Interdependent Emission State's Hidden Markov Model of COVID-19 Disease Prevalence

Interdependent Emission State's Hidden Markov Model of COVID-19 Disease Prevalence | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 December 2025 V1 Latest version Share on Interdependent Emission State's Hidden Markov Model of COVID-19 Disease Prevalence Authors : Tirupathi Rao Padi , Kanimozhi V 0000-0003-3448-9062 [email protected] , and Jeevanand E S 0000-0002-7102-4179 Authors Info & Affiliations https://doi.org/10.22541/au.176673080.04002039/v1 195 views 73 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In this work, we developed a Interdependent Emission State’s Hidden Markov model (IES-HMM) for studying the spread of COVID-19 disease among southern states, namely Andhra Pradesh, Karnataka, Kerala, Telangana, Tamil Nadu, and Pondicherry. This model considered two different transition states, invisible and emission states. COVID cases registered in southern states other than Tamilnadu will be classified as ”invisible,” and emission states will be states where cases are reported in Tamilnadu. In this IES-HMM, we assume that emission states are influenced not only by invisible states but also by emission states. Another breakthrough of this study is exploring probability distributions for three-day sequences based on the conditional probabilities between invisible and emission states. The derivation of all mathematical relations for measuring the different statistical characteristics of the developed model is another significant contribution of this study. This study can also be extended to formulating optimization models for effective healthcare management during the treatment of the disease. Supplementary Material File (ies-hmm.pdf) Download 943.90 KB Information & Authors Information Version history V1 Version 1 26 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords covid-19 emission state and invisible state interdependent emission state’s hidden markov model southern states Authors Affiliations Tirupathi Rao Padi Pondicherry University View all articles by this author Kanimozhi V 0000-0003-3448-9062 [email protected] Vellore Institute of Technology View all articles by this author Jeevanand E S 0000-0002-7102-4179 CHRIST (Deemed to be University) View all articles by this author Metrics & Citations Metrics Article Usage 195 views 73 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Tirupathi Rao Padi, Kanimozhi V, Jeevanand E S. Interdependent Emission State's Hidden Markov Model of COVID-19 Disease Prevalence. Authorea . 26 December 2025. DOI: https://doi.org/10.22541/au.176673080.04002039/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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