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

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

Predicted malaria prevalence in children less than 5 years; median (top), 2.5^th percentile (bottom left) and 97.5^th percentile posterior predictive distribution (bottom right).

<p>Predicted malaria prevalence in children less than 5 years; median (top), 2.5^th percentile (bottom left) and 97.5^th percentile posterior predictive distribution (bottom right).</p

Posterior inclusion probabilities for environmental, intervention, socio-economic and demographic factors.

<p>Posterior inclusion probabilities for environmental, intervention, socio-economic and demographic factors.</p

Coverage of control interventions by region.

<p>Coverage of control interventions by region.</p

Sources, spatial and temporal resolution of environmental/climatic and population data.

<p>Sources, spatial and temporal resolution of environmental/climatic and population data.</p

Estimated number of children less than 5 years infected with malaria.

<p>Estimated number of children less than 5 years infected with malaria.</p

Posterior estimates for the effect of environmental, intervention, socio-economic factors.

<p>Posterior estimates for the effect of environmental, intervention, socio-economic factors.</p

Estimated number of infected children less than 5 years and population-adjusted prevalence.

<p>Estimated number of infected children less than 5 years and population-adjusted prevalence.</p

Posterior median and 95% credible intervals for spatially varying effect of interventions on malaria prevalence.

<p>Posterior median and 95% credible intervals for spatially varying effect of interventions on malaria prevalence.</p

MOESM3 of The effect of case management and vector-control interventions on space–time patterns of malaria incidence in Uganda

Additional file 3. Estimating district-level indicator estimates