The emergence and shift in seasonality of Lyme borreliosis in Northern Europe

Abstract

Climate change has had a major impact on seasonal weather patterns, resulting in marked phenological changes in a wide range of taxa. However, empirical studies of how changes in seasonality impact the emergence and seasonal dynamics of vector-borne diseases have been limited. Lyme borreliosis, a bacterial infection spread by hard-bodied ticks, is the most common vector-borne disease in the northern hemisphere and has been rapidly increasing in both incidence and geographical distribution in many regions of Europe and North America. By analysis of long-term surveillance data (1995-2019) from across Norway (latitude 57°58'-71°08' N), we demonstrate a marked change in the within-year timing of Lyme borreliosis cases accompanying an increase in the annual number of cases. The seasonal peak in cases is now six weeks earlier than 25 years ago, exceeding seasonal shifts in plant phenology and previous model predictions. The seasonal shift occurred predominantly in the first 10 years of the study period. The concurrent upsurgence in case number and shift in case timing indicate a major change in the Lyme borreliosis disease system over recent decades. This study highlights the potential for climate change to shape the seasonal dynamics of vector-borne disease systems.

Keywords: Lyme borreliosis; Lyme disease; climate change; disease ecology; seasonality; vector-borne zoonoses.

Figure 1.

Key changes in the seasonal and long-term trends of Lyme borreliosis. (a) Map of Norway showing the regional aggregations used in this, and prior, studies [25,34]. The statistical models are fitted to national data, as well as to the South, West and East regions independently. (b) The annual component from the main model for Lyme borreliosis cases fitted to the national data (black) and to regional data for the South (blue), West (yellow) and East (purple). The trends show relative changes in average weekly case totals predicted for each year (relative case intensity). Because the intercept is not included, the trends are not comparable on an absolute scale. (c) The shift in the week of peak spring vegetation greening, measured by NDVI. The points represent the week in which peak greening was observed. The trendline is fitted from a linear model with a basis spline for year, with three degrees of freedom. (d) Predicted peak weeks for Lyme borreliosis cases from the main national model (black) and the regional models fitted to the South, West and East (blue, yellow and purple). Black points represent the annual peaks from the national model, and the corresponding black vertical lines show the 95% credible intervals, quantified by repeated sampling from the posterior distribution. The black curve with a shaded confidence interval is a basis spline with four degrees of freedom fitted to the predicted national peaks.

Figure 2.

Weekly case numbers over the study period (grey points), together with model predictions (green) for the main national model. The yearly trend is shown in black (seasonal effect estimates set to zero), and the seasonal trend is shown in magenta (yearly effect estimates set to zero).