Seasonal cycles of the TBE and Lyme borreliosis vector Ixodes ricinus modelled with time-lagged and interval-averaged predictors

Brugger, Katharina; Walter, Melanie; Chiţimia-Dobler, Lidia; Dobler, Gerhard; Rubel, Franz

doi:10.1007/s10493-017-0197-8

Cited by 22 publications

(45 citation statements)

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“…The site as well as the flagging procedure has been described in detail by Brugger et al. ( 2017a ). Here the main focus is on the I. ricinus nymphs as this stage plays an important role in the epidemiology of human infections (Gray 1998 ).…”

Section: Methodsmentioning

confidence: 99%

“…The Haselmühl time series was already used to demonstrate that the seasonal inter-annual fluctuations of tick activity are most affected by time-lagged and temporal averaged variables, such as temperature and precipitation, rather than by contemporaneous variables (Brugger et al. 2017a). Here we use the available time series to quantify the influence of biotic and abiotic variables on the annual nymphal I. ricinus density and to forecast the next year’s density.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting next season’s Ixodes ricinus nymphal density: the example of southern Germany 2018

et al. 2018

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The castor bean tick, Ixodes ricinus (L.) (Ixodida: Ixodidae), is the principal vector of pathogens causing tick-borne encephalitis or Lyme borreliosis in Europe. It is therefore of general interest to make an estimate of the density of I. ricinus for the whole year at the beginning of the tick season. There are two necessary conditions for making a successful prediction: a long homogeneous time series of observed tick density and a clear biological relationship between environmental predictors and tick density. A 9-year time series covering the period 2009–2017 of nymphal I. ricinus flagged at monthly intervals in southern Germany has been used. With the hypothesis that I. ricinus density is triggered by the fructification of the European beech 2 years before, the mean annual temperature of the previous year, and the current mean winter temperature (December–February), a forecast of the annual nymphal tick density has been made. Therefore, a Poisson regression model was generated resulting in an explained variance of 93.4% and an error of ticks per (annual collected ticks/). An independent verification of the forecast for the year 2017 resulted in 187 predicted versus 180 observed nymphs per . For the year 2018 a relatively high number of 443 questing I. ricinus nymphs per is forecasted, i.e., a “good” tick year.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting next season’s Ixodes ricinus nymphal density: the example of southern Germany 2018

et al. 2018

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“…To determine the optimal correlation between TBE and SCAND, so-called cross-correlation maps (CCMs) were used. With CCMs optimal time lags and accumulation periods of predictors can be determined [37]. As known from vector biology, the best correlation between arthropod vectors or disease cases caused by pathogens they transmit and environmental temperature is obtained when temperature data were averaged over the period of the life cycle of the vector.…”

Section: Climate Teleconnectionmentioning

confidence: 99%

Tick-borne encephalitis (TBE) cases are not random: explaining trend, low- and high-frequency oscillations based on the Austrian TBE time series

et al. 2020

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Background: Why human tick-borne encephalitis (TBE) cases differ from year to year, in some years more 100%, has not been clarified, yet. The cause of the increasing or decreasing trends is also controversial. Austria is the only country in Europe where a 40-year TBE time series and an official vaccine coverage time series are available to investigate these open questions. Methods: A series of generalized linear models (GLMs) has been developed to identify demographic and environmental factors associated with the trend and the oscillations of the TBE time series. Both the observed and the predicted TBE time series were subjected to spectral analysis. The resulting power spectra indicate which predictors are responsible for the trend, the high-frequency and the low-frequency oscillations, and with which explained variance they contribute to the TBE oscillations. Results: The increasing trend can be associated with the demography of the increasing human population. The responsible GLM explains 12% of the variance of the TBE time series. The low-frequency oscillations (10 years) are associated with the decadal changes of the large-scale climate in Central Europe. These are well described by the so-called Scandinavian index. This 10-year oscillation cycle is reinforced by the socioeconomic predictor net migration. Considering the net migration and the Scandinavian index increases the explained variance of the GLM to 44%. The high-frequency oscillations (2-3 years) are associated with fluctuations of the natural TBE transmission cycle between small mammals and ticks, which are driven by beech fructification. Considering also fructification 2 years prior explains 64% of the variance of the TBE time series. Additionally, annual sunshine duration as predictor for the human outdoor activity increases the explained variance to 70%. Conclusions: The GLMs presented here provide the basis for annual TBE forecasts, which were mainly determined by beech fructification. A total of 3 of the 5 years with full fructification, resulting in high TBE case numbers 2 years later, occurred after 2010. The effects of climate change are therefore not visible through a direct correlation of the TBE cases with rising temperatures, but indirectly via the increased frequency of mast seeding.

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“…which occurred several months earlier. An example is I. ricinus in Germany, where temperature and relative humidity determined vector densities 3-6 months later [41].…”

Section: Relationships Between Environmental Drivers and Both Seasonamentioning

confidence: 99%

The importance of vector abundance and seasonality

William¹,

Alten

Balenghien

et al. 2018

EFSA Supporting Publications

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This joint ECDC‐EFSA report assesses whether vector count data (abundance) and the way these change throughout the year (seasonality) can provide useful information about vector‐borne diseases epidemiological processes of interest, and therefore, whether resources should be devoted to collecting such data. The document also summarises what measures of abundance and seasonality can be collected for each vector group (mosquitoes, sandflies, midges and ticks), lists the gaps in the sampling coverage and provides guidance for prioritising the acquisition of information. Knowing where vector species occur is crucial for the assessment of vector‐borne disease risk. Collecting abundance and seasonality data through field sampling is, however, more expensive and time consuming than collecting most other vector‐related data. The importance of abundance and seasonality data varies considerably according to the vector group and the epidemiological concept of interest. Epidemiological concepts of tick‐borne disease are less informed by abundance data than are those of the other vector groups, and presence/absence data of ticks may be sufficient in conjunction with other factors. Abundance data are generally considered relevant to pathogen establishment and transmission but less useful to inform early warning systems, estimates of the likelihood of pathogen persistence and –for sandflies, midges and ticks– to inform targeting of control measures. The conclusions on this report were based on VectorNet group discussions, field sampling studies, and review of both peer‐reviewed and grey literature. VectorNet is a European network for gathering and sharing data on the geographic distribution of arthropod vectors of disease agents affecting humans and livestock. Finally, the report notes that any form of extensive vector sampling depends not only on resources to fund the sampling programmes, but also on the availability of entomologists that have the skills to implement surveillance and monitoring programmes.

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Seasonal cycles of the TBE and Lyme borreliosis vector Ixodes ricinus modelled with time-lagged and interval-averaged predictors

Cited by 22 publications

References 64 publications

Forecasting next season’s Ixodes ricinus nymphal density: the example of southern Germany 2018

Forecasting next season’s Ixodes ricinus nymphal density: the example of southern Germany 2018

Tick-borne encephalitis (TBE) cases are not random: explaining trend, low- and high-frequency oscillations based on the Austrian TBE time series

The importance of vector abundance and seasonality

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