Accelerating data sharing, visualization and analysis to support efficient disease monitoring and more real-time decision making in the swine industry using the Disease BioPortal platform.
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1
Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, United States
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2
Boehringer Ingelheim (United States), United States
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3
Veterinary Diagnostic Laboratory, College of Veterinary Medicine, Iowa State University, United States
Nowadays, veterinarians and livestock producers are demanding not only a quicker detection and diagnosis of diseases affecting their animals but to use genome-informed diagnostic tools to classify the specific sub-groups or isolates of the pathogen and measure genetic similarities to other previously isolated strains for tracing and outbreak investigation purposes (1).
The ability of using genomics to trace transmission dynamics coupled with other technological advances that are facilitating the recording and storage of demographic, management and production data as well as of other health and management associated-information (i.e. sensors, animal movements, environmental and weather data, etc.). is providing unprecedented opportunities to more holistically monitor animals and make more informed, data-driven, decisions.
However, despite the increase in genomic and animal- or farm-level data collection systems, very rarely those data streams are integrated and analyzed in a combined way which limits the prediction and decision-making capabilities. There is also need to develop custom algorithms that can effectively integrate and mine these multi-scale data to be able to rapidly identify patterns. Moreover, outcomes need to be intuitively described and presented in a user-friendly manner as well as to allow the easy sharing among producers veterinarians and other stakeholders to inform decisions.
Our research team have been working in an exceptional research-industry partnership to develop key data connections and novel Big Data visualization and analytical capabilities to try to fill this gap (2,3). Our approach facilitates the combined analysis of animal health, production and trade data using novel space-time-genomic visualization and data mining tools to inform risk-based, more cost-effective, decisions at site, system or regional level.
Currently, version 4.0 of the platform can be accessed after registration at: http://bioportal.ucdavis.edu/. The different components and tools in the Disease BioPortal allows the real-time integration, secure access, sharing, visualization and analysis of multiple streams of information (e.g., diagnostics, production, movement, etc.). Some publicly available databases (e.g., OIE-WAHID, GenBank, etc.) are currently accessible to all registered users. Access to confidential databases, including the connection to the Animal Health Monitoring and Evaluation System (AHMES) from the Veterinary Diagnostic Laboratory at Iowa State University (VDL ISU) and other laboratories, can be individually granted with different levels of secure-access for participating producers, labs, veterinarians and other stakeholders.
Here we provide some examples of the Disease BioPortal implementation for the swine industry where we are using diagnostics, production and movement data to track the spatiotemporal dynamics of swine infectious diseases in the US. PRRS is used here for illustration purposes: thousands of PRRSV sequences are generated monthly which allows the use and update of phylogenetic trees for a more precise monitoring of PRRS evolution, generate hypotheses about how PRRSV is being transmitted, identify new viruses that are emerging or those that are disappearing. Similarly, animal movement and production data is daily collected and can be used to evaluate animal movement patterns and even early identify problems or quantify the impact in terms of production loses of PRRS outbreaks on farm. This information is very valuable to implement risk-based surveillance and mitigation strategies, including customized biosecurity and vaccination programs.
All the aforementioned information can be combined for the exploration of the spatial and temporal distribution of cases, genetic and geographic distance and evaluation of high risk contacts among farms. The user can select and save different displays of one or more datasets, using customized dashboards that can contain maps, phylogenetic trees, graphs, distance pareto charts, contact networks, homology tables, etc. (e.g. Figure 1). All this information can be summarized in customized reports using a Report Builder tool that allows the easy generation of reports to inform stakeholders periodically. It is important to note that this tool is focused on providing analytic tools that will help producers and veterinarians more timely manage animal health problems to inform mitigation strategies. We believe that the development and implementation of customized web-based, user-friendly, visualization and analytical tools such as Disease BioPortal, although are challenging and require access to multiple data sources, the implementation of data standards and multi-disciplinary teams with different expertise is shaping the future to vertically improve livestock health. Next steps will be to integrate other data streams as well as incorporate new artificial intelligence algorithms to improve benchmarking, prediction of farm status and identification of high risk areas where strategies should be prioritized.
Figure 1. Example of a Disease BioPortal dashboard depicting the spatiotemporal dynamics of the status of PRRS of farm (map and graph on top) and the spatial distribution of different PRRS virus isolates (map and phylogenetic tree at the bottom) in a large swine production system in the US. Notice that color codes are linked (i.e. match) between the map and graph on top to depict the farm status as well as between the map and phylogenetic tree at the bottom to depict the PRRS viral clades or subgroups.
Acknowledgements
This project was partially funded by Boehringer Ingelheim Vetmedica support and the NSF BIGDATA:IA Award #1838207. Authors would like to acknowledge swine industry collaborators for the provision of data.
References
1. Gardy JL, Loman NJ. Towards a genomics-informed, real-time, global pathogen
surveillance system. Nat Rev Genet. 2018 Jan;19(1):9-20
2. Martínez-López, Polson D, Pires, A., Main R. (2015a). Association between genetic diversity of PRRS virus and the spatial, temporal and trade patterns in the United States. International Symposium on Emerging and re-emerging pig diseases (ISERPD). June 23, Kyoto, Japan.
3. Main R, Bjustrom-Kraft J, Crim B, Lowe E, Whedbee Z, Mondaca E, Mueller K, Polson D, Martinez Lopez B. (2016). Keep your feet on the ground but stick your head in the “cloud”: Web-based tools and services being developed for producer group specific and larger-scale (aggregate data) spatiotemporal swine health monitoring applications. Proceedings of the 47th Annual Meeting of the American Association of Swine Veterinarians (AASV), New Orleans, LA, (Feb 27-Mar 1).
Keywords:
Monitoring system,
user-friendly platform,
web-based,
Decision-making tool,
Swine Diseases,
space-time-genomic visualization
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Regular oral presentation
Topic:
Real-time field data collection and visualization platforms
Citation:
Martínez-López
B,
Polson
D,
Lowe
E,
Whedbee
Z and
Main
R
(2019). Accelerating data sharing, visualization and analysis to support efficient disease monitoring and more real-time decision making in the swine industry using the Disease BioPortal platform..
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00102
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Received:
20 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Prof. Beatriz Martínez-López, Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, Davis, California, CA 95616-5270, United States, beamartinezlopez@ucdavis.edu