Machine learning to improve the understanding of rabies epidemiology in low surveillance settings

Abstract In low and middle-income countries, a large proportion of animal rabies investigations end without a conclusive diagnosis leading to epidemiologic interpretations informed by clinical, rather than laboratory data. We compared Extreme Gradient Boosting (XGB) with Logistic Regression (LR) for...

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Bibliographic Details
Main Authors:	Ravikiran Keshavamurthy, Cassandra Boutelle, Yoshinori Nakazawa, Haim Joseph, Dady W. Joseph, Pierre Dilius, Andrew D. Gibson, Ryan M. Wallace
Format:	Article
Language:	English
Published:	Nature Portfolio 2024-10-01
Series:	Scientific Reports
Subjects:	Rabies epidemiology Prediction Machine learning Extreme gradient boosting Risk stratification Zoonotic disease surveillance
Online Access:	https://doi.org/10.1038/s41598-024-76089-3
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Machine learning to improve the understanding of rabies epidemiology in low surveillance settings

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