Abstract
Using long-term data on incidences of Lyme disease and tickborne encephalitis, we showed that the dynamics of both diseases in central Europe are predictable from rodent host densities and climate indices. Our approach offers a simple and effective tool to predict a tickborne disease risk 1 year in advance.
Highlights
Using long-term data on incidences of Lyme disease and tickborne encephalitis, we showed that the dynamics of both diseases in central Europe are predictable from rodent host densities and climate indices
We studied interannual variation in incidences of 2 tickborne diseases (TBDs), Lyme disease (LD) and tick-borne encephalitis (TBE)
Our results agree with evidence from North America that the number of I. scapularis nymphs can be predicted by small rodent density from the preceding year [7,8]
Summary
Using long-term data on incidences of Lyme disease and tickborne encephalitis, we showed that the dynamics of both diseases in central Europe are predictable from rodent host densities and climate indices. Cross-correlation analysis revealed strong positive correlations between incidences in the Czech Republic in year t and vole densities in t – 1 and negative correlations between the annual NAO index in t – 1 (Appendix Figures 2–4). By fitting autoregressive linear models, we found strong evidence that vole abundance in year t – 1 and the annual NAO index in t – 1 are key to predicting LD incidences during year t in the Czech Republic (Table 1); the final model predicted observed incidence with reasonable accuracy (Appendix Figure 5). Cross-correlations showed that TBE incidence was strongly positively correlated with
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