Parasite associations predict infection risk: incorporating co-infections in predictive models for neglected tropical diseases.

ABSTRACT: BACKGROUND:Schistosomiasis and infection by soil-transmitted helminths are some of the world's most prevalent neglected tropical diseases. Infection by more than one parasite (co-infection) is common and can contribute to clinical morbidity in children. Geostatistical analyses of parasite infection data are key for developing mass drug administration strategies, yet most methods ignore co-infections when estimating risk. Infection status for multiple parasites can act as a useful proxy for data-poor individual-level or environmental risk factors while avoiding regression dilution bias. Conditional random fields (CRF) is a multivariate graphical network method that opens new doors in parasite risk mapping by (i) predicting co-infections with high accuracy; (ii) isolating associations among

SUBMITTER: Clark NJ 

PROVIDER: S-EPMC7077138 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature