Dataset Information

Predicting tick presence by environmental risk mapping.

ABSTRACT: Public health statistics recorded an increasing trend in the incidence of tick bites and erythema migrans (EM) in the Netherlands. We investigated whether the disease incidence could be predicted by a spatially explicit categorization model, based on environmental factors and a training set of tick absence-presence data. Presence and absence of Ixodes ricinus were determined by the blanket-dragging method at numerous sites spread over the Netherlands. The probability of tick presence on a 1 km by 1 km square grid was estimated from the field data using a satellite-based methodology. Expert elicitation was conducted to provide a Bayesian prior per landscape type. We applied a linear model to test for a linear relationship between incidence of EM consultations by general practitioners in the Netherlands and the estimated probability of tick presence. Ticks were present at 252 distinct sampling coordinates and absent at 425. Tick presence was estimated for 54% of the total land cover. Our model has predictive power for tick presence in the Netherlands, tick-bite incidence per municipality correlated significantly with the average probability of tick presence per grid. The estimated intercept of the linear model was positive and significant. This indicates that a significant fraction of the tick-bite consultations could be attributed to the I. ricinus population outside the resident municipality.

SUBMITTER: Swart A

PROVIDER: S-EPMC4244977 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Predicting tick presence by environmental risk mapping.

Swart Arno A Ibañez-Justicia Adolfo A Buijs Jan J van Wieren Sip E SE Hofmeester Tim R TR Sprong Hein H Takumi Katsuhisa K

Frontiers in public health 20141126

Public health statistics recorded an increasing trend in the incidence of tick bites and erythema migrans (EM) in the Netherlands. We investigated whether the disease incidence could be predicted by a spatially explicit categorization model, based on environmental factors and a training set of tick absence-presence data. Presence and absence of Ixodes ricinus were determined by the blanket-dragging method at numerous sites spread over the Netherlands. The probability of tick presence on a 1 km b ...[more]

PMID: 25505781

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Project description:Microbiome management research and applications rely on temporally resolved measurements of community composition. Current technologies to assess community composition make use of either cultivation or sequencing of genomic material, which can become time-consuming and/or laborious in case high-throughput measurements are required. Here, using data from a shrimp hatchery as an economically relevant case study, we combined 16S rRNA gene amplicon sequencing and flow cytometry data to develop a computational workflow that allows the prediction of taxon abundances based on flow cytometry measurements. The first stage of our pipeline consists of a classifier to predict the presence or absence of the taxon of interest, with yielded an average accuracy of 88.13% ± 4.78% across the top 50 operational taxonomic units (OTUs) of our data set. In the second stage, this classifier was combined with a regression model to predict the relative abundances of the taxon of interest, which yielded an average R2 of 0.35 ± 0.24 across the top 50 OTUs of our data set. Application of the models to flow cytometry time series data showed that the generated models can predict the temporal dynamics of a large fraction of the investigated taxa. Using cell sorting, we validated that the model correctly associates taxa to regions in the cytometric fingerprint, where they are detected using 16S rRNA gene amplicon sequencing. Finally, we applied the approach of our pipeline to two other data sets of microbial ecosystems. This pipeline represents an addition to the expanding toolbox for flow cytometry-based monitoring of bacterial communities and complements the current plating- and marker gene-based methods. IMPORTANCE Monitoring of microbial community composition is crucial for both microbiome management research and applications. Existing technologies, such as plating and amplicon sequencing, can become laborious and expensive when high-throughput measurements are required. In recent years, flow cytometry-based measurements of community diversity have been shown to correlate well with those derived from 16S rRNA gene amplicon sequencing in several aquatic ecosystems, suggesting that there is a link between the taxonomic community composition and phenotypic properties as derived through flow cytometry. Here, we further integrated 16S rRNA gene amplicon sequencing and flow cytometry survey data in order to construct models that enable the prediction of both the presence and the abundances of individual bacterial taxa in mixed communities using flow cytometric fingerprinting. The developed pipeline holds great potential to be integrated into routine monitoring schemes and early warning systems for biotechnological applications.

Project description:BackgroundThe vector-borne human viral zoonosis tick-borne encephalitis (TBE) is of growing concern in Sweden. The area where TBE is considered endemic has expanded, with an increasing geographical distribution of Ixodes ricinus as the tick vector and a rising number of reported TBE cases in humans. Efforts to map TBE risk areas have been carried out by sentinel monitoring, mainly based on individual sampling and analysis of wild and domestic animals, as well as ticks, for tick-borne encephalitis virus (TBEV). However, the interpretation of the geographical distribution has been hampered by the patchy and focal nature of TBEV occurrence. This study presents TBEV surveillance data based on antibody analysis of bulk tank milk collected from dairy herds located throughout Sweden before (May) and after (November) the vector season. A commercial TBEV antibody ELISA was modified and evaluated for use in this study.ResultsThe initial comparative TBEV antibody analysis revealed a good correlation between milk and serum antibody levels from individually sampled cows. Also, the TBEV-antibody levels for the mean-herd serum showed good comparability with TBEV antibody levels from bulk tank milk, thus indicating good predictability of seroprevalence when analysing bulk tank milk from a herd. Analyses of bulk tank milk samples collected from 616 herds in May and 560 herds in November showed a geographical distribution of TBEV seropositive herds that was largely consistent with reported human TBE cases. A few TBEV-reactive herds were also found outside known locations of human TBE cases.ConclusionSerological examination of bulk tank milk from dairy cattle herds may be a useful sentinel surveillance method to identify geographical presence of TBEV. In contrast to individual sampling this method allows a large number of animals to be monitored. TBEV seropositive herds were mainly found in coastal areas of southern Sweden similar to human TBE cases. However, some antibody-reactive herds were found outside known TBE areas at the time of the study.

Dataset Information

Predicting tick presence by environmental risk mapping.

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Predicting tick presence by environmental risk mapping.

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