scholarly journals Nonlinear Markov Chain Modelling of the Novel Coronavirus (Covid-19) Pandemic

2020 ◽  
Author(s):  
Muammer Catak ◽  
Necati Duran
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Markov Chain Model ◽  
Chain Model ◽  
The Novel ◽  
The World ◽  
The Usa ◽  
Novel Coronavirus ◽  
Almost All ◽  
Markov Chain Modelling

Almost all countries around the world are struggling against the novel coronavirus (Covid-19) pandemic. In this paper, a nonlinear Markov chains model is proposed in order to analyse and to understand the behaviour of the Covid-19 pandemic. The data from China was used to build up the presented model. Thereafter, the nonlinear Markov chain model is employed to estimate the daily new Covid-19 cases in some countries including Italy, Spain, France, UK, the USA, Germany, Turkey, and Kuwait. In addition, the correlation between the daily new Covid-19 cases and the daily number of deaths is examined.

scholarly journals Determination of the Order of a Markov Chain for Daily Rainfall Data of North East India: "Application AIC criterion"

2018 ◽  
Vol 10 (1) ◽  
pp. 80-87
Author(s):  
Surobhi Deka
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Akaike Information Criterion ◽  
Markov Chain Model ◽  
Information Criterion ◽  
Chain Model ◽  
Adequate Model ◽  
First Order ◽  
North East India ◽  
North East

The paper aims at demonstrating the application of the Akaike information criterion to determine the order of two state Markov chain for studying the pattern of occurrence of wet and dry days during the rainy season (April to September) in North-East India. For each station, each day is classified as dry day if the amount of rainfall is less than 3 mm and wet day if the amount of rainfall is greater than or equal to 3 mm. We apply Markov chain of order up to three to the sequences of wet and dry days observed at seven distantly located stations in North East region of India. The Markov chain model of appropriate order for analyzing wet and dry days is determined. This is done using the Akaike Information Criterion (AIC) by checking the minimum of AIC estimate. Markov chain of order one is found to be superior to the majority of the stations in comparison to the other order Markov chains. More precisely, first order Markov chain model is an adequate model for the stations North Bank, Tocklai, Silcoorie, Mohanbari and Guwahati. Further, it is observed that second order and third order Markov chains are competing with first order in the stations Cherrapunji and Imphal, respectively. A fore-knowledge of rainfall pattern is of immense help not only to farmers, but also to the authorities concerned with planning of irrigation schemes. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.

The Impact of COVID-19 on Stock Markets

2022 ◽  
pp. 250-262
Author(s):  
Aslı Aybars ◽  
Mehtap Öner
Keyword(s):  
Literature Review ◽  
Financial Markets ◽  
Stock Returns ◽  
Empirical Studies ◽  
The Novel ◽  
The World ◽  
Fast Pace ◽  
Novel Coronavirus ◽  
Almost All ◽  
The Impact

The novel coronavirus, COVID-19, which emerged at the end of 2019 and spread to the world at a very fast pace, resulted in a pandemic affecting the finance industry besides many other industries though at varying extents. Financial markets, which can be regarded as cornerstones of each and every country's economic success, have been adversely influenced due to the fear and uncertainty arising with the emergence of the novel coronavirus at different degrees. This chapter provides a summary of a literature review based on the impact of this pandemic on stock returns and volatility in the stock exchanges of different countries and regions of the world. What has been captured as a result of this literature review is that almost all of the financial markets around the world have been influenced due to the virus. Further, industry-wise empirical studies demonstrate that not all industries are affected at the same level or even in the same direction.

scholarly journals THE INFLUENCE OF CHRONIC DISEASES ON THE MANIFESTATION OF COVID-19 INFECTION

2021 ◽  
pp. 126-132
Author(s):  
S. O. Yastremska ◽  
O. M. Krekhovska-Lepiavko ◽  
B. A. Lokay ◽  
O. V. Bushtynska ◽  
S. V. Danchak
Keyword(s):  
Chronic Diseases ◽  
Case Fatality ◽  
The Novel ◽  
Fatality Rate ◽  
The World ◽  
Control And Prevention ◽  
The Usa ◽  
Life Threatening ◽  
One Year ◽  
Novel Coronavirus

Summary. The first known case of infection from the novel coronavirus was recorded almost one year ago, in China’s Hubei province. The city of Wuhan was infamous the world over as the original virus epicenter, seeing more than half of China’s reported cases and deaths. The outbreak of COVID-19 virus, as sickened more than 14.7 million people. At least 610.200 people have died. The aim of the study – to analyze and systematize the literature data about the influence of chronic diseases on the manifestation of COVID-19 infection. Materials and Methods. The study uses publications of the world scientific literature on COVID-19 infection, in particular the causes and mechanisms of its development, treatment, complications and its consequences as well as the influence of different chronic disorders on the course of COVID-19. Results. A sample of patients hospitalized with COVID-19 across 14 states of the USA in March was analyzed by The Centers for Disease Control and Prevention. It was found that many (89 %) had underlying health problem and 94 % of patients were at the age 65 and older. The case fatality rate for those under age 60 was 1.4 percent. For those over age 60, the fatality rate jumps to 4.5 percent. The older the population, the higher the fatality rate. For those 80 and over, Covid-19 appears to have a 13.4 percent fatality rate. Moreover, it was recognized, that older adults don't present in a typical way of the course of different disorders, and we're seeing that with Covid-19 as well. Conclusions. Chronic diseases and conditions are on the rise worldwide. COVID-19 became the most challenging pandemic influencing all countries worldwide. Chronic diseases are suggested to be one of the main causes of different life-threatening complications of COVID-19 infection and one of the main factors of poor prognosis for the patients.

scholarly journals Higher order Markov Chain Model for Synthetic Generation of Daily Streamflows

2018 ◽  
Vol 19 (3) ◽  
pp. 449
Author(s):  
A. G. C. Pereira ◽  
F. A. S. Sousa ◽  
B. B. Andrade ◽  
Viviane Simioli Medeiros Campos
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Markov Chain Model ◽  
Chain Model ◽  
Order Markov Chain ◽  
Chain Order ◽  
Higher Order Markov Chain ◽  
Best Fit ◽  
Synthetic Generation ◽  
Better Than

The aim of this study is to get further into the two-state Markov chain model for synthetic generation daily streamflows. The model proposed in Aksoy and Bayazit (2000) and Aksoy (2003) is based on a two Markov chains for determining the state of the stream. The ascension curve of the hydrograph is modeled by a two-parameter Gamma probability distribution function and is assumed that a recession curve of the hydrograph follows an exponentially function. In this work, instead of assuming a pre-defined order for the Markov chains involved in the modelling of streamflows, a BIC test is performed to establish the Markov chain order that best fit on the data. The methodology was applied to data from seven Brazilian sites. The model proposed here was  better than that one proposed by Aksoy but for two sites which have the lowest time series and are located in the driest regions.

scholarly journals Impact of COVID-19 Outbreak on Financial Reporting in the Light of the International Financial Reporting Standards (IFRS) (An Empirical Study)

2020 ◽  
Vol 5 (2) ◽  
pp. p21
Author(s):  
Hasan El-Mousawi ◽  
Hasan Kanso
Keyword(s):  
Financial Reporting ◽  
Global Economy ◽  
Certified Public Accountants ◽  
The Novel ◽  
Financial Reporting Standards ◽  
The World ◽  
Public Accountants ◽  
Novel Coronavirus ◽  
Almost All ◽  
The Impact

The outbreak of a novel type of Coronavirus (COVID-19) in the majority of countries around the world has had many negative implications on almost all aspects of life. Currently, about a quarter of the population of Earth is quarantined at their homes, social distancing is effective everywhere, almost all industries have ceased their activities, and various businesses are either closed down or working from home. Procedures taken by governments or local authorities to improve their ability to contain the outbreak have impacted the global economy, which in turn will have many consequences on financial reporting of organizations. This study examines the impact of the novel Coronavirus outbreak on financial reporting of organizations from the viewpoint of Certified Public Accountants in Lebanon. The researchers have used a descriptive-analytical approach and have constructed a well-structured five-point Likert style questionnaire as the study tool. The questionnaire was distributed to a sample chosen from the population of certified public accountants in Lebanon. The random sample consisted of 300 practitioners of the profession, and 221 of them responded; all of which were valid for testing and analysis. The study reached some important findings mainly that the COVID-19 outbreak has had a significant impact on the financial reporting of businesses according to the opinions of Certified Public Accountants (CPAs) in Lebanon, and the researchers had some recommendations as a result.

scholarly journals A Method to Model Outbreaks of New Infectious Diseases with Pandemic Potential such as COVID-19

2020 ◽  
Author(s):  
Willem G. Odendaal
Keyword(s):  
Infectious Disease ◽  
Monte Carlo ◽  
Financial Markets ◽  
Infectious Agent ◽  
The Novel ◽  
Acceptable Accuracy ◽  
The World ◽  
Travel Restrictions ◽  
The Usa ◽  
Novel Coronavirus

AbstractThe emergence of the novel coronavirus (a.k.a. COVID-19, SARS-CoV-2) out of Wuhan, Hubei Province, China caught the world by surprise. As the outbreak began to spread outside of China, too little was known about the virus to model its transmission with any acceptable accuracy. World governments responded to rampant misinformation about the virus leading to collateral disasters, such as plunging financial markets, that could have been avoided if better models of the outbreak had been available. This is an engineering approach to model the spread of a new infectious disease from sparse data when little is known about the infectious agent itself. The paper is not so much about the model itself - because there are many good scientific approaches to model an epidemic - as it is about crunching numbers when there are barely any numbers to crunch. The coronavirus outbreak in USA is used to illustrate the implementation of this modeling approach. A Monte Carlo approach is implemented by using incubation period and testing efficiency as variables. Among others it is demonstrated that imposing early travel restrictions from infected countries slowed down the outbreak in the USA by about 26 days.

A Comparison of Stochastic Models for the Interarrival Times of Packets in a Computer Network

2011 ◽  
pp. 248-257 ◽  
Author(s):  
Dennis Guster ◽  
Semyon Litvinov ◽  
Mary Richardson ◽  
David Robinson
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Power Law ◽  
Stochastic Models ◽  
Computer Network ◽  
Markov Chain Model ◽  
Actual Data ◽  
Chain Model ◽  
Power Law Process ◽  
Simulation Techniques

Because of the complexity and over-subscription of today’s networks, the importance of valid simulation techniques to aid in determining sound network design is paramount. A number of studies have shown that the theoretical exponential packet interarrival rates are not appropriate for many network installations. This chapter compares two other modeling techniques: the power law process and Markov chains to the exponential and actual data taken from a ten-minute segment. The results reveal that the exponential and power law models are a poor match to the actual data. The Markov chain model, although not perfect, yielded some promising results.

Application of Markov Chains to Identification of Turbine Engine Dynamic Models

2002 ◽  
Author(s):  
V. Yu. Arkov ◽  
G. G. Kulikov ◽  
T. V. Breikin
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Gas Turbines ◽  
Dynamic Models ◽  
Markov Chain Model ◽  
Dynamic Modelling ◽  
Parametric Models ◽  
Chain Model ◽  
Model Estimation ◽  
Identifiability Analysis

The paper addresses the problem of dynamic modelling of gas turbines for condition monitoring purposes. Identification of dynamic models is performed using a novel Markov chain technique. This includes identifiability analysis and model estimation. When identifying the model, experimental data should be sufficiently informative for identification. So far, identifiability analysis is weak formed and workable solutions are still to be developed. A possible technique is proposed based on non-parametric models in the form of controllable Markov chains. The second step in systems identification is the model estimation. At this stage, Markov chains are introduced to provide more functionality and versatility for dynamic modelling of gas turbines. The Markov chain model combines the deterministic and stochastic components of the engine dynamics within a single model, thus providing more exact and adequate description of the real system behaviour and leading to far more accurate health monitoring.

scholarly journals Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3582 ◽  
Author(s):  
Antonios Karatzoglou ◽  
Dominik Köhler ◽  
Michael Beigl
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Markov Chain Model ◽  
Predictive Performance ◽  
Chain Model ◽  
Semantic Level ◽  
Trajectory Mining ◽  
Solid Foundation ◽  
Semantic Similarity Analysis ◽  
High Level

In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users’ activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation.

scholarly journals Contributions of Latin American researchers in the understanding of the novel coronavirus outbreak: a literature review

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9332 ◽  
Author(s):  
Karen Y. Fiesco-Sepúlveda ◽  
Luis Miguel Serrano-Bermúdez
Keyword(s):  
Literature Review ◽  
Latin American ◽  
Web Of Science ◽  
The Novel ◽  
The World ◽  
The Usa ◽  
Novel Virus ◽  
Scopus Database ◽  
Novel Coronavirus ◽  
Socioeconomic Crisis

This article aimed to give the visibility of Latin American researchers’ contributions to the comprehension of COVID-19; our method was a literature review. Currently, the world is facing a health and socioeconomic crisis caused by the novel coronavirus, SARS-CoV-2, and its disease COVID-19. Therefore, in less than 4 months, researchers have published a significant number of articles related to this novel virus. For instance, a search focused on the Scopus database on 10 April 2020, showed 1,224 documents published by authors with 1,797 affiliations from 80 countries. A total of 25.4%, 24.0% and 12.6% of these national affiliations were from China, Europe and the USA, respectively, making these regions leaders in COVID-19 research. In the case of Latin America, on 10 April 2020, we searched different databases, such as Scopus, PubMed and Web of Science, finding that the contribution of this region was 2.7 ± 0.6% of the total publications found. In other words, we found 153 publications related to COVID-19 with at least one Latin American researcher. We summarized and processed the information from these 153 publications, finding active participation in topics like medical, social and environmental considerations, bioinformatics and epidemiology.

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