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bayesian spatiotemporal

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

October 7, 2020 - 16:01 -- Open Access
Author(s): 
Rouamba T, Samadoulougou S, Kirakoya-Samadoulougou F
Reference: 
Sci Rep. 2020 Oct 6;10(1):16568

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.

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