Estimating the local spatio‐temporal distribution of malaria from routine health information systems in areas of low health care access and reporting

Abstract

Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.