Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.
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 mod...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4114569?pdf=render |
| _version_ | 1818982133670084608 |
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| author | Alyson Lorenz Radhika Dhingra Howard H Chang Donal Bisanzio Yang Liu Justin V Remais |
| author_facet | Alyson Lorenz Radhika Dhingra Howard H Chang Donal Bisanzio Yang Liu Justin V Remais |
| author_sort | Alyson Lorenz |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T17:42:22Z |
| format | Article |
| id | doaj.art-94f7201f02c5481ab406294bd36c8163 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T17:42:22Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-94f7201f02c5481ab406294bd36c81632022-12-21T19:31:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0197e10316310.1371/journal.pone.0103163Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.Alyson LorenzRadhika DhingraHoward H ChangDonal BisanzioYang LiuJustin V RemaisExtrapolating 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.http://europepmc.org/articles/PMC4114569?pdf=render |
| spellingShingle | Alyson Lorenz Radhika Dhingra Howard H Chang Donal Bisanzio Yang Liu Justin V Remais Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling. PLoS ONE |
| title | Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling. |
| title_full | Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling. |
| title_fullStr | Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling. |
| title_full_unstemmed | Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling. |
| title_short | Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling. |
| title_sort | inter model comparison of the landscape determinants of vector borne disease implications for epidemiological and entomological risk modeling |
| url | http://europepmc.org/articles/PMC4114569?pdf=render |
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