Literature DB >> 16619620

Evaluating satellite sensor-derived indices for Lyme disease risk prediction.

Sarah E Rodgers1, Thomas N Mather.   

Abstract

The wetness and greenness indices created using Landsat Thematic Mapper (TM) data from June 1995 and 1997 and July 2002 were tested for their ability to predict the location of sites with different levels of nymphal blacklegged tick, Ixodes scapularis Say, abundance in Rhode Island. In 1995, there were statistically significant differences in the mean of greenness and wetness indices between sites classified as low and moderate tick abundance areas (P = 0.005 and P = 0.041, respectively). In 1997, there also were statistically significant differences in the mean of the greenness and wetness indices, but these differences were between the grouping of low/moderate tick abundance and the high tick abundance category (P = 0.023 and P = 0.015, respectively). The same indices from the 2002 image were not significant predictors of tick abundance. It may be that Landsat TM-derived indices can be used to predict nymphal blacklegged tick abundance in years (e.g., 1995 and 1997) when tick abundance is lower than average but not in years when it is higher (e.g., 2002). Thus, it seems unlikely that these remotely sensed indices will be very useful for modeling nonperidomestic Lyme disease risk over a large region in Rhode Island.

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Year:  2006        PMID: 16619620     DOI: 10.1603/0022-2585(2006)043[0337:essifl]2.0.co;2

Source DB:  PubMed          Journal:  J Med Entomol        ISSN: 0022-2585            Impact factor:   2.278


  6 in total

1.  Spatial risk assessments based on vector-borne disease epidemiologic data: importance of scale for West Nile virus disease in Colorado.

Authors:  Anna M Winters; Rebecca J Eisen; Mark J Delorey; Marc Fischer; Roger S Nasci; Emily Zielinski-Gutierrez; Chester G Moore; W John Pape; Lars Eisen
Journal:  Am J Trop Med Hyg       Date:  2010-05       Impact factor: 2.345

Review 2.  Spatial dynamics of lyme disease: a review.

Authors:  Mary E Killilea; Andrea Swei; Robert S Lane; Cheryl J Briggs; Richard S Ostfeld
Journal:  Ecohealth       Date:  2008-06-05       Impact factor: 3.184

3.  A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters.

Authors:  Abhishek K Kala; Chetan Tiwari; Armin R Mikler; Samuel F Atkinson
Journal:  PeerJ       Date:  2017-03-28       Impact factor: 2.984

4.  Human Babesia microti incidence and Ixodes scapularis distribution, Rhode Island, 1998-2004.

Authors:  Sarah E Rodgers; Thomas N Mather
Journal:  Emerg Infect Dis       Date:  2007-04       Impact factor: 6.883

5.  Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens.

Authors:  Michael C Wimberly; Adam D Baer; Michael J Yabsley
Journal:  Int J Health Geogr       Date:  2008-04-15       Impact factor: 3.918

6.  Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus.

Authors:  Abhishek K Kala; Samuel F Atkinson; Chetan Tiwari
Journal:  PeerJ       Date:  2020-07-27       Impact factor: 2.984

  6 in total

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