Evidence map›Paper›PMID 41872906›Full record

ArticleEnvironmental health : a global access science source2026

Projecting tick-borne encephalitis risk in Sweden under climate change scenarios: a high-resolution spatio-temporal modeling approach.

Maquins Odhiambo Sewe, Jonas Wallin, Joacim Rocklöv, Shiyu Wang, Torben Koenigk, Jan C Semenza

Abstract read
In one paragraph

Article in Environmental health : a global access science source, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Maquins Odhiambo SeweDepartment of Epidemiology and Global Health, Norrland University Hospital, Umeå University, 23, Destination F52,Elsa Bolins Gränd 6, 5th floor, Umeå, 90185, Sweden.
Jonas WallinDepartment of Statistics, Lund university, Lund, Sweden.
Joacim RocklövDepartment of Epidemiology and Global Health, Norrland University Hospital, Umeå University, 23, Destination F52,Elsa Bolins Gränd 6, 5th floor, Umeå, 90185, Sweden.
Shiyu WangSwedish Meteorological and Hydrological Institute, Norrköping, Sweden.
Torben KoenigkSwedish Meteorological and Hydrological Institute, Norrköping, Sweden.
Jan C SemenzaDepartment of Epidemiology and Global Health, Norrland University Hospital, Umeå University, 23, Destination F52,Elsa Bolins Gränd 6, 5th floor, Umeå, 90185, Sweden. jan.semenza@lateralPHC.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTick-borne encephalitis (TBE) is a serious vector-borne neurological disease in Europe, with a complex transmission cycle involving ticks of genus Ixodes, animal hosts, environmental and climatic determinants.

methodsWe modelled annual Geocoded Swedish TBE case data for the period 2005–2023 as a log-Gaussian Cox process in relation to population, environmental and climate data, and wildlife citizen science reports at high spatial resolution. We used the computationally efficient Integrated Nested Laplace Approximation (INLA) and projected the future TBE incidence using fifteen regional climate models.

resultsThe covariates significantly associated with TBE incidence in Sweden, ranked based on predictive capacity, were mean temperature, population density, habitat richness, forest cover, precipitation, relative humidity and roe deer density. Specifically, mean temperature above 12° C degrees in the third quarter of the previous year, habitat richness, precipitation in the third quarter, and higher roe deer density were associated with increased TBE risk. The model performed well on testing data, excluded from model building, demonstrating high predictive accuracy in TBE-endemic areas compared to observed data. Our projections indicate TBE cases will increase by 69% under low emissions (RCP2.6) and 121% under high emissions (RCP8.5) by the 2090s, relative to 2014–2023.

conclusionThe TBE incidence is projected to rise substantially, even under lower emission scenarios. Our findings highlight the growing influence of climate change on TBE transmission in Sweden and provide actionable evidence to inform surveillance, vaccination strategies, and long-term public health planning. Citizen science initiatives and risk maps can help focus resources on areas most vulnerable to transmission. More broadly, the integration of climate models with high-resolution epidemiological data, offers a template for anticipating climate-sensitive vector-borne diseases. Proactive, evidence-based interventions are essential to mitigate the growing health burden posed by TBE in Sweden and beyond.

Indexed as

Climate ChangeEncephalitis, Tick-BorneAnimalsDeerHumansIncidenceSpatio-Temporal AnalysisSweden

Identifiers

PMID41872906
PMCPMC13020375

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.