Quantitative feature extraction for machine learning analysis of resting-state fMRI data

Joint posterior distributions of the fitted statistical models. (DOCX 32 kb

Confidence intervals of praziquantel treatment needs for the school-aged population estimated at different spatial scales.

<p>The estimates are based on 50% confidence intervals of the posterior predictive distribution for 29 West and East African countries. Treatment needs were calculated based on previously published geostatistical model-based prevalence estimates <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur1" target="_blank">[2]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur2" target="_blank">[3]</a> and WHO guidelines <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO1" target="_blank">[4]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO2" target="_blank">[5]</a>.</p

Country-specific praziquantel treatment needs for the school-aged population estimated at different spatial scales.

<p>The estimates are based on the median schistosomiasis risk of the posterior predictive distribution for 29 West and East African countries. Treatment needs were calculated based on previously published geostatistical model-based prevalence estimates <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur1" target="_blank">[2]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur2" target="_blank">[3]</a> and WHO guidelines <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO1" target="_blank">[4]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO2" target="_blank">[5]</a>.</p

Schistosomiasis endemicity estimates at different administrative levels and pixel-level prevalence in Senegal.

<p>Provincial (A), district (B), and pixel-level (C) endemicity and pixel-level prevalence (D). The country-specific prevalence in Senegal is 18.2% based on previously published geostatistical model-based estimates <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur1" target="_blank">[2]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur2" target="_blank">[3]</a>. Low (schistosomiasis prevalence in school-aged children <10%), moderate (10–50%), and high (>50%) endemicity levels are based on WHO guidelines <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO1" target="_blank">[4]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO2" target="_blank">[5]</a>.</p

Schistosomiasis endemicity estimates at different administrative levels and pixel-level prevalence in Burkina Faso.

<p>Provincial (A), district (B), and pixel-level (C) endemicity and pixel-level prevalence (D). The country-specific prevalence in Burkina Faso is 48.0% based on previously published geostatistical model-based estimates <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur1" target="_blank">[2]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur2" target="_blank">[3]</a>. Low (schistosomiasis prevalence in school-aged children <10%), moderate (10–50%), and high (>50%) endemicity levels are based on WHO guidelines <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO1" target="_blank">[4]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO2" target="_blank">[5]</a>.</p

Schistosomiasis endemicity estimates at different administrative levels and pixel-level prevalence in Ghana.

<p>Provincial (A), district (B), and pixel-level (C) endemicity and pixel-level prevalence (D). The country-specific prevalence in Ghana is 54.6% based on previously published geostatistical model-based estimates <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur1" target="_blank">[2]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur2" target="_blank">[3]</a>. Low (schistosomiasis prevalence in school-aged children <10%), moderate (10–50%), and high (>50%) endemicity levels are based on WHO guidelines <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO1" target="_blank">[4]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO2" target="_blank">[5]</a>.</p

Endemicity estimates of schistosomiasis for an ensemble of 29 West and East African countries.

<p>The endemicity levels at country-level (A) and pixel-level (B) are based on previously published geostatistical model-based prevalence estimates <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur1" target="_blank">[2]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-Schur2" target="_blank">[3]</a> and WHO classifications <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO1" target="_blank">[4]</a>, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001773#pntd.0001773-WHO2" target="_blank">[5]</a>. Low endemicity is defined as schistosomiasis prevalence in school-aged children <10%, moderate endemicity as prevalence between 10% and 50%, and high endemicity as prevalence >50%.</p

The distribution of environmental factors in Angola.

<p>The distribution of environmental factors in Angola.</p

The lower (left) and upper (right) percentiles of the posterior distribution for the predicted malaria parasitaemia.

<p>The lower (left) and upper (right) percentiles of the posterior distribution for the predicted malaria parasitaemia.</p

The distribution of the Kullback-Leibler measure for the three Bayesian geostatistical approaches that model the non-linear environmental effect.

<p>The distribution of the Kullback-Leibler measure for the three Bayesian geostatistical approaches that model the non-linear environmental effect.</p