Autor(es):
Carvalho, Bruno M. ; Maia, Carla ; Courtenay, Orin ; Llabrés-Brustenga, Alba ; Lotto Batista, Martín ; Moirano, Giovenale ; van Daalen, Kim R. ; Semenza, Jan C. ; Lowe, Rachel
Data: 2024
Identificador Persistente: http://hdl.handle.net/10362/170440
Origem: Repositório Institucional da UNL
Assunto(s): Climate change; Indicator; Infectious diseases; Leishmaniasis; Machine learning; Internal Medicine; Health Policy; Parasitology; SDG 3 - Good Health and Well-being; SDG 13 - Climate Action
Descrição
Funding Information: European Union Horizon Europe Research and Innovation Programme (European Climate-Health Cluster), United Kingdom Research and Innovation.The authors acknowledge funding from the European Union's Horizon Europe Research And Innovation Programme under grant agreement No. 101057554 (Horizon Europe project IDAlert, https://idalertproject.eu) and No. 101057690 (CLIMOS, https://climos-project.eu). IDAlert and CLIMOS are part of the EU climate change and health cluster (https://climate-health.eu). The work of OC was supported by the UK Research and Innovation (UKRI grant 10038150). We thank the Italian Ministry of Health-Directorate General of Health Programming for sharing Hospital Discharge Register records at the province level. Funding Information: The authors acknowledge funding from the European Union\u2019s Horizon Europe research and innovation programme under grant agreement No. 101057554 (Horizon Europe project IDAlert, https://idalertproject.eu ) and No. 101057690 (CLIMOS, https://climos-project.eu ). IDAlert and CLIMOS are part of the EU climate change and health cluster ( https://climate-health.eu ). The work of OC was supported by the UK Research and Innovation (UKRI grant 10038150). Publisher Copyright: © 2024 The Author(s)
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.
