Progress 09/01/22 to 08/31/23
Outputs
Target Audience:Scientists, modelers, and officials interested in obtaining a better understanding of how risk associated with infectious diseases are affected by the structure of the livestock industry and the behaviors of those involved. Further, the following stakeholders are informed by the outputs and outcomes of this project: veterinary epidemiologists, state animal health officials, and livestock industry professionals. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Four graduate students and two undergraduate students are being trained in animal disease transmission modeling, including InterSpread Plus, ABM, as well as experimental gaming and survey data collection procedures. One undergraduate thesis will focus on the experimental game data analysis. Another undergraduate's thesis is focusing on the routing aspect of trucks between farms. The intent is to better understand how disease spreads via trucks. An enterprise license for InterspreadPlus has been procured for UVM. A virtual machine running Windows has been provisioned for use by project members who do not have direct access to a Windows platform computer. A week long immersive training workshop for the InterSpreadPlus modeling engine has been scheduled for year 3 of this project. One graduate student was sent as a representative of our team to the World Pork Expo in Des Moines, Iowa to learn more about swine production in the U.S. and to explore some potential new data sharing agreements and collaborations for model development. As a more senior graduate student, this was a learning experience in the professionalism and networking in science that is only experienced as one graduates to a more independent scientist. Additionally this graduate student arranged and went on a personal tour of a swine farm in Ohio to talk about biosecurity and see the system in action. Three graduate students have submitted their papers as lead authors for presentation at CRWAD 2024 annual conference and plan to participate and present in the forthcoming meeting. How have the results been disseminated to communities of interest?In addition to publication of journal articles and conference presentations, we have continued to interact with our advisory board members. The advisory board members include representatives from USDA APHIS, National Pork Bureau, Livestock Industry and senior academics. Two scientists of USDA APHIS participate in our weekly project meetings. Through these interactions, our team presents the latest research on the ABM, surveys and experimental games to our target audiences. Our meetings with scientists and policy makers have also served the purpose of disseminating information about ABM, games and surveys to the community of veterinary scientists who study the individual parts of our system models. These experts we have worked with have expressed interest in our model and have discussed it with their respective colleagues. In addition to the slate of system experts discussed above with which we had more formal meetings, the appearance of our PhD researcher, Samuel F. Rosenblatt at the World Pork Expo in June served to disseminate information about our project to a much wider audience of stakeholders and operatives in the swine industry through informal meetings at the expo. Our second PhD student, Richmond Baye, was invited to present at a high-level conference of agricultural policy makers in Washington DC. Another initiative to promote future result dissemination was with UVMs open-source resource office VERSO (VErmont ReSearch OSPO). We have begun discussions that will guide us to create this as an open source product in the future while still protecting the private data we have been entrusted in our data sharing agreements, and have the VERSO program director Kendall Fortney to help us do this. What do you plan to do during the next reporting period to accomplish the goals?In years 3 and 4 of our project, we will continue to develop and test the national scale ABM for simulating ASF and FMD and incorporate policy and behavioral responses from the latest survey and gaming data sets. The InterspreadPlus (ISP) model will also be analyzed for a global parameter sensitivity analysis to provide a baseline for comparing the ABM simulation results. In the ABM, our implementation of feed mills and their truck routes is expanding to include data on registered medicated feed mills in the US, with their locations publicly available. These routes are being constructed systematically using all available knowledge on the heterogeneity of routing practices gathered from expert interviews, open resources, and academic literature. Special focus is being paid on feed routing and swine shipments as they offer potentially a cost-neutral way of increasing biosecurity for the US system. Swine shipment networks are being implemented building off data-informed inference models developed by researchers at the University of Colorado, Linköping University, and The University of Warwick. However this requires data sharing agreements that will be finalized with relevant data owners. For the development process of the ABM, the implementation of a sequential disease model has begun, whereby the same model will take on new parameters and an additional set of logic to go with ASF at a given introduction time. Finally, there are several aspects of the ABM which we deem as important to the simulation of the system and have begun preliminary research for but have yet to begin implementation. Currently there are several aspects of the model which are probabilistically affected by a producer agent's "biosecurity-level", a single variable meant to represent probability of infection and spread for many types of interaction, but also their likelihood of reacting to events and new information in a way that promotes biosecurity for the system (such as calling a vet). We plan to implement this as three different variables which collectively represent biosecurity. One signaling long term investment in biosecurity, such as installation of air filters in barns, which decreases probability of infection and infectiousness in a certain set of interaction types and which does not decrease over time due to social distancing, another representing short term biosecurity investment which does decrease over time, and a third representing the probability of reacting to events and information. We also are planning on implementing airborne transmission and seasonality of disease dynamics. Airborne transmission will follow a similar network-based approach as the other interactions in that a pre-determined list of neighboring farms within a given cutoff radius will be read into the model at initiation along with their distances. Infections from airborne transmission will be limited to these interactions, rather than following a spatial dilution model or similar. Seasonality is important to incorporate as we begin data calibration from MSHMP, as seasonality currently is seen in disease trends which will make calibration difficult without it. Generally we plan to approach seasonality as changing epidemiological parameters slightly according to a schedule. As we add these additional features and expand our model to represent the national scale, new bottlenecks in the computation will arise. We will be utilizing existing runtime testing techniques and developing our own in order to identify and address them. We have also designed and scheduled the launch of the second choice experiment survey from January 2024 to March 2024. From the survey, we will develop 2 additional papers focusing on a discrete choice experiment and an extended integrated theory of planned behavior. We plan for continued development of the experimental game to incorporate more features into the simulated decision mechanism. Participant recruitment using online survey platforms will be utilized to collect behavioral data to inform the agent based modeling initiative. After the initial alpha test is completed, data will be analyzed within the team in order to scope additional updates to the experimental simulation design, before we scale up our participant recruitment phase. We plan to recruit upwards of 1000 participants for the data collection phase of our experimental game. Behavioral results from collected gaming data will be analyzed and prepared for publication in journal articles. Machine learning models will be developed for analyzing learning from the experimental game, testing study hypotheses and integration into the ABM. The biosecurity adoption strategies developed by learning agents will be compared under different policy conditions. Additional experiments will compare agents with different lengths of memory, access to different information, and more or less discounting of future rewards. These results can be compared with data from experimental games and surveys to determine the verisimilitude of learned agent strategies. Findings from the ABM experimental simulations, games and surveys will be published in journal articles, conferences as well as policy briefs.
Impacts
What was accomplished under these goals?
For objective 1, design and test an ABM, the research team has continued to make significant progress in developing a national scale ABM. Three papers from this work have been accepted for presentation at CRWAD 2024 annual conference. Below, we outline specific progress and details in the development of this ABM. The general approach of our participatory ABM is trifold: First, we model the endemic disease PRRSV (Porcine Reproductive and Respiratory Syndrome) as a proxy for ASF (African Swine Fever) in the swine industry to calibrate the ABM with data gathered from multiple sources including incidence data from the Morison Swine Health Morrison Swine Health Monitoring Program. Next, we alter the model to simulate potential interventions, policy changes, and incentive structures and use agent learning informed by experimental game data to equilibrate the model to these changes. Once calibrated with PRRS data and equilibrated to pre-emptive system-wide biosecurity measures, we introduce ASF to the model to see how the adaptations the system has made to the pre-emptive system changes influences the ability of the United States to resist an ASF outbreak. The current iteration of the ABM is written in C++ using the GPU-accelerated FLAME-GPU library with Python used for additional external features. Use of our model for wide parameter sweeps is facilitated through runtime parameterization and CUDA (Compute Unified Device Architecture) processes. This language and library offer high processing speed by utilizing parallelization of agents, but little debugging capability. As such we have implemented our own debugging and output procedures to accelerate model development via output analysis using Python. Python is also used for external interfacing to and sideloading of additional features. We use Python to create a set of pre-made synthetic feedmill truck routes which we load into the model to avoid the computational cost of routing procedures in real time, and to explore more fully the effect of these routes. Reinforcement learning for ABM producer agents was developed in Python alongside the open-source C++ model. Initial agents trained using Q-Learning were shown to develop behavioral strategies similar to hand-designed strategies based on survey and experimental game data. Additional agents trained using Deep Q-Learning demonstrated less future reward discounting and learned strategies that more efficiently invested in biosecurity. Implemented advances of our model compared to prior livestock epidemiology models include modeling the base unit of infection and sale as batches of swine of the same age on a farm which can have multiple batches. Modeling the infection dynamics, transfer and sale of batches of pigs provides a meso-scale metapopulation approach allowing for realistic within-farm transmission dynamics, such as sustained infection which is characteristic of PRRS in the US in recent years, and realistic economic modeling of sales without the prohibitive computational cost of modeling individual swine. The agent-based model we are constructing requires in-depth knowledge of a highly complex and heterogenous system of swine production and commerce in the U.S. In order to bridge the gap between the technical expertise and background knowledge, we have engaged with experts from across the industry in a series of informal interviews and collaborations. Specifically, outside of the expertise of our core team at UVM, we have had a total of eight 1-2 hour interviews with experts in swine production and biosecurity from feedmills to veterinary practice: Dr. Scott Dee, Dr. Jordan Gebhart, Dr. Jason Woodworth, Dr. Lisa Becton, Dr. Stefan Sellman, Dr. Lindsay Beck-Johnson These interviews have informed many aspects of the model which are seemingly small details but where typical assumptions are not viable and can make impactful differences on models which do not include these details. For example we have developed a simulated "pooled testing" procedure with sensitivity and specificity of simulated tests determined according to the number of infectious swine and tested swine on a given premises. Similarly, the heterogeneity of when vet testing occurs and how this information is shared with interested parties is informed by these meetings and implemented. For objective 2, we have made significant progress in designing and implementing a survey and gaming experiments. We designed and implemented a survey tailored to infer heterogenous behaviors among swine producers in the US. We have analyzed the collected data resulting in the development of two scientific papers that are under peer review in high impact journals such as Nature Scientific Reports and Preventive Veterinary Medicine. We have also presented the findings at 3 major international conferences to a diverse audience including ICAS-IX and CRWAD. An advanced experimental game has been in active development using Unity software and coded in C#. This experiment is structured as an outbreak simulation with analogous mechanics to the ABM. This experimental game includes a spatial distribution of configurable animal diseases, allowing the user to make decisions based upon risk related to infection transfer between commercial systems. Supply chains with commercial entities allow for a generalizable disease spread simulation. Diseases and their corresponding epidemiological parameters, like transmission probability, can be instantiated within the simulation and then transferred via explicit through the supply chain's trucking routes to facilities in the spatially explicit system. The trucking routes that connect the producers are configurable along with their capacity to spread disease between commercial systems. A full user interface was developed to inform the player of the infectious disease risk along with a series of policy treatments such as government indemnity policies to test their influence on decision-making. Additional stochasticity is injected into the decision mechanism in the form of infection reports by other simulated farms not controlled by the player. As disease percolates throughout the supply chain, non-player farms can report the presence of an infectious disease, which is clearly rendered on the user interface. The disease reports can contain false positives, which provide additional uncertainty within the simulation based experimental game. The player weighs the risk with respect to spatial progression of the disease spread and can choose to invest their simulation dollars on biosecurity investment, which reduces disease transmission probability. The player must balance the risk of disease and the cost of biosecurity investment, with the added complexity of the government policy interventions for indemnifying culled animals. Beta testing of this experimental game inclusive of full behavioral and network data collection has been completed with participants recruited from Amazon Mechanical Turk's online crowdsourcing worker platform. Data from the beta version allowed us to test the data architecture, robustness of software (i.e., bug catching), test the epidemiological dynamics embedded within the experimental gaming framework, and develop statistical analyses to test network and policy level treatments (e.g., test the effects of conditional indemnity linked to biosecurity for altering the willingness to invest in biosecurity). For objective 3, the open source ABM has been configured to incorporate survey and gaming data. One journal article has been published and one conference paper has been accepted for presentation.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Gabriela Bucini, Eric Michael Clark, Scott C. Merrill, Ollin Langle-Chimal, Asim Zia, Christopher Koliba, Nick Cheney, Serge Wiltshire, Luke Trinity and Julia M Smith (2023) Connecting livestock disease dynamics to human learning and biosecurity decisions. Frontiers in veterinary science. Volume 9. https://doi.org/10.3389/fvets.2022.1067364
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2024
Citation:
Zia, Asim, Richmond Baye, Scott Merrill, Eric Clark, Gemma Del Rossi, Jackson Dean, Samuel Rosenblatt, Nick Cheney, Laurent Hebert-Dufresne, Julie Smith. Eliciting Behavioral Responses to Alternate Biosecurity Risk Management Strategies in a Stochastic Game. Conference of Research Workers in Animal Disease (CRWAD). January 2024. Chicago, IL.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Baye, Richmond, Julie Smith, Asim Zia, Eric Clark, Scott Merrill, Chris Koliba. (Under Review) Biosecurity Indemnification and Attitudes of United States Swine Producers towards the Prevention of an African Swine Fever Outbreak. Preventive Veterinary Medicine
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Baye, Richmond, Julie Smith, Asim Zia, Eric Clark, Scott Merrill, Chris Koliba. (Under Review) Enhancing Food Sustainability through Latent Class Analysis of Biosecurity Attitudes and Decision- Making Strategies of Swine Producers in the United States. Nature Scientific Reports.
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2024
Citation:
Jackson Dean, Eric Clark, Kevin Andrew, Scott Turnbull, Richmond Baye, Samuel F. Rosenblatt, Johnbosco Osuagwu, Asim Zia, Scott Merrill, Julie Smith, Laurent Hebert-Dufresne, Nick Cheney. Reinforcement learning for agent-based modeling of swine producer biosecurity adoption. Conference of Research Workers in Animal Disease (CRWAD). January 2024. Chicago, IL.
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2024
Citation:
Rosenblatt, Samuel F., Laurent H�bert-Dufresne. Maximizing epidemics with spatial and categorical assortativities in modular geometric contagion networks. Conference of Research Workers in Animal Disease (CRWAD). January 2024. Chicago, IL.
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2024
Citation:
Baye, Richmond, Julie Smith, Asim Zia, Eric Clark, Scott Merrill, Chris Koliba. Enhancing Food Sustainability through Latent Class Analysis of Biosecurity Attitudes and Decision- Making Strategies of Swine Producers in the United States. Conference of Research Workers in Animal Disease (CRWAD). January 2024. Chicago, IL.
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2024
Citation:
Zia, Asim, Amy H. Delgado, Gabriela Bucini, Scott C. Merrill, Christopher Koliba, Gemma Del Rossi, Richmond S. Baye, Bo Norby, Julie M. Smith. Testing an Extended Theory of Planned Behavior to Explain Cattle Producers Intent to Comply with FMD Controls. Conference of Research Workers in Animal Disease (CRWAD). January 2024. Chicago, IL.
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Progress 09/01/21 to 08/31/22
Outputs
Target Audience:Scientists, modelers, and officials interested in obtaining a better understanding of how risk associated with infectious diseases are affected by the structure of the livestock industry and the behaviors of those involved. Further, the following stakeholders are informed by the outputs and outcomes of this project:veterinary epidemiologists, state animal health officials, and livestock industry professionals. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Four graduate students and two undergraduate students are being trained in animal disease transmission modeling, including InterSpread Plus, ABM, as well as experiemntal gaming and survey data collection procedures. How have the results been disseminated to communities of interest?In addition to publication of 5 journal articles and 3 conference presentations, we organized a mediated modeling meeting with our advisory board members on October 13 and 14, 2022. The advisory board members included representatives from USDA APHIS, National Pork Bureau, Livestock Industry and senior acamemics. During this meeting, our team presented thelatest research on the ABM, surveys and experimental games. Many members of the advisory board also presented their ongoing research on these topics, as well as reviewed the progress of our project vis a vis our research milestones. Another important part of this mediated modeling meeting was to discuss configuration of processes within the ABM and identification of ABM scenarios for comparison with current models being used by policy makers and the industry professionals. This meeting resulted in concrete recommendations for scenario and hypothesis testing during the forthcoming years 2 and 3 of this project. What do you plan to do during the next reporting period to accomplish the goals?We will continue to develop and test the national scale ABM for simulating ASF and FMD and incoporate policy and behavioral responses from the latest survey and gaming data sets. Findings from the ABM experimental simulations, games and surveys will be published in journal articles,conferences as well as policy briefs.
Impacts
What was accomplished under these goals?
For objective 1, design and test an ABM, the research team signed a data and model sharing MoU with USDA APHIS and acquired InterSpread Model configuration for simulation of ASF and FMD. In parellel, we have developed an open source ABM platform in C++ and Java. This ABM simulates disease propagation in Swine Producer networks under alternate policy and behavioral strategies. Findings from this ABM have been recently published in peer reviewed articles and will be presented in the forthcoming 2023 CRWAD conference. For objective 2, develop and implement panel surveys and online experimental gaming simulations, our team has completed collection of wave 1 of a national survey instrument and findings from this survey data will be presented in the forthcoming 2023 CRWAD conference. Two experimental games have been piloted and the results from the gaming data have been published in three journal articles. For objective 3, the open source ABM has been configured to incorporate survey and gaming data. Two journal articles are under review.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Clark EM, Merrill SC, Trinity L, Liu T-L, O'Keefe A, Shrum T, Bucini G, Cheney N, Langle-Chimal OD, Koliba C, Zia A and Smith JM (2022) Comparing behavioral risk assessment strategies for quantifying biosecurity compliance to mitigate animal disease spread. Front. Vet. Sci. 9:962989. doi: 10.3389/fvets.2022.962989
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Koliba C, Merrill SC, Zia A, Bucini G, Clark E, Shrum TR, Wiltshire S and Smith JM (2022) Assessing strategic, tactical, and operational decision-making and risk in a livestock production chain through experimental simulation platforms. Front. Vet. Sci. 9:962788. doi: 10.3389/fvets.2022.962788
- Type:
Journal Articles
Status:
Accepted
Year Published:
2022
Citation:
Tung-Lin Liu, Scott C. Merrill, Aislinn OKeefe, Eric Michael Clark, Ollin D Langle-Chimal, Luke Trinity, Trisha Shrum, Christopher Koliba, Asim Zia, Julia M Smith, Effects of Message Delivery on Cross-cultural Biosecurity Compliance: Insights from Experimental Simulations. Frontiers in veterinary science, in-press (Accepted October 2022). doi: 10.3389/fvets.2022.984945
- Type:
Journal Articles
Status:
Under Review
Year Published:
2022
Citation:
Gabriela Bucini, Eric Michael Clark, Scott C. Merrill, Ollin Langle-Chimal, Asim Zia, Christopher Koliba, Nick Cheney, Serge Wiltshire, Luke Trinity and Julia M Smith (In review) Connecting livestock disease dynamics to human learning and biosecurity decisions. Frontiers in veterinary science
- Type:
Journal Articles
Status:
Under Review
Year Published:
2022
Citation:
Asim Zia, Amy H. Delgado, Gabriela Bucini, Scott C. Merrill, Christopher Koliba, Gemma Del Rossi, Richmond S. Baye, Bo Norby, Julie M. Smith (Revise and Resubmit) Testing an Extended Theory of Planned Behavior to Explain Cattle Producers Intent to Comply with Foot-and-Mouth Disease (FMD) Control Measures. PLOS Social Epidemiology
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
EM Clark, G Bucini, SC Merrill, O Langle-Chimal, C Koliba, L Trinity, N Cheney, T Shrum, A Zia, and JM Smith. Linking experimental games with agent based models to quantify agricultural outbreak dynamics. Conference of Research Workers in Animal Disease (CRWAD). December 2021. Chicago, IL.
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2023
Citation:
Asim Zia, Eric Clark, Gabriela Bucini, Richmond Baye, Scott Merrill, Christopher Koliba, Nick Cheney, Laurent Hebert-Dufresne, Julie Smith, Discovering Leverage Points for Enhancing Biosecurity in Swine Production Networks Using Agent Based Models . Conference of Research Workers in Animal Disease (CRWAD). January 2023. Chicago, IL.
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2023
Citation:
Baye, Richmond, Julie Smith, Asim Zia, Eric Clark, Scott Merrill, Chris Koliba. Understanding how indemnity affects biosecurity measures by United States swine producers. Conference of Research Workers in Animal Disease (CRWAD). January 2023. Chicago, IL.
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