Mining online behavioural data to measure zoonotic spillover risk
Abstract
Efforts to predict zoonotic disease dynamics rarely directly quantify the fine-scale animal-human interactions that drive spillover. Public digital data (i.e., iEcology) could capture these behaviours at large scales, across time and space. Further, recent statistical advances allow us to semi-automate these processes, providing a framework for continuous exposure monitoring and risk prediction. Using online reviews describing macaque behaviour across southeast Asia, we demonstrate that iEcology provides a usable data source for risky animal-human interactions. We discuss challenges in operationalising these approaches, including ethical considerations and correcting spatial, taxonomic, and socioeconomic bias. In the Big Data era, semi-automated workflows like these could pave the way to dynamically updating risk maps for use in conservation and public health planning and interventions.
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BibTeX
@misc{conchubhair2026mining,
title = {{Mining online behavioural data to measure zoonotic spillover risk}},
author = {Bríon Ó Conchubhair and Maxwell J Farrell and Rory Gibb and Luci Kirkpatrick and David W. Redding and Gregory F Albery},
howpublished = {EcoEvoRxiv},
year = {2026},
month = sep,
doi = {10.32942/x2b38s},
url = {https://ecoevorxiv.org/repository/view/15136/},
note = {CC BY-NC 4.0},
}
RIS
TY - UNPB
TI - Mining online behavioural data to measure zoonotic spillover risk
AU - Bríon Ó Conchubhair
AU - Maxwell J Farrell
AU - Rory Gibb
AU - Luci Kirkpatrick
AU - David W. Redding
AU - Gregory F Albery
PY - 2026
DA - 2026/09/22
PB - EcoEvoRxiv
DO - 10.32942/x2b38s
UR - https://ecoevorxiv.org/repository/view/15136/
AB - Efforts to predict zoonotic disease dynamics rarely directly quantify the fine-scale animal-human interactions that drive spillover. Public digital data (i.e., iEcology) could capture these behaviours at large scales, across time and space. Further, recent statistical advances allow us to semi-automate these processes, providing a framework for continuous exposure monitoring and risk prediction. Using online reviews describing macaque behaviour across southeast Asia, we demonstrate that iEcology provides a usable data source for risky animal-human interactions. We discuss challenges in operationalising these approaches, including ethical considerations and correcting spatial, taxonomic, and socioeconomic bias. In the Big Data era, semi-automated workflows like these could pave the way to dynamically updating risk maps for use in conservation and public health planning and interventions.
ER -