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

Autor: Aimable Mbituyumuremyi, Ricardo J. Soares Magalhães, Eugene Ruberanziza, Jean Bosco Mbonigaba, Alan Fenwick, Irenee Umulisa, Warren Lancaster, Jean Bosco Mucaca, Nicholas J. Clark, Ursin Bayisenge, Giuseppina Ortu, Kei Owada
Rok vydání: 2019
Předmět:
0301 basic medicine
Ancylostomatoidea
Male
Multivariate statistics
Mycology & Parasitology
Feces
0302 clinical medicine
1108 Medical Microbiology
Risk Factors
Prevalence
EPIDEMIOLOGY
HETEROGENEITY
Child
Anthelmintics
biology
Coinfection
Soil-transmitted helminths
Spatial epidemiology
Neglected Diseases
Schistosoma mansoni
COOCCURRENCE
NETWORKS
COMMUNITY
Infectious Diseases
Trichuris
Parasite co-infection
Ancylostoma duodenale
Neglected tropical diseases
Regression Analysis
Female
Ascaris lumbricoides
Life Sciences & Biomedicine
Adolescent
030231 tropical medicine
Regression dilution
Neglected tropical disease
Conditional random fields
Necator americanus
1117 Public Health and Health Services
lcsh:Infectious and parasitic diseases
03 medical and health sciences
Environmental health
Tropical Medicine
HOOKWORM
TRANSMITTED HELMINTH INFECTIONS
Parasitic Diseases
Animals
Humans
lcsh:RC109-216
ELIMINATION
Science & Technology
Research
Rwanda
biology.organism_classification
Schistosomiasis mansoni
GEOSTATISTICAL PREDICTION
030104 developmental biology
Trichuris trichiura
Parasitology
Zdroj: Parasites & Vectors
Parasites & Vectors, Vol 13, Iss 1, Pp 1-16 (2020)
ISSN: 1756-3305
Popis: 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 parasites; and (iii) quantifying how these associations change across landscapes. Methods We built a spatial CRF to estimate infection risks for Ascaris lumbricoides, Trichuris trichiura, hookworms (Ancylostoma duodenale and Necator americanus) and Schistosoma mansoni using data from a national survey of Rwandan schoolchildren. We used an ensemble learning approach to generate spatial predictions by simulating from the CRF’s posterior distribution with a multivariate boosted regression tree that captured non-linear relationships between predictors and covariance in infection risks. This CRF ensemble was compared against single parasite gradient boosted machines to assess each model’s performance and prediction uncertainty. Results Parasite co-infections were common, with 19.57% of children infected with at least two parasites. The CRF ensemble achieved higher predictive power than single-parasite models by improving estimates of co-infection prevalence at the individual level and classifying schools into World Health Organization treatment categories with greater accuracy. The CRF uncovered important environmental and demographic predictors of parasite infection probabilities. Yet even after capturing demographic and environmental risk factors, the presences or absences of other parasites were strong predictors of individual-level infection risk. Spatial predictions delineated high-risk regions in need of anthelminthic treatment interventions, including areas with higher than expected co-infection prevalence. Conclusions Monitoring studies routinely screen for multiple parasites, yet statistical models generally ignore this multivariate data when assessing risk factors and designing treatment guidelines. Multivariate approaches can be instrumental in the global effort to reduce and eventually eliminate neglected helminth infections in developing countries.
Databáze: OpenAIRE
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