@inproceedings{20b758b584b241aca7cb8243d9c6ab58,
title = "Parameter Estimation in a New Markov Jump Process Compartmental Model with Missing Data",
abstract = "In this paper, a novel compartmental model for a disease epidemic dynamics with both symptomatic and asymptomatic disease transmissions; exposure, vaccinated and hospitalized states; and recovery and death states is derived and studied. The dynamic model is a discrete-time approximation of a Markov jump processes with inter-jump times between states that are exponentially distributed. This study addresses the statistical inference challenges in compartmental models for disease dynamics exhibiting numerous states with missing data, such as, asymptomatic infectiousness, exposure to disease, and recovery from an asymptomatic infectious state. The rigorous method of EM-algorithm is employed to find Maximum-Likelihood estimators for the disease parameters in the model. This research is motivated by infectious diseases such as COVID-19, with non-observable compartments requiring data imputation statistical methods for parameter estimation. Numerical simulation results are presented.",
keywords = "62F10, 92B15, Conditional expectation, EM-algorithm, Markov jump process, Maximum-likelihood estimator, Multinomial distribution, SVIS model, Serial exponential distribution",
author = "Divine Wanduku and Ivy Collins",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 1st Southern Georgia Mathematics Conference, SGMC 2021 ; Conference date: 02-04-2021 Through 03-04-2021",
year = "2024",
doi = "10.1007/978-3-031-69710-4_7",
language = "English",
isbn = "9783031697098",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer",
pages = "141--179",
editor = "Divine Wanduku and Shijun Zheng and Zhan Chen and Andrew Sills and Haomin Zhou and Ephraim Agyingi",
booktitle = "Applied Mathematical Analysis and Computations II - 1st SGMC",
address = "Germany",
}
