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A climatic suitabil...
A climatic suitability indicator to support Leishmania infantum surveillance in Europe : a modelling study
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- Carvalho, Bruno M. (författare)
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
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- Maia, Carla (författare)
- Global Health and Tropical Medicine, Associate Laboratory in Translation and Innovation Towards Global Health, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa, Lisboa, Portugal
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- Courtenay, Orin (författare)
- The Zeeman Institute and School of Life Sciences, University of Warwick, Coventry, United Kingdom
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- Llabrés-Brustenga, Alba (författare)
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
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- Lotto Batista, Martín (författare)
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
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- Moirano, Giovenale (författare)
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
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- van Daalen, Kim R. (författare)
- Barcelona Supercomputing Center (BSC), Barcelona, Spain; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
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- Semenza, Jan C. (författare)
- Umeå universitet,Avdelningen för hållbar hälsa,Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany
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- Lowe, Rachel (författare)
- Barcelona Supercomputing Center (BSC), Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain; Centre on Climate Change & Planetary Health and Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
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(creator_code:org_t)
- Elsevier, 2024
- 2024
- Engelska.
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Ingår i: The Lancet Regional Health. - : Elsevier. - 2666-7762. ; 43
- Relaterad länk:
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https://doi.org/10.1...
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https://umu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Background: Leishmaniases are neglected diseases transmitted by sand flies. They disproportionately affect vulnerable groups globally. Understanding the relationship between climate and disease transmission allows the development of relevant decision-support tools for public health policy and surveillance. The aim of this modelling study was to develop an indicator that tracks climatic suitability for Leishmania infantum transmission in Europe at the subnational level.Methods: Historical records of sand fly vectors, human leishmaniasis, bioclimatic indicators, and environmental variables were integrated in a machine learning framework (XGBoost) to predict suitability in two past periods (2001–2010 and 2011–2020). We further assessed if predictions were associated with human and animal disease data from selected countries (France, Greece, Italy, Portugal, and Spain).Findings: An increase in the number of climatically suitable regions for leishmaniasis was detected, especially in southern and eastern countries, coupled with a northward expansion towards central Europe. The final model had excellent predictive ability (AUC = 0.970 [0.947–0.993]), and the suitability predictions were positively associated with human leishmaniasis incidence and canine seroprevalence for Leishmania.Interpretation: This study demonstrates how key epidemiological data can be combined with open-source climatic and environmental information to develop an indicator that effectively tracks spatiotemporal changes in climatic suitability and disease risk. The positive association between the model predictions and human disease incidence demonstrates that this indicator could help target leishmaniasis surveillance to transmission hotspots.Funding: European Union Horizon Europe Research and Innovation Programme (European Climate-Health Cluster), United Kingdom Research and Innovation.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
Nyckelord
- Climate change
- Indicator
- Infectious diseases
- Leishmaniasis
- Machine learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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