Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting.

With the growing popularity of digital currencies known as cryptocurrencies, there is a need to develop models capable of robustly analyzing and predicting the value of future returns in these markets. In this article, we extract behavior rules to predict the values of future returns in the Bitcoin, Ethereum, Litecoin, and Ripple closing series. We used categorical data in the analyses and Markov chain models from the first to the tenth order to propose a new way of establishing possible future scenarios, in which we analyze the dependence of memory on the dynamics of the process. We used the measurements of accuracy Mean Quadratic Error, Absolute Error Mean Percentage, and Absolute Standard Deviation for the choice of the best models. Our findings reveal that cryptocurrencies have long-range memory. Bitcoin, Ethereum, and Ripple exposed seven steps of memory, while Litecoin displayed nine memory steps. From the transitions between states that happened the most, we defined decision rules that assisted in the definition of future returns in the series. Our results can support the decisions of traders, investors, crypto-traders, and policy-makers.

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak.

We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order r for m chains consisting of s possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, rm2s2+2, remarkably lower than msrm+1 required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial-temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.

Hierarchical Bayesian hedonic regression analysis of Japanese rice wine: is the price right?

PurposeThis paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices.Design/methodology/approachThe datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan's largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC.FindingsThe estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model.Originality/valueTo the best of the authors' knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.

How the COVID-19 Pandemic Affected Developing Countries: the Tunisian Investigation

The consequences of COVID-19 vary considerably from country to country and from sector to sector. In this paper, we examine how employment in sectors of Tunisian economy is being affected by the COVID-19 pandemic. For this purpose, we apply the Markov chain approach. This method has the merit to model a system that changes states according to a transition rule that depends only on the current state. We find that the COVID-19 have a negative impact on the employment in industry and in service. Moreover, the agricultural sector benefits most from COVID-19. It is important to plan for economic measures in order to support the resilience of economic establishments, particularly small- and medium-sized enterprises.

Forecasting of Indonesian Digital Economy based on Available New Start-up

Since the last 5 years, digital economy is growing steadily in Indonesia. Right now, the digital economy faces some potential problems and Covid-19 pandemic. This paper presents current data of the national Gross Domestic Product (GDP) and other GDPs (billion IDR) and the number of start-up, and predicts near some categories of future GDP and numbers of available new start-up for the next few years. The forecast will use Markov chain analysis. The results indicate that, while there are problems faced by the digital economy industry, the GDP and numbers of start-up are significantly increasing.

A Discrete Model for the Evolution of Infection Prior to Symptom Onset

We consider a between-host model for a single epidemic outbreak of an infectious disease. According to the progression of the disease, hosts are classified in regard to the pathogen load. Specifically, we are assuming four phases: non-infectious asymptomatic phase, infectious asymptomatic phase (key-feature of the model where individuals show up mild or no symptoms), infectious symptomatic phase and finally an immune phase. The system takes the form of a non-linear Markov chain in discrete time where linear transitions are based on geometric (main model) or negative-binomial (enhanced model) probability distributions. The whole system is reduced to a single non-linear renewal equation. Moreover, after linearization, at least two meaningful definitions of the basic reproduction number arise: firstly as the expected secondary asymptomatic cases produced by an asymptomatic primary case, and secondly as the expected number of symptomatic individuals that a symptomatic individual will produce. We study the evolution of infection transmission before and after symptom onset. Provided that individuals can develop symptoms and die from the disease, we take disease-induced mortality as a measure of virulence and it is assumed to be positively correlated with a weighted average transmission rate. According to our findings, transmission rate of the infection is always higher in the symptomatic phase yet under a suitable condition, most of the infections take place prior to symptom onset.

Workforce burnout from a systems science perspective

Workforce burnout is an increasing problem across many industries and professions, with significant impacts on both manufacturing and service sectors. For example, burnout is a major problem for more than 80% of healthcare systems, with the costs of replacing doctors who leave their job and reduced clinical hours related to burnout are estimated at $4.6 billion annually. Productivity is reduced, mental health is affected, family relations are weakened, and a solution to all of this is not clear. More than 10 years ago, the global cost of burnout was estimated to exceed $300 billion annually. Major societal events, such as the recent ongoing COVID-19 pandemic or political unrest, can further exacerbate individual burnout and its impacts. Advancements in burnout research over the past two decades have mostly been to develop methods for measuring and classifying burnout and in lessons learned empirically from various intervention implementations, but with little-to-no analytic modeling research to help inform effective policies. In a recent report in fact, the National Academy of Medicine emphasized the need to develop analytic models that better quantify the extent of the problem in a way that translates into actionable results in addition to approaches for understanding the impact of interventions. The overall aim of our proposed research accordingly is to develop and apply analytic disease progression models to help understand burnout dynamics and evaluate the long-term benefits of interventions prior to wide-scale implementation testing. The proposed dissertation includes three fundamental contributions. First, we develop and introduce two disease progression models of individual and organizational burnout based on Markov chains, parameterized and linked from limited data via optimization and simulation models. We also illustrate the use of the developed models to estimate and compare the relative effectiveness of various strategies and interventions to reduce burnout, with a focus on estimating long-term impacts from limited early testing data, contributing to pre-randomized trial methods. Second, we leverage the models to estimate the effect of COVID-19 on two healthcare professional populations in two case studies. Finally, we propose several potential methodological extensions to disease progression modeling including investigating the effect of higher order nesting, bootstrapping and time non homogeneity. Results indicate that the disease progression models of the proposed type can accurately model individual and institutional burnout progression to help better understand the dynamics of burnout and analyze the effectiveness of potential interventions to make more informed decisions. Sensitivity analysis investigates the impact of data limitations on model accuracy, while sampling provides limits for model results. Model extensions provide empirical approach to the time non-homogenous problem which if approached mathematically requires extensive longitudinal data. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A Stochastic Model for an Input Control Problem in a Two-Level Supply Chain for Production-Time-Dependent Products with Random Demands

Inspired by the poultry farming process, we studied an input control problem in a two-level supply chain for production-time-dependent products with random demands. Poultry farms deliver chicks in batches, and the raising process begins at timed intervals. Chicks become broilers after a predetermined raising time. The broilers in the process are shipped to manufacturing plants to satisfy the demand. The remaining chicks grow to the next product size to satisfy the demands for larger chickens. This procedure is repeated until the chicks are fully grown. After the chicks are grown to satisfy the demand for the largest size, the remaining chicks are discarded. In this paper, a stochastic model is presented to study an input control problem in the poultry farming process. Because of the production density, feed, and temperature control, one important issue in the operation of a poultry company is the determination of the raising interval and quantity of input (chicks). While existing mathematical models can provide effective information on the production-planning problem of systems, research has not been conducted on cases of random demand. Identifying a recursive structure and Markovian property for the number of raw materials (chicks) and the unfulfilled demand for each product type in the system, we demonstrate that embedded Markov chain models can be obtained. The equilibrium probabilities of the models can be calculated using matrix analytic methods or probability generating functions. Various numerical experiments are conducted to analyze how performance measures such as amount of disposal, unsatisfied demands, and total cost (considering disposal cost and opportunity cost) change with system parameters.

Brazilian Collaborative Network For COVID-19 Modeling: Successful Experience Of Using Real-Time Science To Support Evidence-Based Decision-Making

IntroductionModeling is important for guiding policy during epidemics. The objective of this work was to describe the experience of structuring a multidisciplinary collaborative network in Brazil for modeling coronavirus disease 2019 (COVID-19) to support decision-making throughout the pandemic.MethodsResponding to a national call in June 2020 for proposals on COVID-19 mitigation projects, we established a team of investigators from public universities located in various regions throughout Brazil. The team's main objective was to model severe acute respiratory syndrome coronavirus 2 transmission dynamics in various demographic and epidemiologic settings in Brazil using different types of models and mitigation interventions. The modeling results aimed to provide information to support policy making. This descriptive study outlines the processes, products, challenges, and lessons learned from this innovative experience.ResultsThe network included 18 researchers (epidemiologists, infectious diseases experts, statisticians, and modelers) from various backgrounds, including ecology, geography, physics, and mathematics. The criteria for joining the network were having a communication channel with public health decision-makers and being involved in generating evidence for public policy. During a 24-month period, the following sub-projects were established: (i) development of a susceptible-exposed-infected-recovered-like, individual-based meta-population and Markov chain model;(ii) projection of COVID-19 transmission and impact over time with respect to cases, hospitalizations, and deaths;(iii) assessment of the impact of non-pharmacological interventions for COVID-19;(iv) evaluation of the impact of reopening schools;and (v) determining optimal strategies for COVID-19 vaccination. In addition, we mapped existing COVID-19 modeling groups nationwide and conducted a systematic review of relevant published research literature from Brazil.ConclusionsInfectious disease modeling for guiding public health policy requires interaction between epidemiologists, public health specialists, and modelers. Communicating modeling results in a non-academic format is an additional challenge, so close interaction with policy makers is essential to ensure that the information is useful. Establishing a network of modeling groups will be useful for future disease outbreaks.

Simulation of the Effects of Targeted Immunization on the Peak Number of Infections in Complex Networks

In this paper we study the effect of targeted immunization on the peak number of infections in an epidemic outbreak. For this we extend a previously developed python-based dashboard environment for the time efficient simulation-based study of SIR epidemics spread on complex network topologies, using realistic Continuous Time Markov Chain (CTMC) simulations by means of Gillespie's stochastic simulation algorithm. The new components make it possible to study targeted immunization by means of state-of-the-art methods and to visualize typical paths of infection during the temporal evolution of an epidemic. We show results obtained with different centrality measures (eigenvalue centrality of the adjacency and non-backtracking matrix, degree centrality, and average path length centrality), used in targeted immunization. In the results we focus on studying the peak number of infections (PNI). The PNI is very relevant when it comes to the practical management of an epidemic, as it determines, for instance, the number of intensive care units that are needed to offer an appropriate treatment of the disease in critical cases. However, the PNI has received much less attention in studies than the epidemic threshold, which is more relevant in the early stage of an epidemic. Our example study on classical network topologies reveal that the choice of the centrality measure for targeted immunization as well as the number of targeted nodes will have a strong impact on the drop of the PNI. Our simulation-based results on scale-free Barabasi-Albert networks show that the PNI reduction that can be achieved by using modern centrality metrics such as the non-backtracking eigenvalue drop, can lead to up to 40% lower peaks than those achieved with naïve methods such as degree based immunization (immunization of the biggest node(s)) in case of immunization of 2.5% nodes. These results underpins the crucial importance of the correct choice of the centrality metric in targeted immunization. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Investigation Statistical Methods in Medical Engineering Fields: Coronavirus (COVID-19) as Model

Many epidemic diseases spread and infect humans, including Corona pandemic, which has spread throughout the world, including our country, Iraq, and to all provinces randomly and with different rates of infection. It is difficult to control this disease and determine the exact increase or decrease in the number of people infected with it. Predicting the numbers of people affected by this epidemic supports decision-makers in health institutions to make the right decisions regarding preparedness to face it in terms of providing the infrastructure and human resources of the crisis, and the more accurate the prediction, the more influential the decisions in containing this epidemic. A new framework was proposed by studying the spread of this epidemic, which depends on random spread, so Markov sequences were used, which do not rely mainly on past data to predict the future. The aim of this research is to predict the numbers of people infected with this disease through the use of statistical methods, especially the Markov chains method, which is characterized by its ability to predict the future of cases that are characterized by randomness in spread and not relying on past data only. The accuracy of the results of this research depends on the accuracy of the data taken from the Health Department of Baghdad Governorate / Al-Rusafa side, as well as on the factors affecting the spread of this disease that were taken from the research centers concerned with this epidemic. © 2022 American Institute of Physics Inc.. All rights reserved.

Statistical Inference to the Parameter of the Inverse Power Ishita Distribution under Progressive Type-II Censored Data with Application to COVID-19 Data

The goal of the article is the inference about the parameters of the inverse power ishita distribution (IPID) using progressively type-II censored (Prog–II–C) samples. For IPID parameters, maximum likelihood and Bayesian estimates were obtained. Two bootstrap “confidence intervals” (CIs) are also proposed in addition to “approximate confidence intervals” (ACIs). In addition, Bayesian estimates for “squared error loss” (SEL) and LINEX loss functions are provided. The Gibbs within Metropolis–Hasting samplers process is used to provide Bayes estimators of unknown parameters also “credible intervals” (CRIs) of them by using the “Markov Chain Monte Carlo” (MCMC) technique. Then, an application of the suggested approaches is considered a set of real-life data this data set COVID-19 data from France of 51 days recorded from 1 January to 20 February 2021 formed of mortality rate. To evaluate the quality of the proposed estimators, a simulation study is conducted.

Research on decision-making optimization of emergency supply chain based on Markov chain and multi-objective model

In order to cope with the sudden disasters such as floods, COVID-19,etc.,a discrete time Markov chain and multi-objective programming model（DTMC-MOP） with the maximum supply satisfaction rate, the shortest supply time and the lowest supply cost was proposed to dynamically identify, analyze and respond to the emergency supply chain risk.The improved self-adaptive Non-dominated Sorting Genetic Algorithm-Ⅱ（NSGA-Ⅱ） was used to solve the optimization model, and the feasibility and effectiveness of the model were verified by testing and evaluation with standard test functions.Through the example analysis, the Pareto optimal front with higher precision and more uniform distribution was obtained.The results showed that the decision-maker could choose the appropriate emergency scheme based on the core objective of emergency management or different preferences.It provide a scientific method for the decision-making optimization of emergency supply chain, which has positive significance for ensuring the life safety of victims and maintaining the social harmony and stability.

Effective Preprocessing and Normalization Techniques for COVID-19 Twitter Streams with POS Tagging via Lightweight Hidden Markov Model

The major focus of this research work is to refine the basic preprocessing steps for the unstructured text content and retrieve the potential conceptual features for further enhancement processes such as semantic enrichment and named entity recognition. Although some of the preprocessing techniques such as text tokenization, normalization, and Part-of-Speech (POS) tagging work exceedingly well on formal text, it has not performed well when it is applied into informal text such as tweets and short messages. Hence, we have given the enhanced text normalization techniques to reduce the complexity persist over the twitter streams and eliminate the overfitting issues such as text anomalies and irregular boundaries while fixing the grammar of the text. The hidden Markov model (HMM) has been pervasively used to extract the core lexical features from the Twitter dataset and suitably adapt the external documents to supplement the extraction techniques to complement the tweet context. Using this Markov process, the POS tags are identified as states of the Markov process, and words are the desired results of the model. As this process is very crucial for the next stage of entity extraction and classification, the effective handling of informal text is considered to be important and therefore proposed the most effective hybrid approach to deal with the issues appropriately.

Monitoring Stock Market Returns: A Stochastic Approach

Financial analysis plays a major role in investing the disposable income of various economic agents. Stock markets are predominantly made up of small investors with limited information and low capabilities for a suitable analysis. Researchers, as well as practitioners, are divided over the findings on the adequacy of technical analysis in investing. This paper examines the Markov chain process in the stock market to discover the essential links and probabilities for the stocks' transition through three states of stagnation, growth, and decline (i.e., stagnant, bull, and bear markets). The subject of analysis is a randomly selected portfolio of 20 shares traded on the New York Stock Exchange. The data suggest that the portfolio relatively quickly, in four trading days, achieves equilibrium probabilities that allow a certain amount of predictability of future movements. At the same time, when analyzing the expected time intervals for the first transition, we found that the portfolio returns to a state of growth much faster than a decline. In addition, the results negate the basic habits of frequent trading, herding, and taking a short position in events of negative price fluctuations. Our research contributes towards observing regularities and stock market efficiency with a clear goal of improving expectations and technical analysis for small individual investors.

Real-time forecasting of the COVID 19 using fuzzy grey Markov: a different approach in decision-making

The ongoing epidemic SARS-CoV-2 named Corona Virus Disease (COVID-19) is highly infectious and subsequently spread all over the world affecting millions of people. Humans have never seen such a deadly disease so far, and as there is no specific drug or vaccination, the mortality rate of the disease has been increasing exponentially. This current situation exacerbated people’s restlessness and fear. Because of this pandemic, the world is travelling on a different path. This world has recovered from many disasters, but this is entirely a different situation. Today’s world is struggling in many ways to get rid of this disease. On the other hand, the number of people recovering from this disease gives us comfort. Yet, we have to take urgent precautionary measures to control this disease in all possible ways. Therefore, forecasting is one of the ways to take the necessary precautionary measures. In this paper, using fuzzy–grey–Markov model, we predict the number of affected and recovered patient count, death count using real-time data in different approaches and compared with the real data. The study concludes with important recommendations for the Indian government to manage the COVID 19 critical situation in advance.

Optimal timing of non-pharmaceutical interventions during an epidemic.

In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.

The Dynamic Effects of Urban–Rural Income Inequality on Sustainable Economic Growth under Urbanization and Monetary Policy in China

Income inequality in China has become increasingly serious since the beginning of the economic reform period in the 1970s, with urban–rural income inequality playing a large role. Urbanization policy and monetary policy are currently important economic policy tools for the Chinese government. In order to investigate the influence of inequality on the economy and to provide recommendations for ensuring the sustainability of growth, we study the effect of urban–rural income inequality on economic growth in the context of urbanization and monetary policy in China between 2002 and 2021. Using a flexible time-varying parametric structural vector auto-regression (TVP-VAR) model and a robust Markov chain Monte Carlo (MCMC) algorithm, our empirical results show that the effect is time-varying, with inequality promoting growth in the early years but affecting it adversely at later stages. Currently, urbanization mitigates inequality and promotes growth simultaneously, while easy monetary policy worsens inequality and affects growth adversely in the long term. We suggest that the authorities need to consider the implementation of policy rebalancing to ensure that the sustainability of economic development is not jeopardized because of worsening income disparity. Proactive urbanization policy and prudent monetary policy are viable rebalancing options.

Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data

This article investigates a survival analysis under randomly censored mortality distribution. From the perspective of frequentist, we derive the point estimations through the method of maximum likelihood estimation. Furthermore, approximate confidence intervals for the parameters are constructed based on the asymptotic distribution of the maximum likelihood estimators. Besides, two parametric bootstraps are implemented to construct the approximate confidence intervals for the unknown parameters. In Bayesian framework, the Bayes estimates of the unknown parameters are evaluated by applying the Markov chain Monte Carlo technique, and highest posterior density credible intervals are also carried out. In addition, the Bayes inference based on symmetric and asymmetric loss functions is obtained. Finally, Monte Carlo simulation is performed to observe the behavior of the proposed methods, and a real data set of COVID-19 mortality rate is analyzed for illustration.

ADAPTIVE TEST ALLOCATION for OUTBREAK DETECTION and TRACKING in SOCIAL CONTACT NETWORKS

We present a general framework for adaptive allocation of viral tests in social contact networks and arbitrary epidemic models. We pose and solve several complementary problems. First, we consider the design of a social sensing system whose objective is the early detection of a novel epidemic outbreak. In particular, we propose an algorithm to select a subset of individuals to be tested in order to detect the onset of an epidemic outbreak as fast as possible. We pose this problem as a hitting time probability maximization problem and use submodularity optimization and Monte Carlo techniques to obtain solutions with explicit quality guarantees. Second, once an epidemic outbreak has been detected, we consider the problem of using the data from the sensing system to obtain estimates of the initial patient and the current status of the epidemic. Finally, we consider the problem of adaptively distributing viral tests over time in order to maximize the information gained about the current state of the epidemic. We formalize this problem in terms of mutual information and propose an adaptive allocation strategy with quality guarantees. For these problems, we derive analytical solutions for any stochastic compartmental epidemic model with Markovian dynamics, as well as efficient Monte Carlo-based algorithms for non-Markovian dynamics or large networks. We illustrate the performance of the proposed framework in numerical experiments involving a model of COVID-19 applied to a real human contact network. © 2022 Society for Industrial and Applied Mathematics