A framework for prioritization of foodborne pathogen illness clusters for successful public health intervention using machine learning-based analysis

 

A framework for prioritization of foodborne pathogen illness clusters for successful public health intervention using machine learning-based analysis of integrated surveillance and outbreak population genomics datasets

Funding period: 2019-2023
Lead: John Nash
Total GRDI funding: $718,000

The large-scale implementation of whole genome sequencing the surveillance of foodborne pathogens has led to the increased detection of case clusters that exceeds existing capacity for epidemiological investigation. It is in this context that this project will seek to develop novel analytical tools for the analysis of sequencing data to facilitate cluster prioritization based on parameters demonstrated to lead to successful public health interventions (e.g. the attribution of outbreak clusters to likely food vehicles to inform short and long-term foodborne illness prevention and control strategies), including the identification of biomarkers predictive of overall human health risk and of strain source.

Publications

  • Baclic O, Tunis M, Young K, Doan C, Swerdfeger H, Schonfeld J. 2020. Challenges and opportunities for public health made possible by advances in natural language processing. Can Commun Dis Rep. 46(6):161–8. https://doi.org/10.14745/ccdr.v46i06a02
  • Clark CG, Landgraff C, Robertson J, Pollari F, Parker S, Nadon C, Gannon VPJ, Johnson R, Nash J. 2020. Distribution of heavy metal resistance elements in Canadian Salmonella 4,[5],12:i:- populations and association with the monophasic genotypes and phenotype. PLoS ONE 15(7): e0236436. https://doi.org/10.1371/journal.pone.0236436
  • Hetman BM, Mutschall SK, Carrillo CD, Thomas JE, Gannon VPJ, Inglis GD, Taboada EN. 2020. "These aren't the strains you're looking for": recovery bias of common Campylobacter jejuni subtypes in mixed cultures. Frontiers in Microbiology. 11:541. https://doi.org/10.3389/fmicb.2020.00541
  • Hodges LM, Carrillo CD, Upham JP, Borza A, Eisenbraun M, Kenwell R, Mutschall SK, Haldane D, Scheilauf E, Taboada EN. 2019. A strain comparison of Campylobacter isolated from retail poultry and human clinical cases in Atlantic Canada. 2019. PLoS ONE. 14(5):e0215928. https://doi.org/10.1371/journal.pone.0215928
  • Inglis GD, Boras VF, Webb AL, Suttorp VV, Hodgkinson P, Taboada EN. 2019. Enhanced microbiological surveillance reveals that temporal case clusters contribute to the high rates of campylobacteriosis in a model agroecosystem. 2019. Int J Med Microbiol. 309(3-4):232. https://doi.org/10.1016/j.ijmm.2019.04.003
  • Inglis GD, Gusse JF, House KE, Shelton TG, Taboada EN. 2020. Clinically relevant Campylobacter jejuni subtypes are readily found and transmitted within the cattle production continuum but present a limited foodborne risk. Appl Environ Microbiol. 86(6):e02101-19. https://doi.org/10.1128/AEM.02101-19
  • Inglis GD, Ramezani N, Taboada EN, Boras VF, Uwiera RRE. 2021. Analysis of Campylobacter jejuni subtype distribution in the chicken broiler production continuum: a longitudinal examination to identify primary contamination points. Appl Environ Microbiol. 87(3). https://doi.org/10.1128/AEM.02001-20
  • Labbé G, Kruczkiewicz P, Robertson J, Mabon P, Schonfeld J, Kein D, Rankin MA, Gopez M, Hole D, Son D, Knox N, Laing CR, Bessonov K, Taboada EN, Yoshida C, Ziebell K, Nichani A, Johnson RP, Van Domselaar G, Nash JHE. Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel. Microb Genom. 2021 Sep;7(9):000651. https://doi.org/10.1099/mgen.0.000651. PMID: 34554082; PMCID: PMC8715432
  • Steinkey R, Moat J, Gannon V, Zovoilis A, Laing C. 2020. Application of artificial intelligence to the in silico assessment of antimicrobial resistance and risks to human and animal health presented by priority enteric bacterial pathogens. Canada Communicable Disease Report: Artificial intelligence in public health. 46(6):180-185. https://doi.org/10.14745%2Fccdr.v46i06a05

Contact us

For additional information, please contact:
Genomics R&D Initiative
Email: info@grdi-irdg.collaboration.gc.ca