scholarly journals Partition Markov Model for Covid-19 Virus

4open ◽  
2020 ◽  
Vol 3 ◽  
pp. 13
Author(s):  
Jesús Enrique García ◽  
Verónica Andrea González-López ◽  
Gustavo Henrique Tasca
Keyword(s):  
Markov Model ◽  
Complete Genome Sequence ◽  
Complete Genome ◽  
Markov Models ◽  
Transition Probabilities ◽  
Specific Structure ◽  
Comparison Study ◽  
Sequence Profile ◽  
The Past ◽  
Finite Memory

In this paper, we investigate a specific structure within the theoretical framework of Partition Markov Models (PMM) [see García Jesús and González-López, Entropy 19, 160 (2017)]. The structure of interest lies in the formulation of the underlying partition, which defines the process, in which, in addition to a finite memory o associated with the process, a parameter G is introduced, allowing an extra dependence on the past complementing the dependence given by the usual memory o. We show, by simulations, how algorithms designed for the classic version of the PMM can have difficulties in recovering the structure investigated here. This specific structure is efficient for modeling a complete genome sequence, coming from the newly decoded Coronavirus Covid-19 in humans [see Wu et al., Nature 579, 265–269 (2020)]. The sequence profile is represented by 13 units (parts of the state space’s partition), for each of the 13 units, their respective transition probabilities are computed for any element of the genetic alphabet. Also, the structure proposed here allows us to develop a comparison study with other genomic sequences of Coronavirus, collected in the last 25 years, through which we conclude that Covid-19 is shown next to SARS-like Coronaviruses (SL-CoVs) from bats specimens in Zhoushan [see Hu et al., Emerg Microb Infect 7, 1–10 (2018)].

A semi-markov model for clinical trials

1965 ◽  
Vol 2 (02) ◽  
pp. 269-285 ◽  
Author(s):  
George H. Weiss ◽  
Marvin Zelen
Keyword(s):  
Clinical Trials ◽  
Probability Distribution ◽  
Acute Leukemia ◽  
Markov Model ◽  
Markov Models ◽  
Transition Probabilities ◽  
State Transitions ◽  
Semi Markov Processes ◽  
Absorbing States ◽  
Epidemic Diseases

This paper applies the theory of semi-Markov processes to the construction of a stochastic model for interpreting data obtained from clinical trials. The model characterizes the patient as being in one of a finite number of states at any given time with an arbitrary probability distribution to describe the length of stay in a state. Transitions between states are assumed to be chosen according to a stationary finite Markov chain.Other attempts have been made to develop stochastic models of clinical trials. However, these have all been essentially Markovian with constant transition probabilities which implies that the distribution of time spent during a visit to a state is exponential (or geometric for discrete Markov chains). Markov models need also to assume that the transitions in the state of a patient depend only on absolute time whereas the semi-Markov model assumes that transitions depend on time relative to a patient. Thus the models are applicable to degenerative diseases (cancer, acute leukemia), while Markov models with time dependent transition probabilities are applicable to colds and epidemic diseases. In this paper the Laplace transforms are obtained for (i) probability of being in a state at timet, (ii) probability distribution to reach absorption state and (iii) the probability distribution of the first passage times to go from initial states to transient or absorbing states, transient to transient, and transient to absorbing. The model is applied to a clinical study of acute leukemia in which patients have been treated with methotrexate and 6-mercaptopurine. The agreement between the data and the model is very good.

scholarly journals Testing the Semi Markov Model Using Monte Carlo Simulation Method for Predicting the Network Traffic

2020 ◽  
pp. 713-720
Author(s):  
Shirin Kordnoori ◽  
Hamidreza Mostafaei ◽  
Shaghayegh Kordnoori ◽  
Mohammadmohsen Ostadrahimi
Keyword(s):  
Monte Carlo ◽  
Markov Model ◽  
Markov Processes ◽  
Network Traffic ◽  
Markov Models ◽  
Transition Probabilities ◽  
Simulation Method ◽  
Monte Carlo Algorithm ◽  
Statistical Characteristics ◽  
Semi Markov Processes

Semi-Markov processes can be considered as a generalization of both Markov and renewal processes. One of the principal characteristics of these processes is that in opposition to Markov models, they represent systems whose evolution is dependent not only on their last visited state but on the elapsed time since this state. Semi-Markov processes are replacing the exponential distribution of time intervals with an optional distribution. In this paper, we give a statistical approach to test the semi-Markov hypothesis. Moreover, we describe a Monte Carlo algorithm able to simulate the trajectories of the semi-Markov chain. This simulation method is used to test the semi-Markov model by comparing and analyzing the results with empirical data. We introduce the database of Network traffic which is employed for applying the Monte Carlo algorithm. The statistical characteristics of real and synthetic data from the models are compared. The comparison between the semi-Markov and the Markov models is done by computing the Autocorrelation functions and the probability density functions of the Network traffic real and simulated data as well. All the comparisons admit that the Markovian hypothesis is rejected in favor of the more general semi Markov one. Finally, the interval transition probabilities which show the future predictions of the Network traffic are given.

scholarly journals A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ning Wang ◽  
Shu-dong Sun ◽  
Zhi-qiang Cai ◽  
Shuai Zhang ◽  
Can Saygin
Keyword(s):  
Markov Model ◽  
State Transition ◽  
Markov Models ◽  
Transition Probabilities ◽  
Independence Assumption ◽  
Modeling And Analysis ◽  
Maintenance Systems ◽  
Dependent State ◽  
Real World Problems

Realistic prognostic tools are essential for effective condition-based maintenance systems. In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM) is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM), including the Hidden Semi-Markov Model (HSMM): (1) it allows explicit modeling of state transition probabilities between the states; (2) it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3) it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems. To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed. The demonstration and evaluation of the proposed methodology is carried out through a case study. The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.

A semi-markov model for clinical trials

1965 ◽  
Vol 2 (2) ◽  
pp. 269-285 ◽  
Author(s):  
George H. Weiss ◽  
Marvin Zelen
Keyword(s):  
Clinical Trials ◽  
Probability Distribution ◽  
Acute Leukemia ◽  
Markov Model ◽  
Markov Models ◽  
Transition Probabilities ◽  
State Transitions ◽  
Semi Markov Processes ◽  
Absorbing States ◽  
Epidemic Diseases

This paper applies the theory of semi-Markov processes to the construction of a stochastic model for interpreting data obtained from clinical trials. The model characterizes the patient as being in one of a finite number of states at any given time with an arbitrary probability distribution to describe the length of stay in a state. Transitions between states are assumed to be chosen according to a stationary finite Markov chain.Other attempts have been made to develop stochastic models of clinical trials. However, these have all been essentially Markovian with constant transition probabilities which implies that the distribution of time spent during a visit to a state is exponential (or geometric for discrete Markov chains). Markov models need also to assume that the transitions in the state of a patient depend only on absolute time whereas the semi-Markov model assumes that transitions depend on time relative to a patient. Thus the models are applicable to degenerative diseases (cancer, acute leukemia), while Markov models with time dependent transition probabilities are applicable to colds and epidemic diseases. In this paper the Laplace transforms are obtained for (i) probability of being in a state at timet, (ii) probability distribution to reach absorption state and (iii) the probability distribution of the first passage times to go from initial states to transient or absorbing states, transient to transient, and transient to absorbing. The model is applied to a clinical study of acute leukemia in which patients have been treated with methotrexate and 6-mercaptopurine. The agreement between the data and the model is very good.

scholarly journals Complete Genome Sequence of a Burkholderia mallei Isolate Originating from a Glanderous Horse from the Kingdom of Bahrain

2016 ◽  
Vol 4 (6) ◽  
Author(s):  
Mandy C. Elschner ◽  
Prasad Thomas ◽  
Falk Melzer
Keyword(s):  
Genome Sequence ◽  
Complete Genome Sequence ◽  
Complete Genome ◽  
Animal Health ◽  
Burkholderia Mallei ◽  
Notifiable Disease ◽  
The Past ◽  
The World ◽  
World Organization

Burkholderia mallei is a zoonotic agent causing glanders, a notifiable disease in equines. During the past decades glanders emerged, and the Kingdom of Bahrain reported outbreaks to the World Organization of Animal Health in 2010 and 2011. This paper presents the complete genome sequence of the Burkholderia mallei strain 11RR2811 Bahrain1.

scholarly journals Complete Genome Sequence of Thermus thermophilus Strain HB5018, Isolated from Mine Hot Spring in Japan

2021 ◽  
Vol 10 (10) ◽  
Author(s):  
Kentaro Miyazaki ◽  
Toshiyuki Moriya ◽  
Naoki Nemoto ◽  
Tairo Oshima ◽  
Kei Yura ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Complete Genome Sequence ◽  
Complete Genome ◽  
Type Strain ◽  
Hot Spring ◽  
Thermus Thermophilus ◽  
Species Conservation ◽  
Average Nucleotide Identity ◽  
Content Type ◽  
The Past

ABSTRACT We isolated Thermus thermophilus strain HB5018 from Mine Hot Spring in Japan, where the type strain HB8 was isolated nearly half a century ago. The complete genome sequence of HB5018 showed 99.1% average nucleotide identity with HB8, suggesting strict species conservation in the habitat over the past 50 years.

scholarly journals Complete Genome Sequence of Serogroup B Neisseria meningitidis Strain C311

2021 ◽  
Vol 10 (42) ◽  
Author(s):  
Freda E.-C. Jen ◽  
John M. Atack ◽  
Yuan Zhang ◽  
Jennifer L. Edwards ◽  
Michael P. Jennings
Keyword(s):  
Neisseria Meningitidis ◽  
Genome Sequence ◽  
Complete Genome Sequence ◽  
Complete Genome ◽  
Gc Content ◽  
Content Type ◽  
The Past

Neisseria meningitidis strain C311 has been widely used to study meningococcal pathogenesis in the past 30 years, but its genome is not available. Here, we report that the complete C311 genome is 2,311,508 bp in length, contains a total of 2,274 genes, and has a GC content of 51.25%.

scholarly journals Near-Complete Genome Sequence of a Swine Norovirus GII.11 Strain Detected in Japan in 2018

2020 ◽  
Vol 9 (17) ◽  
Author(s):  
Ayaka Okada ◽  
Yasuo Inoshima
Keyword(s):  
Genome Sequence ◽  
Complete Genome Sequence ◽  
Complete Genome ◽  
Genome Sequences ◽  
The Past ◽  
Norovirus Gii

Here, we report the near-complete genome sequence of swine norovirus strain SwNoV/Sw1/2018/JP. The genome was genetically similar (90.2%) to that of the only other swine norovirus strain previously detected in Japan (SW/NV/swine43/JP). In conclusion, genome sequences of swine noroviruses in Japan have not been changed significantly in the past 15 years.

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