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

Keshavamurthy, Ravikiran; Charles, Lauren E.

doi:10.1038/s41598-023-38074-0

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

Journal Article · Sat Jul 08 00:00:00 EDT 2023 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-023-38074-0· OSTI ID:2221787

Keshavamurthy, Ravikiran ^[1]; Charles, Lauren E. ^[1]

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Washington State University, Pullman, WA (United States)

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 information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE; Defense Threat Reduction Agency (DTRA)

Grant/Contract Number:: AC05-76RL01830; CB11029

OSTI ID:: 2221787

Report Number(s):: PNNL-SA-192685

Journal Information:: Scientific Reports, Vol. 13, Issue 1; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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