Economic Conditions Predict Prevalence of West Nile Virus
Publication Date
November 12, 2010
Journal
PLOS ONE
Authors
Ryan J. Harrigan, Henri A. Thomassen, Wolfgang Buermann, Robert F. Cummings, et al
Volume
5
Issue
11
Pages
e15437
DOI
https://dx.plos.org/10.1371/journal.pone.0015437
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0015437
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/21103053
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2980475
Europe PMC
http://europepmc.org/abstract/MED/21103053
Web of Science
000284147700026
Scopus
78649755194
Mendeley
http://www.mendeley.com/research/economic-conditions-predict-prevalence-west-nile-virus
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/819373"], "description"=>"<p>Percent mean square error indicates the increase in error in out-of-bag samples when that variable is permuted, with higher increases indicative of more important variables. Negative changes in mean square error percentage (2004) suggest that random permutations of a variable perform better under random forest than actual values, indicating a poor predictor. There were not enough West Nile virus positives in vectors for years 2006 and 2007; thus, these years were excluded from analyses.</p>", "links"=>[], "tags"=>["scores", "models", "predictors", "nile", "prevalence"], "article_id"=>489736, "categories"=>["Microbiology", "Biological Sciences", "Biotechnology"], "users"=>["Ryan J. Harrigan", "Henri A. Thomassen", "Wolfgang Buermann", "Robert F. Cummings", "Matthew E. Kahn", "Thomas B. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015437.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variable_importance_scores_under_random_forest_models_for_both_ecological_and_economic_predictors_of_West_Nile_virus_prevalence_in_a_West_Nile_virus_hotspot_/489736", "title"=>"Variable importance scores under random forest models for both ecological and economic predictors of West Nile virus prevalence in a West Nile virus hotspot.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-12 02:42:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/819559"], "description"=>"<p>(A) Data layer representing per capita income across the county, as collected as part of the 2000 U.S. National Census. (B) Predictions of WNV prevalence in vectors across the study area for 2008 based on the 2005 WNV prevalence model. Circles indicate observed WNV prevalence levels in 2008 using the same color codes. (C) Predictions of WNV presence in human hosts in 2008 across the study area, determined using niche modeling (Maxent; <i>25</i>). Scale bar is an approximation, as scale varies according to perspective.</p>", "links"=>[], "tags"=>["predictions", "nile", "vectors"], "article_id"=>489925, "categories"=>["Microbiology", "Biological Sciences", "Biotechnology"], "users"=>["Ryan J. Harrigan", "Henri A. Thomassen", "Wolfgang Buermann", "Robert F. Cummings", "Matthew E. Kahn", "Thomas B. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015437.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_predictions_of_West_Nile_virus_in_vectors_and_human_populations_/489925", "title"=>"Spatial predictions of West Nile virus in vectors and human populations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-12 02:45:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/408156", "https://ndownloader.figshare.com/files/408188", "https://ndownloader.figshare.com/files/408210", "https://ndownloader.figshare.com/files/408241", "https://ndownloader.figshare.com/files/408256", "https://ndownloader.figshare.com/files/408276", "https://ndownloader.figshare.com/files/408321", "https://ndownloader.figshare.com/files/408343", "https://ndownloader.figshare.com/files/408383"], "description"=>"<div><p>Understanding the conditions underlying the proliferation of infectious diseases is crucial for mitigating future outbreaks. Since its arrival in North America in 1999, West Nile virus (WNV) has led to population-wide declines of bird species, morbidity and mortality of humans, and expenditures of millions of dollars on treatment and control. To understand the environmental conditions that best explain and predict WNV prevalence, we employed recently developed spatial modeling techniques in a recognized WNV hotspot, Orange County, California. Our models explained 85–95% of the variation of WNV prevalence in mosquito vectors, and WNV presence in secondary human hosts. Prevalence in both vectors and humans was best explained by economic variables, specifically per capita income, and by anthropogenic characteristics of the environment, particularly human population and neglected swimming pool density. While previous studies have shown associations between anthropogenic change and pathogen presence, results show that poorer economic conditions may act as a direct surrogate for environmental characteristics related to WNV prevalence. Low-income areas may be associated with higher prevalence for a number of reasons, including variations in property upkeep, microhabitat conditions conducive to viral amplification in both vectors and hosts, host community composition, and human behavioral responses related to differences in education or political participation. Results emphasize the importance and utility of including economic variables in mapping spatial risk assessments of disease.</p> </div>", "links"=>[], "tags"=>["conditions", "prevalence", "nile"], "article_id"=>140622, "categories"=>["Microbiology", "Biological Sciences", "Biotechnology"], "users"=>["Ryan J. Harrigan", "Henri A. Thomassen", "Wolfgang Buermann", "Robert F. Cummings", "Matthew E. Kahn", "Thomas B. Smith"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0015437.s001", "https://dx.doi.org/10.1371/journal.pone.0015437.s002", "https://dx.doi.org/10.1371/journal.pone.0015437.s003", "https://dx.doi.org/10.1371/journal.pone.0015437.s004", "https://dx.doi.org/10.1371/journal.pone.0015437.s005", "https://dx.doi.org/10.1371/journal.pone.0015437.s006", "https://dx.doi.org/10.1371/journal.pone.0015437.s007", "https://dx.doi.org/10.1371/journal.pone.0015437.s008", "https://dx.doi.org/10.1371/journal.pone.0015437.s009"], "stats"=>{"downloads"=>12, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Economic_Conditions_Predict_Prevalence_of_West_Nile_Virus/140622", "title"=>"Economic Conditions Predict Prevalence of West Nile Virus", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-11-12 00:10:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/819694"], "description"=>"<p>Root mean square error (RMSE) percentage of observed prevalence range are reported for single models (parentheses), whereas out-of-bag (OOB) error rates were used in calculations of percent variation explained in the separate random forest models.</p>", "links"=>[], "tags"=>["observed", "wnv", "vector", "prevalence", "models", "was", "separated"], "article_id"=>490048, "categories"=>["Microbiology", "Biological Sciences", "Biotechnology"], "users"=>["Ryan J. Harrigan", "Henri A. Thomassen", "Wolfgang Buermann", "Robert F. Cummings", "Matthew E. Kahn", "Thomas B. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015437.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_observed_versus_predicted_values_of_WNV_vector_prevalence_under_random_forest_models_using_the_same_data_set_for_testing_and_training_and_under_models_where_8764_36_of_the_data_was_separated_from_training_and_used_as_test_data_36_/490048", "title"=>"Relationship between observed versus predicted values of WNV vector prevalence under random forest models using the same data set for testing and training, and under models where ∼36% of the data was separated from training and used as test data (36).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-12 00:00:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/819432"], "description"=>"<p>Results are shown for vectors in Orange County, California, for 2004, 2005, and 2008. Prevalence is measured as MLE. Dashed lines indicate the bifurcation between high and low prevalence values as determined by tree regressions. Horizontal lines indicate mean values of prevalence for points above and below this bifurcation (Wilcoxon rank-sum tests for these means were significant for each year, p<0.001). Although absolute measures of WNV prevalence varied between years, relationships between predictors (per capita income in this case) and WNV prevalence were stable throughout the study period.</p>", "links"=>[], "tags"=>["capita", "nile"], "article_id"=>489796, "categories"=>["Microbiology", "Biological Sciences", "Biotechnology"], "users"=>["Ryan J. Harrigan", "Henri A. Thomassen", "Wolfgang Buermann", "Robert F. Cummings", "Matthew E. Kahn", "Thomas B. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015437.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_average_per_capita_income_and_West_Nile_virus_prevalence_/489796", "title"=>"Relationship between average per capita income and West Nile virus prevalence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-12 02:43:16"}

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Relative Metric

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