1
|
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.
Collapse
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
| |
Collapse
|
2
|
Bucini G, Clark EM, Merrill SC, Langle-Chimal O, Zia A, Koliba C, Cheney N, Wiltshire S, Trinity L, Smith JM. Connecting livestock disease dynamics to human learning and biosecurity decisions. Front Vet Sci 2023; 9:1067364. [PMID: 36744225 PMCID: PMC9896627 DOI: 10.3389/fvets.2022.1067364] [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: 10/11/2022] [Accepted: 12/20/2022] [Indexed: 01/21/2023] Open
Abstract
The acceleration of animal disease spread worldwide due to increased animal, feed, and human movement has driven a growing body of epidemiological research as well as a deeper interest in human behavioral studies aimed at understanding their interconnectedness. Biosecurity measures can reduce the risk of infection, but human risk tolerance can hinder biosecurity investments and compliance. Humans may learn from hardship and become more risk averse, but sometimes they instead become more risk tolerant because they forget negative experiences happened in the past or because they come to believe they are immune. We represent the complexity of the hog production system with disease threats, human decision making, and human risk attitude using an agent-based model. Our objective is to explore the role of risk tolerant behaviors and the consequences of delayed biosecurity investments. We set up experiment with Monte Carlo simulations of scenarios designed with different risk tolerance amongst the swine producers and we derive distributions and trends of biosecurity and porcine epidemic diarrhea virus (PEDv) incidence emerging in the system. The output data allowed us to examine interactions between modes of risk tolerance and timings of biosecurity response discussing consequences for disease protection in the production system. The results show that hasty and delayed biosecurity responses or slow shifts toward a biosecure culture do not guarantee control of contamination when the disease has already spread in the system. In an effort to support effective disease prevention, our model results can inform policy making to move toward more resilient and healthy production systems. The modeled dynamics of risk attitude have also the potential to improve communication strategies for nudging and establishing risk averse behaviors thereby equipping the production system in case of foreign disease incursions.
Collapse
Affiliation(s)
- Gabriela Bucini
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States,Social-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States,*Correspondence: Gabriela Bucini ✉
| | - Eric M. Clark
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States,Social-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States
| | - Scott C. Merrill
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States,Social-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States
| | - Ollin Langle-Chimal
- Department of Computer Science, University of Vermont, Burlington, VT, United States
| | - Asim Zia
- Social-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States,Department of Computer Science, University of Vermont, Burlington, VT, United States,Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Christopher Koliba
- Social-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States,Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Nick Cheney
- Department of Computer Science, University of Vermont, Burlington, VT, United States
| | - Serge Wiltshire
- Social-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States,Food Systems Research Center, University of Vermont, Burlington, VT, United States
| | - Luke Trinity
- Social-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States,Computational Biology Research and Analytics Lab, University of Victoria, Victoria, BC, Canada
| | - Julia M. Smith
- Department of Animal and Veterinary Sciences, University of Vermont, Burlington, VT, United States
| |
Collapse
|
3
|
Kaniyamattam K, Tedeschi LO. ASAS-NANP symposium: mathematical modeling in animal nutrition: agent-based modeling for livestock systems: the mechanics of development and application. J Anim Sci 2023; 101:skad321. [PMID: 37997925 PMCID: PMC10664392 DOI: 10.1093/jas/skad321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/30/2023] [Indexed: 11/25/2023] Open
Abstract
Over the last three decades, agent-based modeling/model (ABM) has been one of the most powerful and valuable simulation-based decision modeling techniques used to study the complex dynamic interactions between animals and their environment. ABM is a relatively new modeling technique in the animal research arena, with immense potential for routine decision-making in livestock systems. We describe ABM's fundamental characteristics for developing intelligent modeling systems, exemplify its use for livestock production, and describe commonly used software for designing and developing ABM. After that, we discuss several aspects of the developmental mechanics of an ABM, including (1) how livestock researchers can conceptualize and design a model, (2) the main components of an ABM, (3) different statistical methods of analyzing the outputs, and (4) verification, validation, and replication of an ABM. Then, we perform an overall analysis of the utilities of ABM in different subsystems of the livestock systems ranging from epidemiological prediction to nutritional management to livestock market dynamics. Finally, we discuss the concept of hybrid intelligent models (i.e., merging real-time data streams with intelligent ABM), which have applications in artificial intelligence-based decision-making for precision livestock farming. ABM captures individual agents' characteristics, interactions, and the emergent properties that arise from these interactions; thus, animal scientists can benefit from ABM in multiple ways, including understanding system-level outcomes, analyzing agent behaviors, exploring different scenarios, and evaluating policy interventions. Several platforms for building ABM exist (e.g., NetLogo, Repast J, and AnyLogic), but they have unique features making one more suitable for solving specific problems. The strengths of ABM can be combined with other modeling approaches, including artificial intelligence, allowing researchers to advance our understanding further and contribute to sustainable livestock management practices. There are many ways to develop and apply mathematical models in livestock production that might assist with sustainable development. However, users must be experienced when choosing the appropriate modeling technique and computer platform (i.e., modeling development tool) that will facilitate the adoption of mathematical models by certifying that the model is field-ready and versatile enough for untrained users.
Collapse
Affiliation(s)
- Karun Kaniyamattam
- Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
| | - Luis O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
| |
Collapse
|
4
|
Koliba C, Merrill SC, Zia A, Bucini G, Clark E, Shrum TR, Wiltshire S, Smith JM. Assessing strategic, tactical, and operational decision-making and risk in a livestock production chain through experimental simulation platforms. Front Vet Sci 2022; 9:962788. [PMID: 36337194 PMCID: PMC9634728 DOI: 10.3389/fvets.2022.962788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/23/2022] [Indexed: 12/04/2022] Open
Abstract
This paper provides a research summary of a series of serious games and simulations that form the basis of an experimental platform for the study of human decision-making and behavior associated with biosecurity across complex livestock production chains. This platform is the first of its kind to address the challenges associated with scaling micro-behavior of biosecurity decision-making to macro-patterns of disease spread across strategic, tactical and operational levels, capturing the roles that facility managers and front-line workers play in making biosecurity decisions under risk and uncertainty. Informational and incentive treatments are tested within each game and simulation. Behavioral theories are used to explain these findings. Results from serious games in the form of behavioral probability distributions are then used to simulate disease incidence and spread across a complex production chain, demonstrating how micro-level behaviors contribute to larger macro-level patterns. In the case of this study, the propensity to adopt micro-level biosecurity practices are applied to a network percolation disease spread model. By presenting the suite of companion models of behavior and disease spread we are able to capture scaling dynamics of complex systems, and in the process, better understand how individual behaviors impact whole systems.
Collapse
Affiliation(s)
- Christopher Koliba
- Social Ecological Gaming and Simulation Lab, School of Public Administration and Affairs, University of Kansas, Lawrence, KS, United States
- *Correspondence: Christopher Koliba
| | - Scott C. Merrill
- Social Ecological Gaming and Simulation (SEGS) Lab, Plant and Soil Science Department, Gund Institute for Environment, University of Vermont, Burlington, VT, United States
| | - Asim Zia
- Social Ecological Gaming and Simulation Lab, School of Public Administration and Affairs, University of Kansas, Lawrence, KS, United States
| | - Gabriela Bucini
- Social Ecological Gaming and Simulation (SEGS) Lab, Plant and Soil Science Department, University of Vermont, Burlington, VT, United States
| | - Eric Clark
- Social Ecological Gaming and Simulation (SEGS) Lab, Plant and Soil Science Department, University of Vermont, Burlington, VT, United States
| | - Trisha R. Shrum
- Social Ecological Gaming and Simulation (SEGS) Lab, Community Development & Applied Economics Department, University of Vermont, Burlington, VT, United States
| | - Serge Wiltshire
- Plant Biology Department, Food Systems Research Center, University of Vermont, Burlington, VT, United States
| | - Julia M. Smith
- Social Ecological Gaming and Simulation (SEGS) Lab, Animal and Veterinary Sciences Department, University of Vermont, Burlington, VT, United States
| |
Collapse
|
5
|
Hadaj P, Strzałka D, Nowak M, Łatka M, Dymora P. The use of PLANS and NetworkX in modeling power grid system failures. Sci Rep 2022; 12:17445. [PMID: 36261496 PMCID: PMC9581963 DOI: 10.1038/s41598-022-22268-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022] Open
Abstract
The theoretical and practical aspects and results of simulations based on a specialized tool that is used in the energy industry were adressed. The previously discussed cases in the literature by taking into account the worst case and critical states of networks in terms of complex networks were extended. Using the Monte-Carlo method, the vulnerability of the power grid to node failures was investigated, both in terms of the use of specialized software, which is used in the power industry, and a tool for the analysis of complex networks graphs. We present the results obtained and the observed analogy between the results of the analysis performed in specialized software and the complex network graph analysis tool. It has been shown that the results obtained coincide for both software packages, even though their application focuses on slightly different aspects of system operation. Moreover, further possibilities of extending the research in this direction are proposed, taking into account not only the improvement of the method used, but also a significant increase in the size of the tested structure model.
Collapse
Affiliation(s)
- Piotr Hadaj
- Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Al. Powstańców Warszawy 12, 35-959, Rzeszów, Poland.
| | - Dominik Strzałka
- Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Al. Powstańców Warszawy 12, 35-959, Rzeszów, Poland
| | - Marek Nowak
- Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Al. Powstańców Warszawy 12, 35-959, Rzeszów, Poland
| | - Małgorzata Łatka
- Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Al. Powstańców Warszawy 12, 35-959, Rzeszów, Poland
| | - Paweł Dymora
- Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Al. Powstańców Warszawy 12, 35-959, Rzeszów, Poland
| |
Collapse
|
6
|
Galvis JA, Corzo CA, Machado G. Modelling and assessing additional transmission routes for porcine reproductive and respiratory syndrome virus: Vehicle movements and feed ingredients. Transbound Emerg Dis 2022; 69:e1549-e1560. [PMID: 35188711 PMCID: PMC9790477 DOI: 10.1111/tbed.14488] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/02/2022] [Accepted: 02/13/2022] [Indexed: 12/30/2022]
Abstract
Accounting for multiple modes of livestock disease dissemination in epidemiological models remains a challenge. We developed and calibrated a mathematical model for transmission of porcine reproductive and respiratory syndrome virus (PRRSV), tailored to fit nine modes of between-farm transmission pathways including: farm-to-farm proximity (local transmission), contact network of batches of pigs transferred between farms (pig movements), re-break probabilities for farms with previous PRRSV outbreaks, with the addition of four different contact networks of transportation vehicles (vehicles to transport pigs to farms, pigs to markets, feed and crew) and the amount of animal by-products within feed ingredients (e.g., animal fat or meat and bone meal). The model was calibrated on weekly PRRSV outbreaks data. We assessed the role of each transmission pathway considering the dynamics of specific types of production (i.e., sow, nursery). Although our results estimated that the networks formed by transportation vehicles were more densely connected than the network of pigs transported between farms, pig movements and farm proximity were the main PRRSV transmission routes regardless of farm types. Among the four vehicle networks, vehicles transporting pigs to farms explained a large proportion of infections, sow = 20.9%; nursery = 15%; and finisher = 20.6%. The animal by-products showed a limited association with PRRSV outbreaks through descriptive analysis, and our model results showed that the contribution of animal fat contributed only 2.5% and meat and bone meal only .03% of the infected sow farms. Our work demonstrated the contribution of multiple routes of PRRSV dissemination, which has not been deeply explored before. It also provides strong evidence to support the need for cautious, measured PRRSV control strategies for transportation vehicles and further research for feed by-products modelling. Finally, this study provides valuable information and opportunities for the swine industry to focus effort on the most relevant modes of PRRSV between-farm transmission.
Collapse
Affiliation(s)
- Jason A. Galvis
- Department of Population Health and PathobiologyCollege of Veterinary MedicineNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | - Cesar A. Corzo
- Veterinary Population Medicine DepartmentCollege of Veterinary MedicineUniversity of MinnesotaSt PaulMinnesotaUSA
| | - Gustavo Machado
- Department of Population Health and PathobiologyCollege of Veterinary MedicineNorth Carolina State UniversityRaleighNorth CarolinaUSA
| |
Collapse
|
7
|
Gray H, Friel M, Goold C, Smith RP, Williamson SM, Collins LM. Modelling the links between farm characteristics, respiratory health and pig production traits. Sci Rep 2021; 11:13789. [PMID: 34215759 PMCID: PMC8253804 DOI: 10.1038/s41598-021-93027-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 06/08/2021] [Indexed: 11/10/2022] Open
Abstract
Sustainable livestock production requires links between farm characteristics, animal performance and animal health to be recognised and understood. In the pig industry, respiratory disease is prevalent, and has negative health, welfare and economic consequences. We used national-level carcass inspection data from the Food Standards Agency to identify associations between pig respiratory disease, farm characteristics (housing type and number of source farms), and pig performance (mortality, average daily weight gain, back fat and carcass weight) from 49 all in/all out grow-to-finish farms. We took a confirmatory approach by pre-registering our hypotheses and used Bayesian multi-level modelling to quantify the uncertainty in our estimates. The study findings showed that acquiring growing pigs from multiple sources was associated with higher respiratory condition prevalence. Higher prevalence of respiratory conditions was linked with higher mortality, and lower average daily weight gain, back fat and pig carcass weight. Our results support previous literature using a range of data sources. In conclusion, we find that meat inspection data are more valuable at a finer resolution than has been previously indicated and could be a useful tool in monitoring batch-level pig health in the future.
Collapse
Affiliation(s)
- H Gray
- School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - M Friel
- School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - C Goold
- School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - R P Smith
- Animal and Plant Health Agency (APHA), Weybridge, UK
| | | | - L M Collins
- School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK.
| |
Collapse
|
8
|
Simulating human behavioral changes in livestock production systems during an epidemic: The case of the US beef cattle industry. PLoS One 2021; 16:e0253498. [PMID: 34166451 PMCID: PMC8224970 DOI: 10.1371/journal.pone.0253498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/06/2021] [Indexed: 11/19/2022] Open
Abstract
Human behavioral change around biosecurity in response to increased awareness of disease risks is a critical factor in modeling animal disease dynamics. Here, biosecurity is referred to as implementing control measures to decrease the chance of animal disease spreading. However, social dynamics are largely ignored in traditional livestock disease models. Not accounting for these dynamics may lead to substantial bias in the predicted epidemic trajectory. In this research, an agent-based model is developed by integrating the human decision-making process into epidemiological processes. We simulate human behavioral change on biosecurity practices following an increase in the regional disease incidence. We apply the model to beef cattle production systems in southwest Kansas, United States, to examine the impact of human behavior factors on a hypothetical foot-and-mouth disease outbreak. The simulation results indicate that heterogeneity of individuals regarding risk attitudes significantly affects the epidemic dynamics, and human-behavior factors need to be considered for improved epidemic forecasting. With the same initial biosecurity status, increasing the percentage of risk-averse producers in the total population using a targeted strategy can more effectively reduce the number of infected producer locations and cattle losses compared to a random strategy. In addition, the reduction in epidemic size caused by the shifting of producers' risk attitudes towards risk-aversion is heavily dependent on the initial biosecurity level. A comprehensive investigation of the initial biosecurity status is recommended to inform risk communication strategy design.
Collapse
|
9
|
Clark EM, Merrill SC, Trinity L, Bucini G, Cheney N, Langle-Chimal O, Shrum T, Koliba C, Zia A, Smith JM. Emulating Agricultural Disease Management: Comparing Risk Preferences Between Industry Professionals and Online Participants Using Experimental Gaming Simulations and Paired Lottery Choice Surveys. Front Vet Sci 2021; 7:556668. [PMID: 33537351 PMCID: PMC7848213 DOI: 10.3389/fvets.2020.556668] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 12/18/2020] [Indexed: 11/13/2022] Open
Abstract
Mitigating the spread of disease is crucial for the well-being of agricultural production systems. Implementing biosecurity disease prevention measures can be expensive, so producers must balance the costs of biosecurity investments with the expected benefits of reducing the risk of infections. To investigate the risk associated with this decision making process, we developed an online experimental game that simulates biosecurity investment allocation of a pork production facility during an outbreak. Participants are presented with several scenarios that vary the visibility of the disease status and biosecurity protection implemented at neighboring facilities. Certain rounds allowed participants to spend resources to reduce uncertainty and reveal neighboring biosecurity and/or disease status. We then test how this uncertainty affects the decisions to spend simulation dollars to increase biosecurity and reduce risk. We recruited 50 attendees from the 2018 World Pork Expo to participate in our simulation. We compared their performance to an opportunity sample of 50 online participants from the survey crowdsourcing tool, Amazon Mechanical Turk (MTurk). With respect to biosecurity investment, we did not find a significant difference between the risk behaviors of industry professionals and those of MTurk participants for each set of experimental scenarios. Notably, we found that our sample of industry professionals opted to pay to reveal disease and biosecurity information more often than MTurk participants. However, the biosecurity investment decisions were not significantly different during rounds in which additional information could be purchased. To further validate these findings, we compared the risk associated with each group's responses using a well-established risk assessment survey implementing paired lottery choices. Interestingly, we did not find a correlation in risk quantified with simulated biosecurity investment in comparison to the paired lottery choice survey. This may be evidence that general economic risk preferences may not always translate into simulated behavioral risk, perhaps due to the contextual immersion provided by experimental gaming simulations. Online recruitment tools can provide cost effective research quality data that can be rapidly assembled in comparison to industry professionals, who may be more challenging to sample at scale. Using a convenience sample of industry professionals for validation can also provide additional insights into the decision making process. These findings lend support to using online experimental simulations for interpreting risk associated with a complex decision mechanism.
Collapse
Affiliation(s)
- Eric M Clark
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States
| | - Scott C Merrill
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States.,Gund Institute for Environment, University of Vermont, Burlington, VT, United States
| | - Luke Trinity
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Gabriela Bucini
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States
| | - Nicholas Cheney
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Department of Computer Science, University of Vermont, Burlington, VT, United States
| | - Ollin Langle-Chimal
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Trisha Shrum
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Christopher Koliba
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Gund Institute for Environment, University of Vermont, Burlington, VT, United States.,Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Asim Zia
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Gund Institute for Environment, University of Vermont, Burlington, VT, United States.,Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Julia M Smith
- Social Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.,Department of Animal and Veterinary Sciences, University of Vermont, Burlington, VT, United States
| |
Collapse
|
10
|
Yang Q, Gruenbacher DM, Heier Stamm JL, Amrine DE, Brase GL, DeLoach SA, Scoglio CM. Impact of truck contamination and information sharing on foot-and-mouth disease spreading in beef cattle production systems. PLoS One 2020; 15:e0240819. [PMID: 33064750 PMCID: PMC7567383 DOI: 10.1371/journal.pone.0240819] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 10/04/2020] [Indexed: 11/18/2022] Open
Abstract
As cattle movement data in the United States are scarce due to the absence of mandatory traceability programs, previous epidemic models for U.S. cattle production systems heavily rely on contact rates estimated based on expert opinions and survey data. These models are often based on static networks and ignore the sequence of movement, possibly overestimating the epidemic sizes. In this research, we adapt and employ an agent-based model that simulates beef cattle production and transportation in southwest Kansas to analyze the between-premises transmission of a highly contagious disease, foot-and-mouth disease. First, we assess the impact of truck contamination on the disease transmission with the truck agent following an independent clean-infected-clean cycle. Second, we add an information-sharing functionality such that producers/packers can trace back and forward their trade records to inform their trade partners during outbreaks. Scenario analysis results show that including indirect contact routes between premises via truck movements can significantly increase the amplitude of disease spread, compared with equivalent scenarios that only consider animal movement. Mitigation strategies informed by information sharing can effectively mitigate epidemics, highlighting the benefit of promoting information sharing in the cattle industry. In addition, we identify salient characteristics that must be considered when designing an information-sharing strategy, including the number of days to trace back and forward in the trade records and the role of different cattle supply chain stakeholders. Sensitivity analysis results show that epidemic sizes are sensitive to variations in parameters of the contamination period for a truck or a loading/unloading area of premises, and indirect contact transmission probability and future studies can focus on a more accurate estimation of these parameters.
Collapse
Affiliation(s)
- Qihui Yang
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America
- * E-mail:
| | - Don M. Gruenbacher
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America
| | - Jessica L. Heier Stamm
- Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, KS, United States of America
| | - David E. Amrine
- Beef Cattle Institute, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States of America
| | - Gary L. Brase
- Department of Psychological Sciences, Kansas State University, Manhattan, KS, United States of America
| | - Scott A. DeLoach
- Department of Computer Science, Kansas State University, Manhattan, KS, United States of America
| | - Caterina M. Scoglio
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, United States of America
| |
Collapse
|
11
|
Using experimental gaming simulations to elicit risk mitigation behavioral strategies for agricultural disease management. PLoS One 2020; 15:e0228983. [PMID: 32182247 PMCID: PMC7077803 DOI: 10.1371/journal.pone.0228983] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 01/16/2020] [Indexed: 12/27/2022] Open
Abstract
Failing to mitigate propagation of disease spread can result in dire economic consequences for agricultural networks. Pathogens like Porcine Epidemic Diarrhea virus, can quickly spread among producers. Biosecurity is designed to prevent infection transmission. When considering biosecurity investments, management must balance the cost of protection versus the consequences of contracting an infection. Thus, an examination of the decision making processes associated with investment in biosecurity is important for enhancing system wide biosecurity. Data gathered from experimental gaming simulations can provide insights into behavioral strategies and inform the development of decision support systems. We created an online digital experiment to simulate outbreak scenarios among swine production supply chains, where participants were tasked with making biosecurity investment decisions. In Experiment One, we quantified the risk associated with each participant's decisions and delineated three dominant categories of risk attitudes: risk averse, risk tolerant, and opportunistic. Each risk class exhibited unique approaches in reaction to risk and disease information. We also tested how information uncertainty affects risk aversion, by varying the amount of visibility of the infection as well as the amount of biosecurity implemented across the system. We found evidence that more visibility in the number of infected sites increases risk averse behaviors, while more visibility in the amount of neighboring biosecurity increased risk taking behaviors. In Experiment Two, we were surprised to find no evidence for differences in behavior of livestock specialists compared to Amazon Mechanical Turk participants. Our findings provide support for using experimental gaming simulations to study how risk communication affects behavior, which can provide insights towards more effective messaging strategies.
Collapse
|
12
|
Bucini G, Merrill SC, Clark E, Moegenburg SM, Zia A, Koliba CJ, Wiltshire S, Trinity L, Smith JM. Risk Attitudes Affect Livestock Biosecurity Decisions With Ramifications for Disease Control in a Simulated Production System. Front Vet Sci 2019; 6:196. [PMID: 31294037 PMCID: PMC6604760 DOI: 10.3389/fvets.2019.00196] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 05/31/2019] [Indexed: 01/10/2023] Open
Abstract
Hog producers' operational decisions can be informed by an awareness of risks associated with emergent and endemic diseases. Outbreaks of porcine epidemic diarrhea virus (PEDv) have been re-occurring every year since the first onset in 2013 with substantial losses across the hog production supply chain. Interestingly, a decreasing trend in PEDv incidence is visible. We assert that changes in human behaviors may underlie this trend. Disease prevention using biosecurity practices is used to minimize risk of infection but its efficacy is conditional on human behavior and risk attitude. Standard epidemiological models bring important insights into disease dynamics but have limited predictive ability. Since research shows that human behavior plays a driving role in the disease spread process, the explicit inclusion of human behavior into models adds an important dimension to understanding disease spread. Here we analyze PEDv incidence emerging from an agent-based model (ABM) that uses both epidemiological dynamics and algorithms that incorporate heterogeneous human decisions. We investigate the effects of shifting fractions of hog producers between risk tolerant and risk averse positions. These shifts affect the dynamics describing willingness to increase biosecurity as a response to disease threats and, indirectly, change infection probabilities and the resultant intensity and impact of the disease outbreak. Our ABM generates empirically verifiable patterns of PEDv transmission. Scenario results show that relatively small shifts (10% of the producer agents) toward a risk averse position can lead to a significant decrease in total incidence. For significantly steeper decreases in disease incidence, the model's hog producer population needed at least 37.5% of risk averse. Our study provides insight into the link between risk attitude, decisions related to biosecurity, and consequent spread of disease within a livestock production system. We suggest that it is possible to create positive, lasting changes in animal health by nudging the population of livestock producers toward more risk averse behaviors. We make a case for integrating social and epidemiological aspects in disease spread models to test intervention strategies intended to improve biosecurity and animal health at the system scale.
Collapse
Affiliation(s)
- Gabriela Bucini
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States
| | - Scott C. Merrill
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States
| | - Eric Clark
- The Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Susan M. Moegenburg
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States
| | - Asim Zia
- Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Christopher J. Koliba
- Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Serge Wiltshire
- Department of Food Systems, University of Vermont, Burlington, VT, United States
| | - Luke Trinity
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT, United States
| | - Julia M. Smith
- Department of Animal and Veterinary Sciences, University of Vermont, Burlington, VT, United States
| |
Collapse
|
13
|
Merrill SC, Moegenburg S, Koliba CJ, Zia A, Trinity L, Clark E, Bucini G, Wiltshire S, Sellnow T, Sellnow D, Smith JM. Willingness to Comply With Biosecurity in Livestock Facilities: Evidence From Experimental Simulations. Front Vet Sci 2019; 6:156. [PMID: 31214603 PMCID: PMC6558082 DOI: 10.3389/fvets.2019.00156] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 05/07/2019] [Indexed: 12/15/2022] Open
Abstract
Disease in U.S. animal livestock industries annually costs over a billion dollars. Adoption and compliance with biosecurity practices is necessary to successfully reduce the risk of disease introduction or spread. Yet, a variety of human behaviors, such as the urge to minimize time costs, may induce non-compliance with biosecurity practices. Utilizing a “serious gaming” approach, we examine how information about infection risk impacts compliance with biosecurity practices. We sought to understand how simulated environments affected compliance behavior with treatments that varied using three factors: (1) the risk of acquiring an infection, (2) the delivery method of the infection risk message (numerical, linguistic and graphical), and (3) the certainty of the infection risk information. Here we show that compliance is influenced by message delivery methodology, with numeric, linguistic, and graphical messages showing increasing efficacy, respectively. Moreover, increased situational uncertainty and increased risk were correlated with increases in compliance behavior. These results provide insight toward developing messages that are more effective and provide tools that will allow managers of livestock facilities and policy makers to nudge behavior toward more disease resilient systems via greater compliance with biosecurity practices.
Collapse
Affiliation(s)
- Scott C Merrill
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States
| | - Susan Moegenburg
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States
| | - Christopher J Koliba
- Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Asim Zia
- Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States
| | - Luke Trinity
- Department of Mathematics & Statistics, University of Vermont, Burlington, VT, United States
| | - Eric Clark
- The Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States
| | - Gabriela Bucini
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States
| | - Serge Wiltshire
- Department of Food Systems, University of Vermont, Burlington, VT, United States
| | - Timothy Sellnow
- Nicholson School of Communication and Media, University of Central Florida, Orlando, FL, United States
| | - Deanna Sellnow
- Nicholson School of Communication and Media, University of Central Florida, Orlando, FL, United States
| | - Julia M Smith
- Department of Animal and Veterinary Sciences, University of Vermont, Burlington, VT, United States
| |
Collapse
|