Peter W. Gething

TL;DR: This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time to provide reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels.

TL;DR: The fraction of febrile children with positive malaria smears per homestead and the mean age of children with malaria per Homestead were inversely correlated, indicating that children in homesteads at higher transmission acquire immunity more rapidly.

TL;DR: The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources to better understand many of the Earth’s land surface processes.

TL;DR: The evidence of the biological limits of its contemporary distribution and the global population at risk (PAR) of the disease within endemic countries indicates that the public health significance of P. vivax is likely to have been seriously underestimated.

TL;DR: This work quantifies changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework and provides high-resolution, standardized estimates of housing conditions across sub-African Africa, providing a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals.