Spatio-temporal machine learning for multi-horizon prediction of bluetongue outbreaks
- 1Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
- 2School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, United Kingdom
- 3Agri-Food and Biosciences Institute (AFBI), Belfast, United Kingdom
Abstract
Reliable early warning of infectious disease outbreaks remains a major challenge for surveillance systems, particularly for vector-borne pathogens whose transmission depends on interactions among hosts, vectors, and climate-sensitive environmental conditions. Data-driven forecasting offers a promising approach for predicting outbreak risk using surveillance and environmental data. This study develops a logit-weighted ensemble (LWE), a machine-learning framework that predicts outbreak occurrence 1–6 months ahead at the administrative unit–month scale using routinely available outbreak notifications and gridded climate data. Bluetongue virus (BTV), an arbovirus of ruminants transmitted by Culicoides biting midges, provides a well-characterised system in which transmission is strongly shaped by climate, making it a useful system for applying and testing this approach. The framework is evaluated using surveillance data collected between 2005 and 2024 from France, Greece, and Italy, selected for their long-running and high-quality outbreak surveillance records. Across all three countries, the LWE achieved the strongest and most stable predictive performance under a recall-focused evaluation that prioritises correctly identifying outbreak months. It outperformed or matched 14 benchmark models, with differences becoming more pronounced at longer lead times (month +3 onward), when predictions are more uncertain and outbreaks are relatively rare. Predictability varied across countries, with the highest performance in Greece, strong performance in France, and lower, more variable performance in Italy, reflecting differences in how consistently outbreaks occur and spread across regions. Overall, the results demonstrate that horizon-aware, climate-informed forecasting can reliably identify months and locations at elevated risk of outbreak occurrence up to six months in advance, supporting surveillance planning and preparedness across heterogeneous European settings. The ensemble framework provides a robust and portable strategy for outbreak prediction using routinely collected surveillance and environmental data. Author summary: Predicting infectious disease outbreaks before they occur remains a major challenge, particularly for diseases influenced by environmental conditions. In this study, we focus on bluetongue, a viral disease of livestock transmitted by biting midges, where transmission is strongly affected by climate and seasonal patterns. We develop a method that uses routinely collected outbreak reports and climate data to estimate where and when outbreaks are more likely to occur, up to six months in advance. We apply this approach across three European countries with a history of bluetongue outbreaks. We find that combining climate information with recent outbreak patterns can provide useful early signals of increased risk. Predictions are most accurate at shorter timeframes, but longer-range forecasts can still support planning and preparedness. Because our approach uses widely available data, it could be applied in other regions or to similar environmentally driven diseases. However, it does not include factors such as vaccination, animal movement, or detailed information on vector populations, which may also influence how outbreaks develop.
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BibTeX
@article{devlin2026spatiotemporal,
title = {{Spatio-temporal machine learning for multi-horizon prediction of bluetongue outbreaks}},
author = {Lucy M. Devlin and Phi H. Nguyen and Ross N. Cuthbert and Phu Ngoc Doan and Viet Hung Tran and Zichi Zhang and Archie K. Murchie and Connor G. G. Bamford and Jaimie T. A. Dick and Eric R. Morgan and Thai S. Mai},
journal = {PLOS Computational Biology},
year = {2026},
month = oct,
doi = {10.1371/journal.pcbi.1014870},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014870},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Spatio-temporal machine learning for multi-horizon prediction of bluetongue outbreaks
AU - Lucy M. Devlin
AU - Phi H. Nguyen
AU - Ross N. Cuthbert
AU - Phu Ngoc Doan
AU - Viet Hung Tran
AU - Zichi Zhang
AU - Archie K. Murchie
AU - Connor G. G. Bamford
AU - Jaimie T. A. Dick
AU - Eric R. Morgan
AU - Thai S. Mai
PY - 2026
DA - 2026/10/21
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014870
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014870
AB - Reliable early warning of infectious disease outbreaks remains a major challenge for surveillance systems, particularly for vector-borne pathogens whose transmission depends on interactions among hosts, vectors, and climate-sensitive environmental conditions. Data-driven forecasting offers a promising approach for predicting outbreak risk using surveillance and environmental data. This study develops a logit-weighted ensemble (LWE), a machine-learning framework that predicts outbreak occurrence 1–6 months ahead at the administrative unit–month scale using routinely available outbreak notifications and gridded climate data. Bluetongue virus (BTV), an arbovirus of ruminants transmitted by Culicoides biting midges, provides a well-characterised system in which transmission is strongly shaped by climate, making it a useful system for applying and testing this approach. The framework is evaluated using surveillance data collected between 2005 and 2024 from France, Greece, and Italy, selected for their long-running and high-quality outbreak surveillance records. Across all three countries, the LWE achieved the strongest and most stable predictive performance under a recall-focused evaluation that prioritises correctly identifying outbreak months. It outperformed or matched 14 benchmark models, with differences becoming more pronounced at longer lead times (month +3 onward), when predictions are more uncertain and outbreaks are relatively rare. Predictability varied across countries, with the highest performance in Greece, strong performance in France, and lower, more variable performance in Italy, reflecting differences in how consistently outbreaks occur and spread across regions. Overall, the results demonstrate that horizon-aware, climate-informed forecasting can reliably identify months and locations at elevated risk of outbreak occurrence up to six months in advance, supporting surveillance planning and preparedness across heterogeneous European settings. The ensemble framework provides a robust and portable strategy for outbreak prediction using routinely collected surveillance and environmental data. Author summary: Predicting infectious disease outbreaks before they occur remains a major challenge, particularly for diseases influenced by environmental conditions. In this study, we focus on bluetongue, a viral disease of livestock transmitted by biting midges, where transmission is strongly affected by climate and seasonal patterns. We develop a method that uses routinely collected outbreak reports and climate data to estimate where and when outbreaks are more likely to occur, up to six months in advance. We apply this approach across three European countries with a history of bluetongue outbreaks. We find that combining climate information with recent outbreak patterns can provide useful early signals of increased risk. Predictions are most accurate at shorter timeframes, but longer-range forecasts can still support planning and preparedness. Because our approach uses widely available data, it could be applied in other regions or to similar environmentally driven diseases. However, it does not include factors such as vaccination, animal movement, or detailed information on vector populations, which may also influence how outbreaks develop.
SN - 1553-7358
ER -