Addressing challenges in routine health data reporting in Burkina Faso through Bayesian spatiotemporal prediction of weekly clinical malaria incidence.

ABSTRACT: Sub-Saharan African (SSA) countries' health systems are often vulnerable to unplanned situations that can hinder their effectiveness in terms of data completeness and disease control. For instance, in Burkina Faso following a workers' strike, comprehensive data on several diseases were unavailable for a long period in 2019. Weather, seasonal-malaria-chemoprevention (SMC), free healthcare, and other contextual data, which are purported to influence malarial disease, provide opportunities to fit models to describe the clinical malaria data and predict the disease spread. Bayesian spatiotemporal modeling was applied to weekly malaria surveillance data from Burkina Faso (2011-2018) while considering the effects of weather, health programs and contextual factors. Then, a prediction was used to

SUBMITTER: Rouamba T 

PROVIDER: S-EPMC7538437 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature