Literature DB >> 23144590

Delineating West Nile Virus Transmission Cycles at Various Scales: The Nearest Neighbor Distance-Time Model.

Debarchana Ghosh1, Steven M Manson, Robert B McMaster.   

Abstract

Various approaches are used to identify West Nile virus (WNV) exposure areas, including unusual sightings of infected dead birds, mosquito pools or human cases both prospectively and retrospectively. A significant and largely unmet need in WNV research is to incorporate the temporal characterization of virus spread and locational information of the three components of transmission cycle-i.e., birds (reservoir), mosquitoes (vector), and humans (host)-on a localized scale. Exposure areas containing all three components of the WNV cycle in close proximity have higher potential to amplify an outbreak as compared to exposure areas delineated by a single component. In this paper, we introduce a novel approach, termed 'Nearest Neighbor Distance Time' or NNDT, to identify and retrospectively monitor WNV transmission cycles on various scales in the Twin Cities Metropolitan area of Minnesota. The NNDT model was implemented in a geographic information system using data from the period 2002 to 2006. The results indicated that 2002 and 2003 had three such WNV cycles, followed by one, two, and four respectively in 2004, 2005, and 2006. The NNDT method can be useful in locating chronically exposed areas and generating hypotheses about the transmission of WNV.

Entities:  

Year:  2010        PMID: 23144590      PMCID: PMC3491911          DOI: 10.1559/152304010791232208

Source DB:  PubMed          Journal:  Cartogr Geogr Inf Sci        ISSN: 1523-0406


  22 in total

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Journal:  Vector Borne Zoonotic Dis       Date:  2002       Impact factor: 2.133

2.  Identifying West Nile virus risk areas: the Dynamic Continuous-Area Space-Time system.

Authors:  Constandinos N Theophilides; Sean C Ahearn; Sue Grady; Mario Merlino
Journal:  Am J Epidemiol       Date:  2003-05-01       Impact factor: 4.897

3.  A mathematical model for assessing control strategies against West Nile virus.

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Journal:  Bull Math Biol       Date:  2005-09       Impact factor: 1.758

4.  Prediction of human cases of West Nile virus by equine cases, Saskatchewan, Canada, 2003.

Authors:  Rebecca L A Corrigan; Cheryl Waldner; Tasha Epp; Judith Wright; Stephen M Whitehead; Helen Bangura; Eric Young; Hugh G G Townsend
Journal:  Prev Vet Med       Date:  2006-06-27       Impact factor: 2.670

5.  Discovering spatio-temporal models of the spread of West Nile virus.

Authors:  Jennifer Orme-Zavaleta; Jane Jorgensen; Bruce D'Ambrosio; Eric Altendorf; Philippe A Rossignol
Journal:  Risk Anal       Date:  2006-04       Impact factor: 4.000

6.  Modeling the spatial distribution of mosquito vectors for West Nile virus in Connecticut, USA.

Authors:  Maria A Diuk-Wasser; Heidi E Brown; Theodore G Andreadis; Durland Fish
Journal:  Vector Borne Zoonotic Dis       Date:  2006       Impact factor: 2.133

7.  Dead crow density and West Nile virus monitoring, New York.

Authors:  Millicent Eidson; Kate Schmit; Yoichiro Hagiwara; Madhu Anand; P Bryon Backenson; Ivan Gotham; Laura Kramer
Journal:  Emerg Infect Dis       Date:  2005-09       Impact factor: 6.883

8.  Dead crow densities and human cases of West Nile virus, New York State, 2000.

Authors:  M Eidson; J Miller; L Kramer; B Cherry; Y Hagiwara
Journal:  Emerg Infect Dis       Date:  2001 Jul-Aug       Impact factor: 6.883

9.  Predicting outbreaks: a spatial risk assessment of West Nile virus in British Columbia.

Authors:  Kaoru Tachiiri; Brian Klinkenberg; Sunny Mak; Jamil Kazmi
Journal:  Int J Health Geogr       Date:  2006-05-16       Impact factor: 3.918

10.  Using geographic information systems and spatial and space-time scan statistics for a population-based risk analysis of the 2002 equine West Nile epidemic in six contiguous regions of Texas.

Authors:  Min Lian; Ronald D Warner; James L Alexander; Kenneth R Dixon
Journal:  Int J Health Geogr       Date:  2007-09-21       Impact factor: 3.918

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  3 in total

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Authors:  Shlomit Paz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2015-04-05       Impact factor: 6.237

2.  A meta-analysis of the factors influencing development rate variation in Aedes aegypti (Diptera: Culicidae).

Authors:  Jannelle Couret; Mark Q Benedict
Journal:  BMC Ecol       Date:  2014-02-05       Impact factor: 2.964

Review 3.  Exploring the spatio-temporal dynamics of reservoir hosts, vectors, and human hosts of West Nile virus: a review of the recent literature.

Authors:  Esra Ozdenerol; Gregory N Taff; Cem Akkus
Journal:  Int J Environ Res Public Health       Date:  2013-10-25       Impact factor: 3.390

  3 in total

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