State-Transition Model for Malaria Symptoms

Abstract

Aims/ objectives: To develop a state-transition model for malaria symptoms. Study design: Longitudinal study.

Place and Duration of Study: Department of Mathematics Masinde Muliro University of Science and Technology between January 2015 and December 2015.

Methodology: We included 300 students (patients) with liver malaria disease, with or without the medical history of malaria disease, physical examination for signs and symptoms for both specific and non-specific symptom, investigation of the disease through laboratory test (BS test) and diagnostic test results. the focus of this study was to develop state-transition model for malaria symptoms. Bayesian method using Markov Chain Monte Carlo via Gibbs sampling algorithm was implemented for obtaining the parameter estimates.

Results: The results of the study showed a significant association between malaria disease and observed symptoms

Conclusion: The study findings provides a useful information that can be used for predicting malaria disease in areas where Blood slide test and rapid diagnostic test for malaria disease is not possible.