Tracing outbreaks with machine learning

This Genome Watch article discusses the application of machine learning algorithms to predict the source of food-borne infections.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Allard, M. W. et al. Practical value of food pathogen traceability through building a whole-genome sequencing network and database. J. Clin. Microbiol. 54, 1975–1983 (2016).

  2. 2.

    Branchu, P., Bawn, M. & Kingsley, R. A. Genome variation and molecular epidemiology of Salmonella enterica Serovar Typhimurium pathovariants. Infect. Immun. 86, e00079–18 (2018).

  3. 3.

    Lupolova, N. et al. Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli. Microb. Genom. 3, e000135 (2017).

  4. 4.

    Zhang, S. et al. Zoonotic source attribution of Salmonella enterica serotype Typhimurium using genomic surveillance data, United States. Emerg. Infect. Dis. 25, 82–91 (2019).

  5. 5.

    Falush, D. Bacterial genomics: microbial GWAS coming of age. Nat. Microbiol. 1, 16059 (2016).

Download references

Author information

Correspondence to Nicole E. Wheeler.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark