LAB/savi – supercharging laboratory data

Diagnostic laboratories are a key data source for health information in both human and animal health. However, data application at the population or group level has been limited because reporting is primarily focused on individual cases or at a highly aggregated population level, allowing little opportunity for customised data exploration by end-users. While laboratory data will always be an opportunistic source of information for population health studies and interventions, advances in metadata collection and assembly will make laboratory data more usable in the future.

To address this challenge LAB/savi provides a customer interface for laboratory testing results where the user can explore real-time test results in space and time, filter by specific criteria or compare genetic sequences of outbreaks. The first version of LAB/savi was developed to monitor swine health and includes PCR and sequencing results for Porcine Respiratory and Reproductive Syndrome (PRRS) as well as antimicrobial susceptibility testing outputs.

LAB/savi is an R Shiny application and was developed in close collaboration with stakeholders of the Veterinary Diagnostic Laboratory of the University of Minnesota. The app is scalable to other species and diagnostic methods.  A key feature of LAB/savi is improved access to specialist data analysis such as the assessment of genetic relatedness or exploration of antimicrobial susceptibility trends.


  • Muellner P, Muellner U, Fioravante P, Thurn M, Yang L, Torrison J.  Supercharging laboratory data - from case to test to genetic tree in three clicks or less. 5th One Health Aotearoa Symposium, Wellington, New Zealand, December, 2019.
      View the conference presentation poster. 
  • Muellner P, Muellner U, Fioravante P, Thurn M, Yang L, Torrison J.  Red dot, blue line, black box – overcoming challenges in the visualisation of laboratory data. Australian and New Zealand College of Veterinary Scientists (ANZCVS) Science Week, Surfer’s Paradise, Australia, 2019.