Estimation of Malaria Symptom Data Set using Hidden Markov Model

Main Article Content

Drinold Mbete
Kennedy Nyongesa
Joseph Rotich

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.

Article Details

How to Cite
Mbete, D., Nyongesa, K., & Rotich, J. (2019). Estimation of Malaria Symptom Data Set using Hidden Markov Model. Asian Journal of Probability and Statistics, 4(2), 1-29. https://doi.org/10.9734/ajpas/2019/v4i230110
Section
Original Research Article

References

Mandal S, Sarkar RR, Sinha S. Mathematical models of malaria-a review. Malaria Journal.2011;10:202.

WHO. Malaria Fact Sheet 2017 Report; 2017.

WHO. World Malaria Report; 2012.

Martins, et al. Clustering symptoms of non-severe malaria in semi-immune Amazonian patients. Peer J; 2015.

Dondorp AM, Day NP. The treatment of severe malaria. Trans R Soc Trop Med Hyg. 2007;101:633-634.22 Mbete et al.; AJPAS, 4(2), 1-29, 2019; Article no.AJPAS.49517

Cholewa M, Gomb P. Estimation of the number of states for gesture recognition with hidden markov models based on the number of critical points in time sequence. Recognition Letters. 2013;34(5):574-9.

Farsi H, Saleh R. Implementation and optimization of a speech recognition system based on hidden Markov model using genetic algorithm. Intelligent Systems (ICIS), 2014 Iranian Conference on. IEEE; 2014.

Vimala K. Stress causing arrhythmia detection from ECG signal using HMM. International Journal of Innovative Research in Computer and Communication Engineering. 2014;2(10):6079-85.

Li HM FL Y, Wang P, Yan JZ. Hidden markvo models based research on lung cancer progress modeling. Research Journal of Applied Sciences, Engineering and Technology. 2013;6(13):2470-3.

Lee HK, Lee J, Kim H, Ha JY, Lee KJ. Snoring detection using a piezo snoring sensor based on hidden Markov models. Physiological Measurement. 2013;34(5):41-45.

Liu Ying, Shuang Li, Fuxin Li, Le Song, James M Rehg, Ecient learning of Continuous-time hidden markov models for disease progression. In Advances in Neural Information Processing Systems. 2015;3600-3608.

Barber C, Bockhorst J, Roebber P. Auto-regressive HMM inference with incomplete data for short-horizon wind forecasting. Adv.Neural Inf Process Syst; 2010.

Wu H, Rojai J, Lin H, Harada K. Introspection with bayesian non-parametric vector autoregressive hidden markov model; 2017.

Tuncel KS, Baydogan MG. Autoregressive forest for multivariate time series modeling. Pattern recognition. 2018;73:202-215.

Ferguson JD. Variable duration models for speech in proceeding of the symposium on the application of hmm to text and speech ed. J.D Ferguson, Princeton NJ. 1980;143-179.

Rabinner LR. A tutorial on hidden markov model and selected application in speech recognition. Proceeding of the IEEE. 1989;77:257-285.

Xu J, Zeger S. Joint analysis of longitudinal data comprising repeated measures and time to events. Journal of the Royal Statistical Society series C. Applied statistics 2001;50:375-87.

Zammit, Nicola N, George Streftaris, Gavin J. Gibson, Ian J. Deary, Brian M. Frier. Modelling the consistency of hypoglycaemic symptoms:High variability in diabetes. Diabetes Technology and Therapeutics. 2011;13(5)571-578.

Xing Z, Nicholson B, Jimenez M, Velderman T, Hudson L, Lucas J, Duson D, Zaas A, Woods C, Geofrey G, Carin L. Bayesian modeling of temporal properties of infectious disease in college student population. Journal of Applied Statistics. 2014;41(6):1358-1382.

Masinde Muliro University of Science and Technology Student Health Facility Records; 2018.