Posts tagged GIS

3 Notes

Map showing overlap in breeding relative abundance for mallard and gadwall species. Note that the geographic distribution of gadwall breeding locations is contained almost entirely by areas where mallard breed, with similar areas of high- and low-breeding concentrations across the contiguous United States. The mallard tested positive at some of the highest rates and the gadwall was near the lowest in proportion of AIV positive tests, suggesting geographic overlap alone does not explain variations in species prevalence patterns.
Published in Farnsworth ML , Miller RS , Pedersen K , Lutman MW , Swafford SR , et al. (2012) Environmental and Demographic Determinants of Avian Influenza Viruses in Waterfowl across the Contiguous United States. PLoS ONE 7(3): e32729. doi:10.1371/journal.pone.0032729

Map showing overlap in breeding relative abundance for mallard and gadwall species. Note that the geographic distribution of gadwall breeding locations is contained almost entirely by areas where mallard breed, with similar areas of high- and low-breeding concentrations across the contiguous United States. The mallard tested positive at some of the highest rates and the gadwall was near the lowest in proportion of AIV positive tests, suggesting geographic overlap alone does not explain variations in species prevalence patterns.

Published in Farnsworth ML , Miller RS , Pedersen K , Lutman MW , Swafford SR , et al. (2012) Environmental and Demographic Determinants of Avian Influenza Viruses in Waterfowl across the Contiguous United States. PLoS ONE 7(3): e32729. doi:10.1371/journal.pone.0032729

2 Notes

Top model estimate of average predicted probability that an individual bird sampled from local watersheds during the breeding season tests positive for avian influenza virus.The probability is an average across all three years of data for all waterfowl sampled within a given watershed. Note the strong latitudinal gradient with higher probabilities of testing positive in northern latitudes and decreasing probabilities in southern latitudes.
Published in Farnsworth ML , Miller RS , Pedersen K , Lutman MW , Swafford SR , et al. (2012) Environmental and Demographic Determinants of Avian Influenza Viruses in Waterfowl across the Contiguous United States. PLoS ONE 7(3): e32729. doi:10.1371/journal.pone.0032729 

Top model estimate of average predicted probability that an individual bird sampled from local watersheds during the breeding season tests positive for avian influenza virus.The probability is an average across all three years of data for all waterfowl sampled within a given watershed. Note the strong latitudinal gradient with higher probabilities of testing positive in northern latitudes and decreasing probabilities in southern latitudes.

Published in Farnsworth ML , Miller RS , Pedersen K , Lutman MW , Swafford SR , et al. (2012) Environmental and Demographic Determinants of Avian Influenza Viruses in Waterfowl across the Contiguous United States. PLoS ONE 7(3): e32729. doi:10.1371/journal.pone.0032729 

Notes

Relative risk of water-associated infectious diseases. Shown are relative risk distributions for different categories of water-associated infectious diseases – water-borne (A), water-carried (B), water-based (C), water-related (D), water-washed (E), and water-dispersed (F). Relative risk estimate was based on the best fit Bayesian model integrating reported outbreaks, random and spatial effects.
For a description of these water categories see here
Published in Yang K , LeJeune J , Alsdorf D , Lu B , Shum CK , et al. 2012 Global Distribution of Outbreaks of Water-Associated Infectious Diseases. PLoS Negl Trop Dis 6(2): e1483. doi:10.1371/journal.pntd.0001483. 

Relative risk of water-associated infectious diseases. Shown are relative risk distributions for different categories of water-associated infectious diseases – water-borne (A), water-carried (B), water-based (C), water-related (D), water-washed (E), and water-dispersed (F). Relative risk estimate was based on the best fit Bayesian model integrating reported outbreaks, random and spatial effects.

For a description of these water categories see here

Published in Yang K , LeJeune J , Alsdorf D , Lu B , Shum CK , et al. 2012 Global Distribution of Outbreaks of Water-Associated Infectious Diseases. PLoS Negl Trop Dis 6(2): e1483. doi:10.1371/journal.pntd.0001483. 

1 Notes

Predicted and observed density of infected host-seeking Ixodes scapularis nymphs.
Published in Am J Trop Med Hyg 2012 vol. 86 no. 2 320-327.

Predicted and observed density of infected host-seeking Ixodes scapularis nymphs.

Published in Am J Trop Med Hyg 2012 vol. 86 no. 2 320-327.

1 Notes

Statistically significant high- and low-risk areas. High risk: 95% probability that at least 0.3 infected nymphs will be collected per 1,000 m2; low risk: 95% probability that < 0.3 infected nymphs will be collected per 1,000 m2; transitional area: risk cannot be ascertained with 95% confidence (confidence interval includes 0.3); true high risk: > 0.3 infected nymphs collected in a predicted high-risk area; true low risk: < 0.3 infected nymphs collected in a predicted low-risk area; false high risk: < 0.3 infected nymphs collected in a predicted high-risk area; false low risk: > 0.3 infected nymphs collected in a predicted low-risk area.
Published in Am J Trop Med Hyg 2012 vol. 86 no. 2 320-327.

Statistically significant high- and low-risk areas. High risk: 95% probability that at least 0.3 infected nymphs will be collected per 1,000 m2; low risk: 95% probability that < 0.3 infected nymphs will be collected per 1,000 m2; transitional area: risk cannot be ascertained with 95% confidence (confidence interval includes 0.3); true high risk: > 0.3 infected nymphs collected in a predicted high-risk area; true low risk: < 0.3 infected nymphs collected in a predicted low-risk area; false high risk: < 0.3 infected nymphs collected in a predicted high-risk area; false low risk: > 0.3 infected nymphs collected in a predicted low-risk area.

Published in Am J Trop Med Hyg 2012 vol. 86 no. 2 320-327.

13 Notes

Observed prevalence of Schistosoma haematobium in Africa based on the current progress of the newly developed, open-access global database for mapping, control, and surveillance of neglected tropical diseases.
The data included 5807 georeferenced survey locations. Prevalence equal to 0%, low infection rates (0.1–9.9%), moderate infection rates (10.0–49.9%) and high infection rates (≥50%) indicated by a red scale from light red to dark red. Cut-offs follow WHO recommendations.
Hurlimann E, Schur N, Boutsika K, Stensgaard A-S, Laserna de Himpsl M, et al. (2011) Toward an Open-Access Global Database for Mapping, Control, and
Surveillance of Neglected Tropical Diseases. PLoS Negl Trop Dis 5(12): e1404. doi:10.1371/journal.pntd.0001404

Observed prevalence of Schistosoma haematobium in Africa based on the current progress of the newly developed, open-access global database for mapping, control, and surveillance of neglected tropical diseases.

The data included 5807 georeferenced survey locations. Prevalence equal to 0%, low infection rates (0.1–9.9%), moderate infection rates (10.0–49.9%) and high infection rates (≥50%) indicated by a red scale from light red to dark red. Cut-offs follow WHO recommendations.

Hurlimann E, Schur N, Boutsika K, Stensgaard A-S, Laserna de Himpsl M, et al. (2011) Toward an Open-Access Global Database for Mapping, Control, and

Surveillance of Neglected Tropical Diseases. PLoS Negl Trop Dis 5(12): e1404. doi:10.1371/journal.pntd.0001404

Notes

Observed prevalence of Schistosoma mansoni in Africa based on the current progress of the newly developed, open-access global database for mapping, control, and surveillance of neglected tropical diseases.
The data included 4604 georeferenced survey locations. Prevalence equal to 0% in yellow dots, low infection rates (0.1–9.9%) in orange dots, moderate infection rates (10.0–49.9%) in light brown dots and high infection rates (≥50%) in brown dots. Cut-offs follow WHO recommendations.
Published in Hu¨ rlimann E, Schur N, Boutsika K, Stensgaard A-S, Laserna de Himpsl M, et al. (2011) Toward an Open-Access Global Database for Mapping, Control, and
Surveillance of Neglected Tropical Diseases. PLoS Negl Trop Dis 5(12): e1404.

Observed prevalence of Schistosoma mansoni in Africa based on the current progress of the newly developed, open-access global database for mapping, control, and surveillance of neglected tropical diseases.

The data included 4604 georeferenced survey locations. Prevalence equal to 0% in yellow dots, low infection rates (0.1–9.9%) in orange dots, moderate infection rates (10.0–49.9%) in light brown dots and high infection rates (≥50%) in brown dots. Cut-offs follow WHO recommendations.

Published in Hu¨ rlimann E, Schur N, Boutsika K, Stensgaard A-S, Laserna de Himpsl M, et al. (2011) Toward an Open-Access Global Database for Mapping, Control, and

Surveillance of Neglected Tropical Diseases. PLoS Negl Trop Dis 5(12): e1404.