Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.

ABSTRACT: In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance in

SUBMITTER: Keshavamurthy R 

PROVIDER: S-EPMC10329696 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature