- 1Helmholtz Centre - UFZ, Department Computational Hydrosystems, Leipzig, Germany (majid.soheili@ufz.de, ehsan.modiri@ufz.de, luis.samaniego@ufz.de)
- 2Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Czech Republic (oldrich.rakovec@ufz.de)
- 3Departamento de Sanidad Animal, Universidad de Murcia, Murcia, Spain (berriatu@um.es)
- 4Institute for Medical Research, University of Belgrade, Belgrade, Serbia (suzana@imi.bg.ac.rs)
- *A full list of authors appears at the end of the abstract
Abstract
Climate change significantly influences the spread of infectious diseases, including leishmaniasis, a vector-borne disease transmitted by infected sand flies. Leishmaniasis affects approximately 12 million people globally, with significant health, economic, and social impacts.
Despite ongoing research, there is no registered vaccine, and treatment options remain limited due to drug toxicity and emerging resistance.
The geographical range of sand flies has expanded from the Mediterranean region toward Northern Europe, exacerbating public health challenges.
Current prediction models for sand fly populations are hindered by limitations in temporal and spatial scales, high data collection costs, and highly skewed observation data.
Recent advancements in climate modeling, data assimilation, and remote sensing offer opportunities to enhance these models.
This study utilizes the largest observational dataset on sand flies from the European CLIMOS project (https://climos-project.eu), incorporating data from VectorNet and EDENext, combined with high-resolution climate and hydrological datasets, to create a sand fly population prediction model named Sand Flies Extreme Prediction Population (FEPO). By enhancing predictive accuracy and speed, it can facilitate targeted public health interventions while also strengthening strategies for climate change adaptation.
The initial findings indicate that the proposed model achieves a mean absolute error that is 12% lower than the classical regression approach when validated against observational data. Moreover, the FEPO model successfully maps the distribution of sand fly species responsible for transmitting leishmaniasis across Europe with high spatial resolution.
Acknowledgments:
Funding:
The CLIMOS consortium is co-funded by the European Commission grant 101057690 and UKRI grants 10038150 and 10039289.
The six Horizon Europe projects, BlueAdapt, CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, form the Climate Change and Health Cluster. \\
Sand Flies Data Contribution:
- EDENext: The data for EDENext was obtained from the Palebludata website (https://www.palebludata.com).
- VectorNet: The data for VectorNet was obtained from the ECDC.
Oldrich Rakovec, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Czech Republic (oldrich.rakovec@ufz.de) Eduardo Berriatua, Departamento de Sanidad Animal, Universidad de Murcia, Murcia, Spain (berriatu@um.es) Ehsan Modiri, Helmholtz Centre - UFZ, Department Computational Hydrosystems, Leipzig, Germany (ehsan.modiri@ufz.de) Suzana Blesic, Institute for Medical Research, University of Belgrade, Belgrade, Serbia (suzana@imi.bg.ac.rs) Jorian Prudhomme, University of Reims Champagne-Ardenne (jorian.prudhomme@univ-reims.fr) Edwin Kniha, Medical University of Vienna (edwin.kniha@meduniwien.ac.at) Kirami Olgen, Department of Geography, Ege University (kirami.olgen@ege.edu.tr) Gabriella Gaglio, Universita’ degli Studi di Messina (gabriella.gaglio@unime.it) Emanuele Brianti, Universita’ degli Studi di Messina (ebrianti@unime.it) Ognyan Mikov, National Center of Infectious and Parasitic Diseases, Sofia (mikov@ncipd.org) Oscar Kirstein, Israeli Ministry of Health, Jerusalem (oscar.kirstein@moh.gov.il) Vladimir Ivovic, University of Primorska, Koper (vladimir.ivovic@famnit.upr.si) Maribel Jimenez, Instituto de Salud Carlos III (mjimenez@isciii.es) Ricardo Molina, Instituto de Salud Carlos III (rmolina@isciii.es) Carla Maia, University NOVA of Lisbon (CarlaMaia@ihmt.unl.pt) Ozge Erisoz, Hacettepe University (ozgeerisoz@yahoo.com) Maria Antoniou, School of Medicine, University of Crete (antoniou@uoc.gr) Gioia Bongiorno, Istituto Superiore di Sanità (gioia.bongiorno@iss.it) Michaelakis Antonios, Benaki Phytopathological Institute (a.michaelakis@bpi.gr) Bisia Marina, Benaki Phytopathological Institute (m.bisia@bpi.gr) Luis Samaniego, Helmholtz Centre - UFZ, Department Computational Hydrosystems, Leipzig, Germany (luis.samaniego@ufz.de)
How to cite: Soheili, M., Rakovec, O., Berriatua, E., modiri, E., Blesic, S., and Samaniego, L. and the Majid Soheili: Towards proactive disease control: predicting sand fly population dynamics over Europe for enhanced public health outcomes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13586, https://doi.org/10.5194/egusphere-egu25-13586, 2025.
