Modelling the seasonality of Lyme disease risk and the potential impacts of a warming climate within the heterogeneous landscapes of Scotland

Abstract

Lyme disease is the most prevalent vector-borne disease in the temperate Northern Hemisphere. The abundance of infected nymphal ticks is commonly used as a Lyme disease risk indicator. Temperature can influence the dynamics of disease by shaping the activity and development of ticks and, hence, altering the contact pattern and pathogen transmission between ticks and their host animals. A mechanistic, agent-based model was developed to study the temperature-driven seasonality of Ixodes ricinus ticks and transmission of Borrelia burgdorferi sensu lato across mainland Scotland. Based on 12-year averaged temperature surfaces, our model predicted that Lyme disease risk currently peaks in autumn, approximately six weeks after the temperature peak. The risk was predicted to decrease with increasing altitude. Increases in temperature were predicted to prolong the duration of the tick questing season and expand the risk area to higher altitudinal and latitudinal regions. These predicted impacts on tick population ecology may be expected to lead to greater tick-host contacts under climate warming and, hence, greater risks of pathogen transmission. The model is useful in improving understanding of the spatial determinants and system mechanisms of Lyme disease pathogen transmission and its sensitivity to temperature changes.

Keywords: Borrelia burgdorferi sensu lato; Ixodes ricinus; agent-based model; climate warming; environmental health hazard; spatio-temporal dynamics.

Figure 1.

Model conceptual framework: interactions between ticks, hosts, pathogens and landscape. (Online version in colour.)

Figure 2.

Simulated spatial dynamics of Lyme disease risk in mainland Scotland. (a) Predicted spatial pattern of relative peak DIN (density of nymphs per square kilometre as per cent of total) and (b–d) predicted influence of temperature increase on peak DIN on an increasing gradient of elevation (averaging interval of 1 m), annual average temperature (averaging interval of 0.1°C) and deer density (averaging interval of 0.1 heads km⁻²). (Online version in colour.)

Figure 3.

Predicted influence of temperature increase on the simulated seasonal variation of infected nymph abundance (summed DIN in woodland areas) in each AFRC (agriculture, food and rural communities) area office region. (Online version in colour.)