Lyme Disease Risks in Europe under Multiple Uncertain Drivers of Change

Abstract

Background: Debates over whether climate change could lead to the amplification of Lyme disease (LD) risk in the future have received much attention. Although recent large-scale disease mapping studies project an overall increase in Lyme disease risk as the climate warms, such conclusions are based on climate-driven models in which other drivers of change, such as land-use/cover and host population distribution, are less considered.

Objectives: The main objectives were to project the likely future ecological risk patterns of LD in Europe under different assumptions about future socioeconomic and climate conditions and to explore similarity and uncertainty in the projected risks.

Methods: An integrative, spatially explicit modeling study of the ecological risk patterns of LD in Europe was conducted by applying recent advances in process-based modeling of tick-borne diseases, species distribution mapping, and scenarios of land-use/cover change. We drove the model with stakeholder-driven, integrated scenarios of plausible future socioeconomic and climate change [the Shared Socioeconomic Pathway (SSPs) combined with the Representative Concentration Pathways (RCPs)].

Results: The model projections suggest that future temperature increases may not always amplify LD risk: Low emissions scenarios (RCP2.6) combined with a sustainability socioeconomic scenario (SSP1) resulted in reduced LD risk. The greatest increase in risk was projected under intermediate (RCP4.5) rather than high-end (RCP8.5) climate change scenarios. Climate and land-use change were projected to have different roles in shaping the future regional dynamics of risk, with climate warming being likely to cause risk expansion in northern Europe and conversion of forest to agriculture being likely to limit risk in southern Europe.

Conclusions: Projected regional differences in LD risk resulted from mixed effects of temperature, land use, and host distributions, suggesting region-specific and cross-sectoral foci for LD risk management policy. The integrated model provides an improved explanatory tool for the system mechanisms of LD pathogen transmission and how pathogen transmission could respond to combined socioeconomic and climate changes. https://doi.org/10.1289/EHP4615.

Figure 1.

Theoretical framework and key drivers of Lyme disease risk dynamics. Climate change: (1) Climate influences tick phenology and distribution of ticks; (2) climate suitability influences distribution of tick hosts (e.g., deer, rodents, and birds); (3) climate influences growth of plant species and profitability of land, driving land-use/cover change. Socioeconomic change: (4) Socioeconomics influence the demand and preferences for how land is used, which affects conversion between land cover types. Land cover change: (5) Land cover influences host type and abundance as well as microclimate and, hence, (6) distribution of ticks. Host distribution change: (7) Availability of hosts influences tick survival and pathogen transmission.

Figure 2.

The integrative modeling strategy for Lyme disease risk projection. The LYMERISK model is an agent-based model for the ecological risk of Lyme disease, with three interactive agent layers representing ticks, reproduction and transmission host animals, and habitats. BIOMOD is a platform for the ensemble prediction of species’ distributions and is used to project future distribution patterns of key tick host species. The Integrated Assessment Platform for climate change impact assessment is a product of the IMPRESSIONS project and provides projections in habitats and climate conditions.

Figure 3.

Projected changes in disease risk drivers by the 2050s in Europe under the combined scenarios of plausible shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) changes. Projected changes in temperature, forest land cover, and densities of deer and transmission hosts were summarized for each combined scenario by taking averaged values derived from different climate models under different combined scenarios.

Figure 4.

Projected changes in Lyme disease risk by the 2050s in European countries. The extent of high disease risk area in peak season (%, lines in the figure) and mean density of infected nymphal ticks (per ha, closed circles in the figure, with light/dark gray indicating projected decrease/increase) are summarized across combined scenarios of the Shared Socioeconomic Pathway (SSP) and the Representative Concentration pathway (RCP) changes. The countries are classified into four geographical divisions (north, east, west, and south). Country abbreviations: AT, Austria; BE, Belgium; BG, Bulgaria; CH, Switzerland; CZ, Czech Republic; DE, Germany; DK, Denmark; EE, Estonia; ES, Spain; FI, Finland; FR, France; GR, Greece; HU, Hungary; IE, Ireland; IT, Italy; LI, Liechtenstein; LT, Lithuania; LU, Luxembourg; LV, Latvia; NL, Netherlands; NO, Norway; PL, Poland; PT, Portugal; RO, Romania; SE, Sweden; SI, Slovenia; SK, Slovakia; UK, United Kingdom.

Figure 5.

Projected baseline and future Lyme disease risk ranges under SSP1 × RCP2.6 and SSP4 × RCP4.5 scenarios. The projected distribution of risk indicator, DIN (infected nymphal ticks per ha), at the baseline of 2010 in (A) winter and (B) peak season are classified into five levels: null ($\text{DIN}=0$), negligible ($\text{DIN}<82$), low ($82\le \text{DIN}<698$), moderate ($698\le \text{DIN}<\mathrm{1,608}$), and high ($\text{DIN}\ge \mathrm{1,608}$). Among the six combined scenarios considered, the SSP1 × RCP2.6 represents the least risky future in which the low emissions scenario (RCP2.6) is combined with a sustainability socioeconomic scenario (SSP1). Both the (C) winter low-risk range and (D) peak season high-risk range were projected to likely decrease by the 2050s. The blue/red color gradients indicate agreement among projections under different climate models, with darker meaning higher agreement. The patterned blue/red area refers to the baseline projections for comparison. The SSP4 × RCP4.5 is the most risky future in which the intermediate emissions scenario (RCP4.5) is combined with an unequal future of increased socioeconomic disparities (SSP4), under which an overall increase and geographical changes in both (E) winter low-risk range and (F) peak season high-risk range by the 2050s were projected.