Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach

Rita Ribeiro*, JI Eze, GJ Gunn, Lucy Gilbert, Alastair Macrae, HK Auty

*Corresponding author for this work

Research output: Contribution to conferencePoster

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Abstract

In Northern and Western Europe Ixodes ricinus is the most abundant and widespread tick species and also the vector of several pathogens. Predictive maps with the spatial and seasonal distribution of vectors are important tools in assessing the risk of vector-borne diseases, enabling better targeting of surveillance and control, as well increasing awareness. The objectives of this study are to identify the environmental, climatic, habitat and host related predictors of I. ricinus abundance and to predict I. ricinus abundance in mainland Scotland.

The dataset used comprised questing nymphs counted by scientists during tick abundance surveys performed between 2006 and 2017. To account for the complex structure of the dataset and the existence of spatial heterogeneity, a Bayesian approach was used. Georeferenced variables were selected as possible predictors, based on their biological meaning, statistical significance and lack of correlation with other variables. The model was fitted with two random effects and a zero-inflated Poisson distribution. Candidate models were evaluated using two Bayesian information criteria and the cross-validation method leave-one-out.

The selected model showed positive relationships between tick abundance and temperature in July, roe deer presence and proportion of coniferous and deciduous forest, and a negative association with frost in September. A predictive map was developed based on this model. Although the model choice seemed adequate to capture data complexity, the variability present in the dataset and the lack of data from some areas highlights the need for further data collection to improve the accuracy of predictive maps.

Original languageEnglish
Publication statusPrint publication - 3 Dec 2018
EventVECTOR-BORNE DISEASES IN THE UK (VBD2018) - Norwich, United Kingdom
Duration: 3 Dec 20184 Dec 2018

Conference

ConferenceVECTOR-BORNE DISEASES IN THE UK (VBD2018)
CountryUnited Kingdom
CityNorwich
Period3/12/184/12/18

Fingerprint

Ixodes ricinus
Scotland
spatial distribution
tick
ticks
vector-borne diseases
Capreolus capreolus
Northern European region
Western European region
coniferous forest
frost
deciduous forests
deciduous forest
deer
targeting
coniferous forests
nymphs
pathogen
pathogens
monitoring

Cite this

Ribeiro, R., Eze, JI., Gunn, GJ., Gilbert, L., Macrae, A., & Auty, HK. (2018). Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach. Poster session presented at VECTOR-BORNE DISEASES IN THE UK (VBD2018), Norwich, United Kingdom.
Ribeiro, Rita ; Eze, JI ; Gunn, GJ ; Gilbert, Lucy ; Macrae, Alastair ; Auty, HK. / Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach. Poster session presented at VECTOR-BORNE DISEASES IN THE UK (VBD2018), Norwich, United Kingdom.
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title = "Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach",
abstract = "In Northern and Western Europe Ixodes ricinus is the most abundant and widespread tick species and also the vector of several pathogens. Predictive maps with the spatial and seasonal distribution of vectors are important tools in assessing the risk of vector-borne diseases, enabling better targeting of surveillance and control, as well increasing awareness. The objectives of this study are to identify the environmental, climatic, habitat and host related predictors of I. ricinus abundance and to predict I. ricinus abundance in mainland Scotland.The dataset used comprised questing nymphs counted by scientists during tick abundance surveys performed between 2006 and 2017. To account for the complex structure of the dataset and the existence of spatial heterogeneity, a Bayesian approach was used. Georeferenced variables were selected as possible predictors, based on their biological meaning, statistical significance and lack of correlation with other variables. The model was fitted with two random effects and a zero-inflated Poisson distribution. Candidate models were evaluated using two Bayesian information criteria and the cross-validation method leave-one-out.The selected model showed positive relationships between tick abundance and temperature in July, roe deer presence and proportion of coniferous and deciduous forest, and a negative association with frost in September. A predictive map was developed based on this model. Although the model choice seemed adequate to capture data complexity, the variability present in the dataset and the lack of data from some areas highlights the need for further data collection to improve the accuracy of predictive maps.",
author = "Rita Ribeiro and JI Eze and GJ Gunn and Lucy Gilbert and Alastair Macrae and HK Auty",
year = "2018",
month = "12",
day = "3",
language = "English",
note = "VECTOR-BORNE DISEASES IN THE UK (VBD2018) ; Conference date: 03-12-2018 Through 04-12-2018",

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Ribeiro, R, Eze, JI, Gunn, GJ, Gilbert, L, Macrae, A & Auty, HK 2018, 'Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach' VECTOR-BORNE DISEASES IN THE UK (VBD2018), Norwich, United Kingdom, 3/12/18 - 4/12/18, .

Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach. / Ribeiro, Rita; Eze, JI; Gunn, GJ; Gilbert, Lucy; Macrae, Alastair; Auty, HK.

2018. Poster session presented at VECTOR-BORNE DISEASES IN THE UK (VBD2018), Norwich, United Kingdom.

Research output: Contribution to conferencePoster

TY - CONF

T1 - Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach

AU - Ribeiro, Rita

AU - Eze, JI

AU - Gunn, GJ

AU - Gilbert, Lucy

AU - Macrae, Alastair

AU - Auty, HK

PY - 2018/12/3

Y1 - 2018/12/3

N2 - In Northern and Western Europe Ixodes ricinus is the most abundant and widespread tick species and also the vector of several pathogens. Predictive maps with the spatial and seasonal distribution of vectors are important tools in assessing the risk of vector-borne diseases, enabling better targeting of surveillance and control, as well increasing awareness. The objectives of this study are to identify the environmental, climatic, habitat and host related predictors of I. ricinus abundance and to predict I. ricinus abundance in mainland Scotland.The dataset used comprised questing nymphs counted by scientists during tick abundance surveys performed between 2006 and 2017. To account for the complex structure of the dataset and the existence of spatial heterogeneity, a Bayesian approach was used. Georeferenced variables were selected as possible predictors, based on their biological meaning, statistical significance and lack of correlation with other variables. The model was fitted with two random effects and a zero-inflated Poisson distribution. Candidate models were evaluated using two Bayesian information criteria and the cross-validation method leave-one-out.The selected model showed positive relationships between tick abundance and temperature in July, roe deer presence and proportion of coniferous and deciduous forest, and a negative association with frost in September. A predictive map was developed based on this model. Although the model choice seemed adequate to capture data complexity, the variability present in the dataset and the lack of data from some areas highlights the need for further data collection to improve the accuracy of predictive maps.

AB - In Northern and Western Europe Ixodes ricinus is the most abundant and widespread tick species and also the vector of several pathogens. Predictive maps with the spatial and seasonal distribution of vectors are important tools in assessing the risk of vector-borne diseases, enabling better targeting of surveillance and control, as well increasing awareness. The objectives of this study are to identify the environmental, climatic, habitat and host related predictors of I. ricinus abundance and to predict I. ricinus abundance in mainland Scotland.The dataset used comprised questing nymphs counted by scientists during tick abundance surveys performed between 2006 and 2017. To account for the complex structure of the dataset and the existence of spatial heterogeneity, a Bayesian approach was used. Georeferenced variables were selected as possible predictors, based on their biological meaning, statistical significance and lack of correlation with other variables. The model was fitted with two random effects and a zero-inflated Poisson distribution. Candidate models were evaluated using two Bayesian information criteria and the cross-validation method leave-one-out.The selected model showed positive relationships between tick abundance and temperature in July, roe deer presence and proportion of coniferous and deciduous forest, and a negative association with frost in September. A predictive map was developed based on this model. Although the model choice seemed adequate to capture data complexity, the variability present in the dataset and the lack of data from some areas highlights the need for further data collection to improve the accuracy of predictive maps.

M3 - Poster

ER -

Ribeiro R, Eze JI, Gunn GJ, Gilbert L, Macrae A, Auty HK. Assessing the spatial distribution of Ixodes ricinus in Scotland using a Bayesian approach. 2018. Poster session presented at VECTOR-BORNE DISEASES IN THE UK (VBD2018), Norwich, United Kingdom.