Predicting tick presence by environmental risk mapping

Public health statistics recorded an increasing trend in the incidence of tick bites and erythema migrans 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 absen...

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Main Authors: Arno eSwart, Adolfo eIbañez-Justicia, Jan eBuijs, Sip evan Wieren, Tim eHofmeester, Hein eSprong, Katsuhisa eTakumi
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-11-01
Series:Frontiers in Public Health
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fpubh.2014.00238/full
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spelling doaj-ffc5653f176b4517b0182582284f428a2020-11-24T22:56:20ZengFrontiers Media S.A.Frontiers in Public Health2296-25652014-11-01210.3389/fpubh.2014.00238108150Predicting tick presence by environmental risk mappingArno eSwart0Adolfo eIbañez-Justicia1Jan eBuijs2Sip evan Wieren3Tim eHofmeester4Hein eSprong5Katsuhisa eTakumi6RIVMCentre for Monitoring of VectorsGGD AmsterdamWageningen UniversityWageningen UniversityRIVMRIVMPublic health statistics recorded an increasing trend in the incidence of tick bites and erythema migrans 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 a correlation between incidence of erythema migrans 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% percent 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 Ixodes ricinus population outside the resident municipality.http://journal.frontiersin.org/Journal/10.3389/fpubh.2014.00238/fullLyme DiseaseNetherlandsTicksspatial modelingRiskmap
collection DOAJ
language English
format Article
sources DOAJ
author Arno eSwart
Adolfo eIbañez-Justicia
Jan eBuijs
Sip evan Wieren
Tim eHofmeester
Hein eSprong
Katsuhisa eTakumi
spellingShingle Arno eSwart
Adolfo eIbañez-Justicia
Jan eBuijs
Sip evan Wieren
Tim eHofmeester
Hein eSprong
Katsuhisa eTakumi
Predicting tick presence by environmental risk mapping
Frontiers in Public Health
Lyme Disease
Netherlands
Ticks
spatial modeling
Riskmap
author_facet Arno eSwart
Adolfo eIbañez-Justicia
Jan eBuijs
Sip evan Wieren
Tim eHofmeester
Hein eSprong
Katsuhisa eTakumi
author_sort Arno eSwart
title Predicting tick presence by environmental risk mapping
title_short Predicting tick presence by environmental risk mapping
title_full Predicting tick presence by environmental risk mapping
title_fullStr Predicting tick presence by environmental risk mapping
title_full_unstemmed Predicting tick presence by environmental risk mapping
title_sort predicting tick presence by environmental risk mapping
publisher Frontiers Media S.A.
series Frontiers in Public Health
issn 2296-2565
publishDate 2014-11-01
description Public health statistics recorded an increasing trend in the incidence of tick bites and erythema migrans 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 a correlation between incidence of erythema migrans 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% percent 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 Ixodes ricinus population outside the resident municipality.
topic Lyme Disease
Netherlands
Ticks
spatial modeling
Riskmap
url http://journal.frontiersin.org/Journal/10.3389/fpubh.2014.00238/full
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