Malaria parasites DNA barcode geography classification
Description
To complement epidemiologic investigations of domestic malaria cases by determining the geographic origin of malaria parasite strains, helping to understand how the strain entered the US.
Detailed example
The AI examines a sequence barcode/genotype and assigns the malaria parasite genotype to a geographic origin (e.g., continent or subregion).
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
This AI supports epidemiological investigations by providing rapid, automated classification of malaria parasite genotypes to geographic origins. This enhances the ability to track and respond to malaria cases, especially those domestically acquired, and supports public health interventions. For more information, see the manuscript:?https://journals.asm.org/doi/full/10.1128/aac.01203-24
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Data used are a mixture of data generated at CDC and other data available publicly. CDC data: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA428490/ https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1092573/ https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1110244 Non-CDC data: https://apps.malariagen.net/apps/pf7/ Travel histories from case patients were used to assess model performance (see manuscript:?https://journals.asm.org/doi/full/10.1128/aac.01203-24)