Details:

Alternative Title:
PLoS One
Publisher's site:
http://dx.doi.org/10.1371/journal.pone.0103163
Personal Author:
Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H.; Bisanzio, Donal; Liu, Yang; ... More +
Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H.; Bisanzio, Donal; Liu, Yang; Remais, Justin V.; Less -
Description:
Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.
Subject:
[+]
Communicable Diseases
Datasets As Topic
Disease Vectors
Humans
Ixodes
Lyme Disease
Models, Theoretical
Odds Ratio
Reproducibility Of Results
Risk

Source:
PLoS One. 2014; 9(7).;
Pubmed ID:
25072884
Pubmed Central ID:
PMC4114569
Document Type:
Journal Article;
Funding:
5 U01 EH000405/EH/NCEH CDC HHS/United States; K01 AI091864/AI/NIAID NIH HHS/United States; K01AI091864/AI/NIAID NIH HHS/United States;
5 U01 EH000405/EH/NCEH CDC HHS/United States; K01 AI091864/AI/NIAID NIH HHS/United States; K01AI091864/AI/NIAID NIH HHS/United States; Less -
Place as Subject:
United States;
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:5e9cb062893e2cbfb4b40105c1629fd97bfc52f06557912159710d57cea324cd
File Type:

Supporting Files

• 	pone.0103163.e008.jpg	jpeg
  	pone.0103163.e009.jpg	jpeg
  	pone.0103163.e010.jpg	jpeg
  	pone.0103163.e011.jpg	jpeg
  	pone.0103163.e012.jpg	jpeg
  	pone.0103163.g001.gif	gif
  	pone.0103163.g001.jpg	jpeg
  	pone.0103163.nxml	txt
  	pone.0103163.s001.pdf	pdf
  	pone.0103163.s002.pdf	pdf
  	license.txt	txt
  	pone.0103163.t001.gif	gif
  	pone.0103163.t001.jpg	jpeg
  	pone.0103163.t002.gif	gif
  	pone.0103163.t002.jpg	jpeg
  	pone.0103163.t003.gif	gif
  	pone.0103163.t003.jpg	jpeg
  	pone.0103163.t004.gif	gif
  	pone.0103163.t004.jpg	jpeg
  	pone.0103163.t005.gif	gif
  	pone.0103163.t005.jpg	jpeg
  	pone.0103163.e001.jpg	jpeg
  	pone.0103163.t006.gif	gif
  	pone.0103163.t006.jpg	jpeg
  	pone.0103163.e002.jpg	jpeg
  	pone.0103163.e003.jpg	jpeg
  	pone.0103163.e004.jpg	jpeg
  	pone.0103163.e005.jpg	jpeg
  	pone.0103163.e006.jpg	jpeg
  	pone.0103163.e007.jpg	jpeg

More +