Genetic distance computation method for comparing complex multi-locus parasite (Cyclospora) genotypes
Description
Investigating the similarity of infections for epidemiologic investigations of cyclosporiasis outbreaks. The method enables clustering and comparison of complex genotypes, which are too large and complex for traditional methods, to identify related infections during outbreak tracking.
Detailed example
The system outputs genetic distance matrices and clusters of closely related infections, based on comparisons of haplotypes from clinical samples. These outputs are used to complement epidemiologic investigations and traceback activities.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The AI enables analysis of massive genotype datasets, facilitating rapid and accurate identification of infection clusters. This supports epidemiologic investigations and traceback for cyclosporiasis and other parasites, improving outbreak response and public health interventions.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Cyclospora sequence data generated by CDC, State Public Health Labs, and the Public Health Agency of Canada, following a CDC-developed protocol for 8 genotyping markers. All CDC and State Public Health Labs sequence data are publicly available via NCBI (see below).