Using Big Data analytics and other new methods for the modeling of infectious diseases affecting aquatic organisms
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1
Center for Animal Disease Modeling and Surveillance (CADMS), University of California, Davis, United States
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2
Norwegian Veterinary Institute, Oslo, Norway
Infectious diseases affecting aquaculture industry have devastating economic implications for the affected regions or countries. To prevent or minimize the disease impact, timely decisions need to be made for their rapid prevention, management, control and eradication. Ideally, those decisions need to be informed by scientific-based epidemiological analyses, risk assessments and models based on the most reliable and current data. Currently, large amount of the data are daily generated from various sources for monitoring the fish production, reporting disease occurrence and evaluate environmental parameters. However, there are no tools that allow effectively integrating, analyzing and summarizing all these information to timely inform decisions. In fact, most of the epidemiological analyses, risk assessments and models conducted so far need substantial amount of time and effort, mainly to gather and integrate the needed information, and are difficult and time consuming to update if the epidemiological scenario changes (e.g. introduction of the disease to new areas, implementation of area control programs, vaccination or trade restrictions, changes in policies, etc.), which limits their potential use to inform decision making in a timely manner. Because decisions about aquaculture health need to be made continuously and rapidly (particularly, in crisis time), we need tools that allow to inform and support those decisions with the same velocity. Here we aimed to fill this gap by developing innovative, accessible and user-friendly Big data analytics and real-time risk assessment and modeling of infectious diseases within the web-based platform known as Disease BioPortal® (http://bioportal.ucdavis.edu/) to support timely decisions in aquaculture industry.
The platform incorporates advanced analytical methods very useful for infectious disease epidemiology such as cluster analysis, social network analysis, phylogenetic models, time-series analysis, and space-time-genomic visualization methods. Depending on the level of access, the user can combine, visualize and download data in different formats: 1) tabular, which includes: events (e.g., test results of the reference labs, fish movements, production information, etc.), date, farm location (i.e. coordinates), type of facility, disease, strains, address, etc.; 2) maps (linked to Google maps and Google Earth); 3) graphs (columns, pie, bars, phylogenetic trees, contact networks, homology and distance pareto charts for genetic and geographic distance visualization, etc.). Parameters and variation over time can be dynamically evaluated using different criteria cut-off and the temporal and genetic slide bar. All the aforementioned information can be combined for the exploration of the spatial and temporal distribution of cases, genetic and geographic distance, evaluation of high risk contacts among farms and risk classification and benchmarking. The user can select and save different displays of one or more datasets, using maps, phylogenetic trees, graphs, distance pareto charts (i.e. proximity analysis), homology tables, etc.. All this information can be summarized in customized reports using the Report Builder tool that allows to easily generate reports to inform producers and other stakeholders periodically.
Here, we will illustrate the characteristics and performance of the new Big data analytics and real-time risk assessment and modeling capabilities within Disease BioPortal® using information from the Norwegian salmon industry. We will also discuss the limitations, challenges and future directions for the implementation of real-time risk assessment and modeling tools to support aquaculture health policies locally and globally.
Acknowledgements
This work has been founded by the Norwegian Research Council Grant Number 245477/E40 and the Norwegian Veterinary Institute.
Keywords:
Real-time surveillance,
Risk Assessment,
Aquaculture industry,
Epidemiology,
Network analysis
Conference:
AquaEpi I - 2016, Oslo, Norway, 20 Sep - 22 Sep, 2016.
Presentation Type:
Keynote
Topic:
Aquatic Animal Epidemiology
Citation:
Martínez-López
B and
Tavornpanich
S
(2016). Using Big Data analytics and other new methods for the modeling of infectious diseases affecting aquatic organisms.
Front. Vet. Sci.
Conference Abstract:
AquaEpi I - 2016.
doi: 10.3389/conf.FVETS.2016.02.00065
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Received:
19 Sep 2016;
Published Online:
19 Sep 2016.
*
Correspondence:
Prof. Beatriz Martínez-López, Center for Animal Disease Modeling and Surveillance (CADMS), University of California, Davis, Davis, California, 95616, United States, beamartinezlopez@ucdavis.edu