PERSPECTIVE article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
This article is part of the Research TopicLeveraging Real-Time Genomic Surveillance to Combat Infectious Diseases and Antimicrobial ResistanceView all 16 articles
Leveraging Artificial Intelligence community analytics and Nanopore metagenomic surveillance to monitor early enteropathogen outbreaks
Provisionally accepted
Jeff Gauthier1,2*
Sima Mohammadi1,2
Irena Kukavica-Ibrulj1,2
Brian Boyle1
Chrystal Landgraff3
Lawrence Goodridge4Kenton White5Benjamin Chapman5,6
Roger C. Levesque1,2*- 1Universite Laval Institut de Biologie Integrative et des Systemes, Québec City, Canada
- 2Universite Laval Faculte de Medecine, Québec City, Canada
- 3Public Health Agency of Canada Infectious Disease Prevention and Control, Ottawa, Canada
- 4University of Guelph Department of Food Science, Guelph, Canada
- 5Advanced Symbolics Inc., Ottawa, Canada
- 6NC State University College of Agriculture and Life Sciences, Raleigh, United States
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Foodborne enteric infections are a major public health and economical burden, yet their surveillance often relies on latent indicators that delay containment efforts by several days and weeks. Conversely, whole metagenome shotgun sequencing of communal wastewater allows continuous monitoring of enteric pathogens. Spikes in abundance can be observed several weeks before the first case reports emerge. In addition, AI-driven social media mining, already in use for public opinion analytics, could be repurposed for predicting outbreaks at the community level by predicting the number of people experiencing symptoms in the population given their social media activity. Here we report how AI-driven community analytics and high-throughput long-read metagenomic surveillance of communal wastewater microbiota were combined to monitor non-typhoidal salmonellosis in Quebec City, Canada, from August 2023 to February 2024. Both approaches indicated similar fluctuations over time for: i) people experiencing salmonellosis symptoms, and ii) Salmonella enterica relative abundance in wastewater, with predicted cases leading metagenomic peaks by a week. Moreover, both approaches detected a maximum around September 13th, 2023, five weeks before a Salmonella food recall for the Quebec and Ontario provinces was made by the Public Health Agency of Canada. We therefore suggest that continuous AI-driven analytics and wastewater metagenomics monitoring could become part of a nationwide surveillance pipeline from the community scale to the molecular level.
Keywords: Metagenomics, artificial intelligence, Salmonella, Pathogen surveillance, Oxford Nanopore Sequencing (ONT)
Received: 28 Jul 2025; Accepted: 12 Nov 2025.
Copyright: © 2025 Gauthier, Mohammadi, Kukavica-Ibrulj, Boyle, Landgraff, Goodridge, White, Chapman and Levesque. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jeff Gauthier, jeff.gauthier.1@ulaval.ca
Roger C. Levesque, rclevesq@ibis.ulaval.ca
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.