To review examples where joint analyses of food safety

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2 To review examples where joint analyses of food safety and public health data from different sources is beneficial To show that computationally efficient analytics can help with food safety monitoring and investigations, even if the available data is complex, voluminous, and multi-dimensional To vouch for the idea of sharing data between departments and jurisdictions To elicit feedback, suggestions, and to attract new users of the underlying technology 2 2

3 Background Introduction Review of the multiple data streams aspects of the proof of concept work into Predictive Analytics conducted so far by the team of FSIS, CMU and SAIC Prototype tool Movie Live demonstration available after the talk (upon request) Conclusions Key findings Importance of data sharing Utility of computationally efficient analytics 3 3

4 In the past, much of the FSIS data was stove-piped Data from inspection and processing facilities was not correlated with microbial results from laboratory testing Microbial data was not in a format that could be analyzed geospatially or chronologically FSIS partnered with the Auton Lab of Carnegie Mellon University and SAIC to develop methods and tools for interactive analysis of multiple streams of food safety and public health data 4 4

5 Initially, we have looked jointly at two independently maintained FSIS data streams: Performance Based Inspection System (PBIS) PBIS records noncompliance data produced by the FSIS inspectors in the field M2K Microbial testing data generated by the FSIS laboratories 5 5

6 We have found that recording one or more of certain class of noncompliances significantly increases the likelihood of the establishment to fail a Salmonella test in the near future: ift+ L pbis_allnr pbis_coalition pbis_w Evidence Window Size (days) - looking back periods The next question posed: Can similar methodology be used to predict possible human exposure from FSIS establishment data? 6 6

7 Regulatory Inspection data (PBIS, eadrs) Microbial Sampling Data (M2K) Positive samples Serotype PFGE Pattern PulseNet Human PFGE Data PHLIS Serotype Data Establishment and production volume info (PBIS, eadrs) Data Sources: FSIS ARS CDC Antibiotic Resistance We have assembled serotype information from the 2005 CDC Annual Salmonella Summary and PFGE data from VetNet and PulseNet Salmonella serotypes and VetNet PFGE patterns were mapped by FSIS establishment and also plotted chronologically Human illness PulseNet data was also mapped by location (state) and time of occurrence 7 7

8 CDC Top 10 FSIS Top Not on CDC list The top serotypes of Salmonella causing human illness are not the same as those found most frequently in FSIS controlled establishments 8 Should we drill down to serotype and PFGE level and focus on organisms with greater impact on human health more than on others when they are detected in food? 8

9 The graph shows 28-day Moving Averages for all Salmonella Montevideo cases recorded in PulseNet and VetNet from 2005 to 2007: For instance, the peak in human cases in October 2006 is preceded by a peak in FSIS-regulated establishments 9 9

10 PulseNet human source data JIXX cases in cluster 0609GAJIX-1c (24 cases) PulseNet human source data JIXX cases NOT in cluster 0609GAJIX-1c Vet-Net data All JIXX cases Suspicious cases 10 Temporal view of PFGE data for CDC cluster 0609GAJIX-1c A clear group of USDA isolates with the same PFGE pattern immediately precede the human outbreak 10

11 1. Joint analyses of data from multiple sources can support food recall investigations and traceback 2. Predictive analytics of multiple streams can be useful for identifying risks at food establishments 3. It can be useful to inform resource allocation based on expected public health impact 4. In the process, we have developed a prototype tool, which: Can speed up linking (or disproving links) between FSISregulated products and human illness Enables highly interactive visualization to improve understanding of data by the analysts Allows for incorporation of automated detectors that could reliably produce alerts for analysts to evaluate Is not intended to replace but to support epi investigations 11 11

12 USDA Food Sampling Data About 150,000 records related to Salmonella across 3 years Daily, transactional temporal resolution Spatial resolution by unique establishment Key attributes: test result (positive, negative), serotype, PFGE pattern, antibiotic resistance pattern, product type, establishment production profile Certain statistics such as moving average can be computed/updated on-the-fly Color coding is used to show multiple categories of data 12 CDC PulseNet Human Illness Data About 100,000 records related to Salmonella across 3 years Daily, transactional temporal resolution Spatial resolution by state Key attributes: serotype, PFGE pattern, outbreak code, product type, source type, source agency Controls such as time window sliders make visualization interactive 12

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14 Est. A Est. A FSIS CDC 14 In this example we began with a cluster of human Salmonella cases having a specific PFGE pattern Est A had positives with a similar PFGE immediately prior to the human cluster. It is located in the same geographic area as the human cases Investigators would immediately be aware that this establishment might be a source 14

15 One establishment recorded specific PFGE positives immediately prior to a human cluster with the same pattern It was located on the West Coast as were the human cases FSIS This pattern had never been seen before by USDA or CDC CDC 15 15

16 There are cases where we can identify events in the USDA data that precede human events and appear to be potentially related Analyses similar to those shown here could be very helpful in attribution, traceback, cluster triage, etc. investigations Further work may lead to automated detectors that could reliably alert whenever the indicative conditions occur The analysts can benefit from highly interactive data navigation environment by becoming aware of developing events rapidly and comprehensively 16 16

17 Significant benefits can be realized for all agencies by cooperating on data sharing and analysis: Better understanding of data, patterns, and pathways More rapid discovery of emerging serotypes and pulsotypes Ability to intervene sooner and prevent human exposure/illness Reduction of traceback times and improved investigations Presently, Memorandums of Agreement (MOA) have been developed to facilitate data sharing between FSIS-CDC, as well as FSIS-ARS. These MOA s define what data fields can be shared. They also set standards for clearance of information We are hoping to expand these relationships among other federal, state and local government agencies 17 17

18 By enabling: 1. Interactive navigation and visualization of data Quick response times to ad-hoc queries make it possible to interactively navigate data for clues and for confirmations of hypotheses 2. Support of explanations of detected patterns Fast access to data also supports answering follow-up questions such as What aspects contribute the most to the observed patterns? Some previously prohibitively expensive but helpful statistical tests often become feasible 3. Automated comprehensive searches through large collections of data Comprehensive search guarantees that important patterns are never missed Efficient representation of data can enable such searches in applications where previously they were never considered feasible

19 This research has been funded by FSIS, USDA through the Office of Data Integration and Food Protection NSF, CDC, US Air Force, IDRC of Canada, and private sources, have significantly contributed to making technical developments possible Special thanks to our collaborators: USDA Food Safety and Inspection Service Carol Maczka, Kristin Holt, Scott Seys, Bill Cray, Neelam Narang, Jeffrey Levine, Todd Lauze, PHELS staff USDA Agricultural Research Service Paula Cray, Beth McGlinchey, Jovita Haro CDC National Center for Zoonotic Vector-borne and Enteric Diseases David Warnock, Robert Tauxe, Peter Gerner-Smidt, John Besser, Efrain Ribot, Patricia Fields, Kelley Hise, Cara Cooper, Brenda Brown, Patricia Griffin, Fred Angulo, Ian Williams, Ezra Barzilay, Olga Henao, Richard Bishop, Mike Hoekstra SAIC Mark Huckabee, Adrienne Dunham Carnegie Mellon University Auton Lab Lujie (Karen) Chen, John Ostlund, Josep Roure, Saswati Ray, Daria Sorokina 19 19

20 Many thanks to all of the State Public Health Laboratories that submit PulseNet data Without your efforts this work would not be possible 20 20

21 21 Lynda Kelley Artur Dubrawski 21