Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in GermanyResearch in context
Summary: Background: Lyme borreliosis (LB) is a predominant vector-borne disease in Europe, with Germany reporting endemic circulation for at least the past two decades. Climatic and environmental conditions are key drivers of tick activity, and human exposure to tick bites. Understanding the clima...
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Elsevier
2025-05-01
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| Series: | EBioMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396425001458 |
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| author | Martín Lotto Batista Bruno Carvalho Rory Gibb Balakrishnan Solaraju-Murali Stefan Flasche Stefanie Castell Stéphane Ghozzi Rachel Lowe |
| author_facet | Martín Lotto Batista Bruno Carvalho Rory Gibb Balakrishnan Solaraju-Murali Stefan Flasche Stefanie Castell Stéphane Ghozzi Rachel Lowe |
| author_sort | Martín Lotto Batista |
| collection | DOAJ |
| description | Summary: Background: Lyme borreliosis (LB) is a predominant vector-borne disease in Europe, with Germany reporting endemic circulation for at least the past two decades. Climatic and environmental conditions are key drivers of tick activity, and human exposure to tick bites. Understanding the climatic and environmental factors driving LB dynamics can help devise decision-support tools to guide interventions and adaptation strategies. Methods: Using a Bayesian modelling framework, we assessed the delayed and nonlinear associations between climate variation and land use change and monthly LB case counts from the German national notification system at a district level from 2009 to 2022. We evaluated the predictive performance of our model and then predicted risk trends in states without mandatory notification. We then used the fitted risk function for maximum temperature to assess long-term trends in relative risk since the 1950s. Findings: Our analyses revealed that climate and environmental factors are positively associated with LB cases reported to the national notification system. Maximum temperature between 10.5 °C and 26.3 °C two to four months prior, relative humidity levels exceeding 78.8% six months prior, and exceptionally wet conditions accumulated over three months, lagged by one month, were associated with an increased risk of LB. The effect of relative humidity was only relevant in areas suitable for deer population, potentially linked to tick survival. Predictions from our model identified significant increasing trends in Schleswig–Holstein, Hamburg, and Lower Saxony, three states without mandatory case notification. We also observed an increasing trend in maximum-temperature related LB relative risk in all Federal States, with the largest percentage change in the period 2013–2022 in northern districts, compared to 1951–1970. Interpretation: Our study underscores the role of climatic variables as potential drivers of LB risk in Germany. We identified optimal conditions that may be related to human exposure and tick survival and detected long-term upward trends nationwide, including in areas without mandatory notification. This decision-support modelling framework emphasises the added value of expanding LB surveillance in Germany and across Europe to address the emerging risk of tick-borne infectious diseases. Funding: Helmholtz Association, Helmholtz Climate Initiative, Wellcome Trust, Royal Society, and Horizon Europe. |
| format | Article |
| id | doaj-art-ed036ffad7ee4807a400fca8baaffe0f |
| institution | OA Journals |
| issn | 2352-3964 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj-art-ed036ffad7ee4807a400fca8baaffe0f2025-08-20T02:20:09ZengElsevierEBioMedicine2352-39642025-05-0111510570110.1016/j.ebiom.2025.105701Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in GermanyResearch in contextMartín Lotto Batista0Bruno Carvalho1Rory Gibb2Balakrishnan Solaraju-Murali3Stefan Flasche4Stefanie Castell5Stéphane Ghozzi6Rachel Lowe7Barcelona Supercomputing Center (BSC), Barcelona, Spain; Department for Epidemiology, Helmholtz Centre for Infection Research, Brunswick, Germany; Corresponding author. Barcelona Supercomputing Center (BSC), Plaça d’Eusebi Güell 1-3, Barcelona 08034, Spain.Barcelona Supercomputing Center (BSC), Barcelona, SpainCentre for Biodiversity and Environment Research, Department of Genetics, Evolution & Environment, University College London, London, United KingdomBarcelona Supercomputing Center (BSC), Barcelona, SpainCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom; Centre for Global Health, Charite, Universitaetsmedizin Berlin, Berlin, GermanyDepartment for Epidemiology, Helmholtz Centre for Infection Research, Brunswick, GermanyDepartment for Epidemiology, Helmholtz Centre for Infection Research, Brunswick, GermanyBarcelona Supercomputing Center (BSC), Barcelona, Spain; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom; Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain; Corresponding author. Barcelona Supercomputing Center (BSC), Plaça d’Eusebi Güell 1-3, Barcelona 08034, Spain.Summary: Background: Lyme borreliosis (LB) is a predominant vector-borne disease in Europe, with Germany reporting endemic circulation for at least the past two decades. Climatic and environmental conditions are key drivers of tick activity, and human exposure to tick bites. Understanding the climatic and environmental factors driving LB dynamics can help devise decision-support tools to guide interventions and adaptation strategies. Methods: Using a Bayesian modelling framework, we assessed the delayed and nonlinear associations between climate variation and land use change and monthly LB case counts from the German national notification system at a district level from 2009 to 2022. We evaluated the predictive performance of our model and then predicted risk trends in states without mandatory notification. We then used the fitted risk function for maximum temperature to assess long-term trends in relative risk since the 1950s. Findings: Our analyses revealed that climate and environmental factors are positively associated with LB cases reported to the national notification system. Maximum temperature between 10.5 °C and 26.3 °C two to four months prior, relative humidity levels exceeding 78.8% six months prior, and exceptionally wet conditions accumulated over three months, lagged by one month, were associated with an increased risk of LB. The effect of relative humidity was only relevant in areas suitable for deer population, potentially linked to tick survival. Predictions from our model identified significant increasing trends in Schleswig–Holstein, Hamburg, and Lower Saxony, three states without mandatory case notification. We also observed an increasing trend in maximum-temperature related LB relative risk in all Federal States, with the largest percentage change in the period 2013–2022 in northern districts, compared to 1951–1970. Interpretation: Our study underscores the role of climatic variables as potential drivers of LB risk in Germany. We identified optimal conditions that may be related to human exposure and tick survival and detected long-term upward trends nationwide, including in areas without mandatory notification. This decision-support modelling framework emphasises the added value of expanding LB surveillance in Germany and across Europe to address the emerging risk of tick-borne infectious diseases. Funding: Helmholtz Association, Helmholtz Climate Initiative, Wellcome Trust, Royal Society, and Horizon Europe.http://www.sciencedirect.com/science/article/pii/S2352396425001458Lyme borreliosisClimate changeBayesian modellingTick-borne diseasesGermany |
| spellingShingle | Martín Lotto Batista Bruno Carvalho Rory Gibb Balakrishnan Solaraju-Murali Stefan Flasche Stefanie Castell Stéphane Ghozzi Rachel Lowe Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in GermanyResearch in context EBioMedicine Lyme borreliosis Climate change Bayesian modelling Tick-borne diseases Germany |
| title | Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in GermanyResearch in context |
| title_full | Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in GermanyResearch in context |
| title_fullStr | Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in GermanyResearch in context |
| title_full_unstemmed | Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in GermanyResearch in context |
| title_short | Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in GermanyResearch in context |
| title_sort | modelling the impact of climate and the environment on the spatiotemporal dynamics of lyme borreliosis in germanyresearch in context |
| topic | Lyme borreliosis Climate change Bayesian modelling Tick-borne diseases Germany |
| url | http://www.sciencedirect.com/science/article/pii/S2352396425001458 |
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