Characteristics of surveys included in the analysis.

<p>Characteristics of surveys included in the analysis.</p

Reduction in the odds of malaria infection in children aged 0–5 y sleeping under insecticide-treated nets in sub-Saharan Africa.

<p>Values to the left of the vertical line representing the null value indicate a reduction in the odds of malaria infection in users of insecticide-treated nets compared to non-users. Data are taken from 15 Demographic and Health Surveys and 14 Malaria Indicator Surveys conducted between 2008 and 2015. ORs are adjusted for age, gender, indoor residual spraying in the past 12 mo (where measured), household wealth, house type, and geographic cluster. Summary effects are from random effects analysis. Sub-groups show diagnostic test. Error bars show 95% confidence intervals. DRC, Democratic Republic of the Congo; OR, odds ratio; RDT, rapid diagnostic test.</p

Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having a direct impact on current residents and future generations. Slum mapping is one of the key problems concerning slums. Policymakers need to delineate slum settlements to make informed decisions about infrastructure development and allocation of aid. A wide variety of machine learning and deep learning methods have been applied to multispectral satellite images to map slums with outstanding performance. Since the physical and visual manifestation of slums significantly varies with geographical region and comprehensive slum maps are rare, it is important to quantify the uncertainty of predictions for reliable and confident application of models to downstream tasks. In this study, we train a U-Net model with Monte Carlo Dropout (MCD) on 13-band Sentinel-2 images, allowing us to calculate pixelwise uncertainty in the predictions. The obtained outcomes show that the proposed model outperforms the previous state-of-the-art model, having both higher AUPRC and lower uncertainty when tested on unseen geographical regions of Mumbai using the regional testing framework introduced in this study. We also use SHapley Additive exPlanations (SHAP) values to investigate how the different features contribute to our model’s predictions which indicate a certain shortwave infrared image band is a powerful feature for determining the locations of slums within images. With our results, we demonstrate the usefulness of including an uncertainty quantification approach in detecting slum area changes over time

MOESM2 of Treatment-seeking rates in malaria endemic countries

Additional file 2: Title: National level treatment-seeking and indicator data for malaria-endemic countries. Description: Treatment-seeking data extracted from national surveys and national-level indicator data from the World Bank are provided here along with the predicted estimates derived from the models described in the main text and Additional file 1

MOESM1 of Treatment-seeking rates in malaria endemic countries

Additional file 1: Supplementary information for: Gap filling country-level knowledge of treatment-seeking behaviour in malaria-endemic countries. Description: Supplementary methods, additional figures and tables regarding model development and validation are shown here

Additional file 1: of Spatio-temporal mapping of Madagascar’s Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016

Figure S1. Sample screening during the three MIS events in Madagascar, showing the progressive delay in the sampling time window. a Overall bar plots of sampling months. b–d Maps by sampling month by cluster location. (PNG 1353 kb

Additional file 9: of Predicting the geographical distributions of the macaque hosts and mosquito vectors of Plasmodium knowlesi malaria in forested and non-forested areas

Macaca leonina data. Each record of M. leonina occurrence is provided with a location and date. Duplicate records within a calendar year have been removed. Locations are classed as points (defined as <25 km2) or polygons (defined as >25 km2). (XLSX 63 kb