Towards proactive disease control: predicting sand fly population dynamics over Europe for enhanced public health outcomes
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866901228691128320 |
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| author | Soheili, Majid Rakovec, Oldrich Berriatua, Eduardo Modiri, Ehsan Blesic, Suzana Samaniego Eguiguren, Luis Eduardo |
| author_facet | Soheili, Majid Rakovec, Oldrich Berriatua, Eduardo Modiri, Ehsan Blesic, Suzana Samaniego Eguiguren, Luis Eduardo |
| contents | <p><strong>Abstract</strong><br>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.<br>Despite ongoing research, there is no registered vaccine, and treatment options remain limited due to drug toxicity and emerging resistance.<br>The geographical range of sand flies has expanded from the Mediterranean region toward Northern Europe, exacerbating public health challenges.<br>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.<br>Recent advancements in climate modeling, data assimilation, and remote sensing offer opportunities to enhance these models.<br>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.<br>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5194_egusphere-egu25-13586 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Towards proactive disease control: predicting sand fly population dynamics over Europe for enhanced public health outcomes Soheili, Majid Rakovec, Oldrich Berriatua, Eduardo Modiri, Ehsan Blesic, Suzana Samaniego Eguiguren, Luis Eduardo Early warning system <p><strong>Abstract</strong><br>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.<br>Despite ongoing research, there is no registered vaccine, and treatment options remain limited due to drug toxicity and emerging resistance.<br>The geographical range of sand flies has expanded from the Mediterranean region toward Northern Europe, exacerbating public health challenges.<br>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.<br>Recent advancements in climate modeling, data assimilation, and remote sensing offer opportunities to enhance these models.<br>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.<br>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.</p> |
| title | Towards proactive disease control: predicting sand fly population dynamics over Europe for enhanced public health outcomes |
| topic | Early warning system |
| url | https://doi.org/10.5194/egusphere-egu25-13586 |