Le-Bretonneux, France; Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion unit, F-75015, Paris, France
| | - Laura Temime
- MESuRS Laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003 Paris, France; PACRI Unit, Conservatoire National des Arts et Métiers, Institut Pasteur, Paris, France
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Lee J, Ko Y, Jung E. Effective control measures considering spatial heterogeneity to mitigate the 2016-2017 avian influenza epidemic in the Republic of Korea. PLoS One 2019; 14:e0218202. [PMID: 31194835 PMCID: PMC6564009 DOI: 10.1371/journal.pone.0218202] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 05/28/2019] [Indexed: 11/18/2022] Open
Abstract
During the winter of 2016-2017, an epidemic of highly pathogenic avian influenza (HPAI) led to high mortality in poultry and put a serious burden on the poultry industry of the Republic of Korea. Effective control measures considering spatial heterogeneity to mitigate the HPAI epidemic is still a challenging issue. Here we develop a spatial-temporal compartmental model that incorporates the culling rate as a function of the reported farms and farm density in each town. The epidemiological and geographical data of two species, chickens and ducks, from the farms in the sixteen towns in Eumseong-gun and Jincheon-gun are used to find the best-fitted parameters of the metapopulation model. The best culling radius to maximize the final size of the susceptible farms and minimize the total number of culled farms is calculated from the model. The local reproductive number using the next generation method is calculated as an indicator of virus transmission in a given area. Simulation results indicate that this parameter is strongly influenced not only by epidemiological factors such as transmissibility and/or susceptibility of poultry species but also by geographical and demographical factors such as the distribution of poultry farms (or density) and connectivity (or distance) between farms. Based on this result, we suggest the best culling radius with respect to the local reproductive number in a targeted area.
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Affiliation(s)
- Jonggul Lee
- National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Youngsuk Ko
- Mathematic department, Konkuk University, Seoul, Republic of Korea
| | - Eunok Jung
- Mathematic department, Konkuk University, Seoul, Republic of Korea
- * E-mail:
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