Predicted population coverage in 2012 for rural and urban populations, by second administrative area.

<p>Access to improved drinking-water supply in (A) rural and (B) urban populations; access to improved sanitation in (C) rural and (D) urban populations; and open defecation in (E) rural and (F) urban populations. Model results showing posterior median predicted coverage (i.e. most likely value) for each second administrative area. No data was available for Botswana and Eritrea (hatched). Each indicator was modelled independently.</p

Regional summary of water and sanitation coverage data sources and quantity for 41 sub-Saharan African countries.

<p>Sub-Saharan countries not included are Botswana, Cape Verde, Comoros, Djibouti, Eritrea, Reunion, Sao Tome and Principe, and Seychelles, representing <1.0% of the population of SSA in 2012.</p><p>AIS, AIDs Indicator Surveys; DHS, Demographic and Health Surveys; LSMS, Living Standard Measurement Studies; MICs, Multiple Indicator Cluster Surveys; MIS, Malaria Indicator Surveys.</p

Comparison of coverage in improved drinking water against improved sanitation in overall population, by second administrative area.

<p>Comparisons are made for (<b>A</b>) Nigeria, (<b>B</b>) Mozambique and (<b>C</b>) Uganda. <i>r</i> is the Pearson pairwise correlation coefficient. Each dot represents one administrative area.</p

Empirical relationship between inequality (GINI score) as a function of national coverage.

<p>Plots are shown for (<b>A</b>) use of improved drinking water, (<b>B</b>) use of improved sanitation facilities, and (<b>C</b>) use of any type of sanitation. All plots show the linear regression prediction (solid line) with 95% confidence interval (shaded area). Labelled countries (by 3-letter ISO codes) are those with GINI scores significantly higher or lower than would be expected, given national coverage.</p

Availability of nationally representative, cluster survey data on improved drinking water and sanitation across sub-Saharan Africa for the period 1990–2012.

<p>Data are linked to second administrative areas where possible and if not to the first administrative level; administrative boundaries are provided by the United Nations Second Administrative Level Boundaries (SALB) project.</p

Distribution of modelled WSS coverage across administrative areas by country.

<p>Second administrative areas were stratified into quintiles based on coverage of each indicator. Dots show median proportion of households with access for each quintile; lines show the full range in coverage.</p

Comparison of (A) any improved drinking water source against accessible, improved drinking water source and (B) any improved sanitation against private improved sanitation.

<p>Dots show national comparisons for urban (red) and rural (blue) populations. An accessible drinking water supply is defined as one within 15 minutes of the household; private sanitation is defined as a facility used by only one household.</p

Predicted second administrative areas that differ significantly from national mean coverage.

<p>Second administrative areas shaded red have significantly lower coverage than the national average, based on 95% BCI, for either both (dark red) or one (light red) of improved drinking water and improved sanitation; administrative areas shaded blue have significantly higher coverage rates than the national average. Administrative areas shaded grey are not significantly different from the national mean.</p

Relationship between relative geographical inequality for use of improved drinking water and RGI for use of improved sanitation for

<p>(<b>A</b>) rural populations (correlation (<i>r</i>) = 0.47, <i>p</i> = 0.002) and (<b>B</b>) urban populations (<i>r</i> = 0.39, <i>p</i> = 0.01).</p

Map of the predicted probabilities that five-fold reductions will interrupt transmission in Southeast Asia.

<p>The predicted probabilities that a control effort with a five-fold reduction would interrupt transmission are shown for Southeast Asia, using the same masking of high transmission areas (<i>R</i>₀>10) and mapping assumptions as for <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003434#pcbi-1003434-g004" target="_blank"><b>Figure 4</b></a>. Areas that appear to be uniform may have small-scale heterogeneities in transmission that are beyond the scale of this map. Map pixel size is 5 km².</p