Gustavo Machado

The high mutation rate of PRRSV represents a big challenge and raises two important questions for swine producers: Which PRRSV strain will hit my farm next? And which vaccine can best protect my herd against it? Currently no technology can adequately answer those questions. To overcome this issue, this project will combine two state-of-the-art technologies – PRRSV forecasting and heterologous vaccine efficacy prediction. These technologies will create the first proactive PRRSV mitigation system: Predict and Protect against PRRSV (PreProPRRSV). Thereby, for the first time, PRRSV outbreak mitigation will become proactive! We will establish the PreProPRRSV system in two objectives: Objective 1 will establish a PRRSV forecasting technology. This forecasting methodology uses computer-based prediction algorithms based on surveillance data relevant to predict PRRSV spread – both intrinsic (e.g. variation of pathogen strains) and extrinsic (e.g. landscape) variables, pig transporting and farm locations. This technology can precisely predict the spread of PRRSV strains. Objective 2 will establish a vaccine efficacy prediction system. This system consists of an immune biobank (cells + serum) from pigs, which received different PRRSV vaccinations. This biobank will enable us to determine within around two weeks which vaccine induces the strongest immune response to the approaching PRRSV strain. This interdisciplinary project combines computer-algorithm-based forecasting with translational immunology to enable precision animal management for PRRSV: It will determine the most effective vaccine BEFORE the emerging PRRSV strain arrives at a production site. The proactive PreProPRRSV outbreak mitigation system will drastically enhance animal health and production by decreasing the impact of PRRS.

Evaluation of decontamination protocols and vehicle movement to mitigate the transmission risk of PED virus as a proxy for FAD

The purpose of this agreement is to utilize our existing African swine fever (ASF) transmission model to create a website dashboard that calculates the number of certified swine sample collectors (CSSCs) needed to effectively respond to an ASF outbreak in the U.S. Intended beneficiaries include the creators of a national CSSC training program, the National Pork Board, as well as state and federal animal health officials and industry veterinarians, who currently lack information about the number of CSSCs needed for surveillance and movement permitting within outbreak control zones. This is particularly challenging due to differences between ASF strains, which lead to epidemics of varying duration and magnitude and could be catastrophic if not effectively controlled. Activities to be performed will build on the success of previously funded NADPRP projects that have supported the development of a transmission model (PigSpread) for low, moderate, and high virulence ASF strains, and the collection of vast movement datasets from swine businesses across North Carolina, South Carolina, and Virginia. Specifically, we will modify our existing ASF model to calculate the number of CSSCs needed based on factors such as movement networks, time taken to collect and deliver samples to surveillance laboratories within an 8-hour workday, and mandatory downtime between farm visits among other biosecurity rules. We will subsequently construct a dashboard that swine industry stakeholders can use to input information to generate region-specific information for their CSSC training programs. This project will deliver a website dashboard that calculates the number of people that need to be trained via state CSSC training programs based on key transmission factors such as ASF strain incubation and latency, as well as regional-specific information such as farm and laboratory locations and movement networks. In doing so, this project will help states and swine companies conduct strategic CSSC training to ensure sufficient blood samples can be collected from swine farms during an ASF outbreak, thus enabling effective disease surveillance and distribution of movement permits within outbreak control zones. Ultimately, this has the potential to help preserve swine business continuity in the event of an ASF emergency in the U.S.

Award purpose: The main aim of this project is to provide rapid access to up to date, standardized on-farm Biosecurity Poultry Producer (BPP) plans including Secure Egg (SES), Turkey (STS), and Broiler (SBS) supply plans to enable business continuity for the poultry industry in Pennsylvania state. The feasibility of the project is based on the strong preliminary work in which we developed a dashboard (Rapid Access Biosecurity Application [RABapp™] swine), which provides this team with the necessary lessons learned to expand the initiative to other systems. Our work will provide a clear picture of the current state of on-farm biosecurity and address the lack of readily accessible data that is crucial for developing timely response and recovery strategies. Activities to be performed: 1) Collect and standardize approximately 1,000 BPP plans and maps of on-farm biosecurity infrastructure from farms Pennsylvania state; 2) Integrate standardized BPP plans and maps of on-farm biosecurity infrastructure into one rapid-to-access database dashboard RABappTM) to stock, review and approve BP plans. This will involve the following activities: • Collect and standardize at least 1,000 BPP plans of Pennsylvania state using rigorous data collection and analysis procedures. • Training government and industry stakeholders across Pennsylvania state to use the RABappTM poultry. Deliverables: 1) Integrate standardized BPP plans data and maps of on-farm biosecurity infrastructure into RABapp™ poultry; and 2) Expand BPP plan adoption throughout the poultry industry, train stakeholders to use the dashboard, and ultimately refine site-specific business continuity plans. This will involve the following activities: • Confidential disclosure agreements with 2 poultry production in Pennsylvania state. • Approximately 1,000 BPP site-specific business continuity plans are available to be approved by animal health officials. Expected outcomes: By expanding access to on-farm biosecurity for poultry industry stakeholders across Pennsylvania state, this project will expedite the national responses to poultry disease emergencies. Lastly, we expect this project to equip a major proportion of the national poultry industry with the tools, training, and information needed to strengthen on-farm biosecurity defenses, and thus increase preparedness for future outbreak events.

Promote continuing education in the official veterinary services, through the development and application of statistical techniques used in the field of epidemiology, with a special focus on the analysis of complex networks to be applied to animal movement data, in order to improve the capacity of the state of RS in defense animal health through research and advisory projects. In particular, automation of animal movement analysis for all species susceptible to Foot-and-Mouth Disease and analytical assistance for the Sentinela program.

Optimal performance of commercial swine populations depends on the interaction of several determinants including infectious diseases and factors related to management and environment such as mixing pigs from different sources, space allowance, and nutrition. Producers capture vast amounts of data but store them in disconnected databases. Thus, there is a tremendous opportunity to pursue synergizing swine data. We will leverage ongoing initiatives and resources to develop, deploy, and promote the Predictors of Swine Performance (PROSPER), a digital platform to capture, integrate, analyze, and visualize data of multiple sources in an ongoing and automated fashion. Causal models for observational dataset will be developed and implemented allowing producers to identify and measure the effect of various factors on swine performance under their specific field conditions. We will also implement forecasting models to help producers to strategically allocate resources as needed to improve swine health & productivity of commercial flows. Strategic collaborations and extension activities within various swine industry stakeholders will target effective dissemination of knowledge generated in this proposal, driving the productivity of the swine industry forward. The process and models herein developed can be adapted to poultry, cattle, and other livestock. In summary, the project will develop, deploy, and promote the Precision Animal Agriculture concept in swine. Activities will cultivate the implementation of technologies and applied knowledge to support producers making data-driven decisions to significantly improve swine performance, strengthening the sustainability and the competitiveness of the US pork production.

Porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED) cost the US swine industry more than $1 billion/year. The detection of these viruses in the US led to the application of regional disease control and elimination efforts. Despite the great reduction in PED incidence, PRRS still affects a third of the US sow herds yearly. Therefore, there is a critical need to identify: i) why PED and PRRS keep spreading and ii) strategies that improve disease control. Our goals combine data-visualization with spatial and statistical methods to increase the fundamental knowledge about PED and PRRS spatial epidemiology, enhancing our understanding of disease spread to develop novel data-integration tools to optimize disease control. This proposal aligns with the program area “Food and Agriculture Cyberinformatics Tools (FACT: A1541)-Animal Health and Production and Animal Products”. Our research activities use existing data of PED and PRRS outbreaks from one of the US major swine regions. We will apply Bayesian and machine-learning methodology to quantify the geographic distribution, local variation of intrinsic and extrinsic risk factors and farm characteristics that predispose swine farms to be infected with PED and/or PRRS. We propose the development of spatiotemporal treatment regimen tool to help the swine industry decide the location and when to allocate their control resources (e.g., vaccination, herd exposure to live virus upon an outbreak). Impacts of this study include the promotion of informed disease management and improved animal health through enabling the swine community to effectively use existing data to manage outbreaks.

Like many infectious diseases, African swine fever (ASF) propagates between farms via direct and indirect contact. Among these routes, the movement of transportation vehicles (e.g., feed and holing trucks) has been recently associated with the spread of infectious diseases. Thus, it is necessary to evaluate strategies that decrease the connectivity among farms by vehicle movements and consider the frequency of cleaning and disinfection visits to mitigate ASF spread. Through a previous proposal funded by SHIC, we developed a methodology to reconstruct vehicle movement networks. We will use this methodology to reconstruct contact among farms by ~600 vehicles visiting the farms. The raw movement data comes directly from GPS data of tracking equipment installed in the company's own vehicles, along with transportation data we have collected from disinfection stations (truck wash) which is considered while reconstructing the networks. The epidemiological framework to rebuild the vehicle movement network uses the collected GPS information, location, effectiveness of truck wash, environmental temperature, time among contacts, and pathogen stability. Using the previous methodology as a base, we will create a novel methodology to redirect vehicle movements and evaluate the effectiveness of redirecting them to mitigate the disease spread among farms.

When effectively implemented, on-farm biosecurity measures, minimize the transmission of endemic pathogens and protect against the introduction and spread of transboundary animal diseases such as Foot-and-mouth disease (FMD). Research suggests an outbreak in the U.S. could result in losses of over $6 billion, even if contained to one state, and scores of billions of dollars, depending on the duration of the outbreak, the extent of trade embargoes, and the reaction of consumers to the disease and response measures. Without sound biosecurity, response and recovery strategies and control interventions are rendered ineffective. Although the role of biosecurity is well understood, the U.S. cattle industry is working without a clear understanding of the viability of individual farm biosecurity plans and the efficacy of existing biosecurity measures. Thus, this project will be built on top of a recent NADPRP project, that is focused on providing rapid access to up-to-date, standardized on-farm biosecurity plans and animal movement data of commercial pig operation in multiple states and a recent 2019 NADPRP project on the regional approach to the continuity of business planning for the beef industry in Kansas. For the proposed project our objective is to provide access to standardized on-farm biosecurity plans following strict and standardized requirements for the cattle farms in Kansas state. Outcomes/deliverables: 1) Standardized Secure Beef Supply (SBS) plans data and maps of on-farm biosecurity infrastructure into one rapid-to-access database dashboard (RABapp) and 2) Expand SBS plan adoption throughout the cattle industry, train stakeholders to use the dashboard, and ultimately refine site-specific business continuity plans. Target audience and feasibility: Kansas state animal health, beef producers, and mixed animal veterinarians. The feasibility of the project is based on the strong preliminary work in which we developed a dashboard (RABapp). Our work will provide a clear picture of the current state of on-farm biosecurity and address the lack of readily accessible data that is crucial for developing timely response and recovery strategies.

African swine fever (ASF) is a foreign animal disease associated with mass mortality and is considered the most impactful swine disease worldwide. Detailed knowledge relating to the spread of such a catastrophic and deadly virus is critical for an effective planning and deployment of control and eradication strategies. In the absence of effective vaccines for ASF, control strategies are heavily dependent on mass depopulation and movement restrictions. Therefore, complete and precise population demography (i.e., pig capacity, farm type, and location) and between-farm movement data become important pieces of data needed to characterize the complex and highly integrated movement of animal, feed, and other personnel among premises. Hence, with such data finally available, we are at last ready to evaluate disease propagation in-depth and test the feasibility of disease control strategies. Our objectives and outcomes explore the specifics in further detail and are as follows: 1. Reconstruct the contact networks of pigs and trucks moving among commercial swine producing premises among multiple U.S. states and generate network-based risk levels to further classify super spread farms. 2. Utilizing the networks from objective 1, we will develop a network transmission model to simulate the implementation of control zones of multiple sizes and different durations to calculate the volume of movements to, from, within and outside the monitored farms. Target audience and feasibility: NC, OK and MN state animal health officials, federal animal health officials, swine production veterinarians, and pig producers. This project will minimize industry disruption during disease emergencies by testing the effectiveness of current network-based movement restrictions against an ASF outbreak.