arXiv Machine Learning By Aniruddha Adiga, Jingyuan Chou, Gursharn Kaur, Andrew Warren, Srinivasan Venkatramanan, Baltazar Espinoza, Bryan Lewis, Justin Crow, Alexandra Lorentz, Rekha Singh, Madhav Marathe

EpiFlow: A framework for improving the utility of wastewater signals for disease forecasting

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arXiv:2608. 06671v1 Announce Type: new Abstract: Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks.

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arXiv Machine Learning
Sep 23

Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series

The paper proposes an event-based approach to predict transitions into vineyard disease‑risk periods within a 3–7 day window, rather than daily disease status. It defines events only after a minimum disease‑free gap to reduce label fragmentation and uses multi‑year agro‑meteorological data to capture humidity, rainfall, temperature, and seasonal patterns. Experiments with XGBoost, LSTM, and TCN models show that this formulation improves short‑horizon warning, highlighting trade‑offs among recall, lead time, and false alerts.

By Ivica Dimitrovski, Ivan Kitanovski, Danco Davcev, Slobodan Kalajdziski, Kosta Mitreski