Estimation of Malaria Symptom Data Set using Hidden Markov Model
Drinold Mbete *
Department of Mathematics, Masinde Muliro University of Science and Technology, P. O Box 190-50100, Kakamega, Kenya.
Kennedy Nyongesa
Department of Mathematics, Masinde Muliro University of Science and Technology, P. O Box 190-50100, Kakamega, Kenya.
Joseph Rotich
Vice Chancellor,Laikipia University, P. O Box 1100-20300, Nyahururu, Kenya.
*Author to whom correspondence should be addressed.
Abstract
Clinical study of malaria presents a modeling challenge as patients disease status and progress is partially observed and assessed at discrete clinic visit times. Since patients initiate visits based on symptoms, intense research has focused on identication of reliable prediction for exposure, susceptibility to infection and development of severe malaria complications. Despite detailed literature on malaria infection and transmission, very little has been documented in the existing literature on malaria symptoms modeling, yet these symptoms are common. Furthermore, imperfect diagnostic tests may yield misclassication of observed symptoms. Place and Duration of Study: The main objective of this study is to develop a Bayesian Hidden Markov Model of Malaria symptoms in Masinde Muliro University of Science and Technology student population. An expression of Hidden Markov Model is developed and the parameters estimated through the forward-backward algorithm.
Keywords: Hidden markov model, algorithm, forward variable, symptom, backward variable.