Prediction of the SARS-CoV-2 Derived T-Cell Epitopes' Response against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error. © 2023 Tech Science Press. All rights reserved.

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models.

BACKGROUND AND OBJECTIVE: SARS-CoV-2 emerged by the end of 2019 and became a global pandemic due to its rapid spread. Various outbreaks of the disease in different parts of the world have been studied, and epidemiological analyses of these outbreaks have been useful for developing models with the aim of tracking and predicting the spread of epidemics. In this paper, an agent-based model that predicts the local daily evolution of the number of people hospitalized in intensive care due to COVID-19 is presented. METHODS: An agent-based model has been developed, taking into consideration the most relevant characteristics of the geography and climate of a mid-size city, its population and pathology statistics, and its social customs and mobility, including the state of public transportation. In addition to these inputs, the different phases of isolation and social distancing are also taken into account. By means of a set of hidden Markov models, the system captures and reproduces virus transmission associated with the stochastic nature of people's mobility and activities in the city. The spread of the virus in the host is also simulated by following the stages of the disease and by considering the existence of comorbidities and the proportion of asymptomatic carriers. RESULTS: As a case study, the model was applied to Paraná city (Entre Ríos, Argentina) in the second half of 2020. The model adequately predicts the daily evolution of people hospitalized in intensive care due to COVID-19. This adequacy is reflected by the fact that the prediction of the model (including its dispersion), as with the data reported in the field, never exceeded 90% of the capacity of beds installed in the city. In addition, other epidemiological variables of interest, with discrimination by age range, were also adequately reproduced, such as the number of deaths, reported cases, and asymptomatic individuals. CONCLUSIONS: The model can be used to predict the most likely evolution of the number of cases and hospital bed occupancy in the short term. By adjusting the model to match the data on hospitalizations in intensive care units and deaths due to COVID-19, it is possible to analyze the impact of isolation and social distancing measures on the disease spread dynamics. In addition, it allows for simulating combinations of characteristics that would lead to a potential collapse in the health system due to lack of infrastructure as well as predicting the impact of social events or increases in people's mobility.

mRNA-based COVID-19 booster vaccination is highly effective and cost-effective in Australia.

BACKGROUND: Australia implemented an mRNA-based booster vaccination strategy against the COVID-19 Omicron variant in November 2021. We aimed to evaluate the effectiveness and cost-effectiveness of the booster strategy over 180 days. METHODS: We developed a decision-analytic Markov model of COVID-19 to evaluate the cost-effectiveness of a booster strategy (administered 3 months after 2nd dose) in those aged ≥ 16 years, from a healthcare system perspective. The willingness-to-pay threshold was chosen as A$ 50,000. RESULTS: Compared with 2-doses of COVID-19 vaccines without a booster, Australia's booster strategy would incur an additional cost of A$0.88 billion but save A$1.28 billion in direct medical cost and gain 670 quality-adjusted life years (QALYs) in 180 days of its implementation. This suggested the booster strategy is cost-saving, corresponding to a benefit-cost ratio of 1.45 and a net monetary benefit of A$0.43 billion. The strategy would prevent 1.32 million new infections, 65,170 hospitalisations, 6,927 ICU admissions and 1,348 deaths from COVID-19 in 180 days. Further, a universal booster strategy of having all individuals vaccinated with the booster shot immediately once their eligibility is met would have resulted in a gain of 1,599 QALYs, a net monetary benefit of A$1.46 billion and a benefit-cost ratio of 1.95 in 180 days. CONCLUSION: The COVID-19 booster strategy implemented in Australia is likely to be effective and cost-effective for the Omicron epidemic. Universal booster vaccination would have further improved its effectiveness and cost-effectiveness.

The Predictive Power of Financial Stress on the Financial Markets Dynamics: Hidden Markov Model

This study investigates the predictive power of the financial stress on the dynamic of the Middle East and North Africa (MENA) financial market returns from 2007 to 2021. Based on a Quantile Regression, we show that financial stress has highest predictive abilities at the lower quantiles when the market is bearish. Then, we propose a Hidden Markov Model (HMM) based on the transition matrix to understand the relationship between financial stress index and the MENA stock market dynamics. We find that the effect of financial stress on stock market return reveals the persistence of regimes: Bullish state exists and persists, and has the longest conditional expected duration for the majority of MENA markets, except Bahrain, Qatar and Jordan. However, the transition probability from the bullish to the calm regime is too low for the financial market of Bahrain, United Arab Emirates and Egypt. Besides, the estimated mean returns for each regime divulge that the bearish and calm states are more attractive destination for both portfolio managers and investors. © 2022, Academy of Economics and Finance.

Learning-based systems for assessing hazard places of contagious diseases and diagnosing patient possibility

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals' infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease. • Utilizing the movement trajectories of individuals in a city to manage infection disease. • Proposing a learning-based system to assess hazardous places of a contagious disease. • Proposing a hidden Markov model to predict the probability of individuals infection. • Applying the Covid-19 outbreak in an urban environment as a case study. [Display omitted] [ FROM AUTHOR]

Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models

Using available phylogeographical data of 3585 SARS–CoV–2 genomes we attempt at providing a global picture of the virus's dynamics in terms of directly interpretable parameters. To this end we fit a hidden state multistate speciation and extinction model to a pre-estimated phylogenetic tree with information on the place of sampling of each strain. We find that even with such coarse–grained data the dominating transition rates exhibit weak similarities with the most popular, continent–level aggregated, airline passenger flight routes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Intelligent and Interactive Healthcare System ((IHS)-H-2) Using Machine Learning

There has been a gigantic stir in the world's healthcare sector for the past couple of years with the advent of the Covid-19 pandemic. The healthcare system has suffered a major setback and, with the lack of doctors, nurses, and healthcare facilities the need for an intelligent healthcare system has come to the fore more than ever before. Smart healthcare technologies and AI/ML algorithms provide encouraging and favorable solutions to the healthcare sector's challenges. An Intelligent Human-Machine Interactive system is the need of the hour. This paper proposes a novel architecture for an Intelligent and Interactive Healthcare System that incorporates edge/fog/cloud computing techniques and focuses on Speech Recognition and its extensive application in an interactive system. The focal reason for using speech in the healthcare sector is that it is easily available and can easily predict any physical or psychological discomfort. Simply put, human speech is the most natural form of communication. The Hidden Markov Model is applied to process the proposed approach as using the probabilistic approach is more realistic for prediction purposes. Ongoing projects and directions for future work along with challenges/issues are also addressed.

Cost-Effectiveness of COVID-19 Sequential Vaccination Strategies in Inactivated Vaccinated Individuals in China.

To effectively prevent and control the COVID-19 pandemic, countries have adopted a booster vaccination strategy. This study aimed to estimate the cost-effectiveness of sequential booster COVID-19 vaccination compared to two-dose inactivated vaccination in China from a societal perspective. A Markov model was developed to estimate the cost-effectiveness of sequential vaccination, including two doses of an inactivated vaccine followed by a booster shot of an inactivated vaccine, adenovirus vectored vaccine, protein subunit vaccine, or mRNA vaccine. The incremental effects of a booster shot with an inactivated vaccine, protein subunit vaccine, adenovirus vectored vaccine, and mRNA vaccine were 0.0075, 0.0110, 0.0208, and 0.0249 QALYs and saved costs of US$163.96, US$261.73, US$583.21, and US$724.49, respectively. Under the Omicron virus pandemic, the sequential vaccination among adults and the elderly (aged 60-69, 70-79, over 80) was consistently cost-saving, and a booster shot of the mRNA vaccine was more cost-saving. The results indicate that the sequential vaccination strategy is cost-effective in addressing the COVID-19 pandemic, and improving vaccination coverage among the elderly is of great importance in avoiding severe cases and deaths.

Learning-based systems for assessing hazard places of contagious diseases and diagnosing patient possibility

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals’ infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease.

Health condition prediction and covid risk detection using healthcare 4.0 techniques

Healthcare 4.0 is one of the emerging concepts that has grabbed the interest among researchers as well as the medical sector. Using the Internet of Things (IoT) and sophisticated communication technologies, it is now possible to monitor the patient from a remote area. In this paper, we design a remote health monitoring system using IoT and Machine Learning (ML) to determine the health condition of a patient. Supervised ML algorithms along with a time-series model such as Seasonal Autoregressive Integrated Moving Average (SARIMA) model are applied on the gathered data from IoT medical sensors to predict the health status of a patient. We consider a use-case of covid and compared it with our sensor data by applying the unsupervised ML algorithm, Long Short Term Memory (LSTM) along with a stochastic model, namely Markov Model to detect the risk of getting covid for a particular patient. LSTM with Markov model provides better results for detection with root mean squared error (RMSE) of 0.18 as against the RMSE of 0.45 obtained with only LSTM. We further design an optimization algorithm using “fuzzy logic” that attains optimum results in detecting the risk of getting covid.

Evaluation and Prediction of Coupling Coordinated Development of Compound System of Key State-owned Forest Region in Daxing'anling, Heilongjiang Province

Coordination is an important part of the new development philosophy. Promoting the coordinated development is the main goal of deepening the reform and development in state-owned forest region. Aiming to provide scientific basis and theoretical supports for promoting the continuous deepening of reform of state-owned forest region and realizing comprehensive and high-quality coordinated development, the key state-owned forest region in Daxing'anling, Heilongjiang Province was chosen as the research object. A compound system covering ecological conservation, industrial development, enterprise management, well-being of the people and support capability was constructed. The coupling coordination model was used to quantitatively evaluate the coupling coordination status of the compound system from 2000 to 2020. The Grey Markov model was used to predict the trend of coupling coordination development in this compound system from 2021 to 2022. Results show that, after 21-year of transformation and development, the development index of each subsystem of state-owned forest region in Daxing'anling, Heilongjiang Province has been changed, however, the process were different among subsystems. The growth rates of the subsystems of well-being of the people and resource conservation have been high, while the subsystems of enterprise management and the support capability have been lagged dramatically behind. The development stage of coupling coordination of the compound system has changed from misalignment to coordination, nevertheless, the coordinated development level was regressive in recent years due to certain factors such as policy, COVID-19, etc. It is predicted that by 2022, the development stage of coupling coordination of compound system will be recovered to the benign coordinated development type, however, there is still a big gap before it reaches the high-quality coordinated development type. It is suggested that the existing support policies and inputs should be kept stable, moreover, the enterprise management and support capability should be strengthened, in order to promote the stable and high-quality coupling coordinated development in the key state-owned forest regions in Daxing'anling, Heilongjiang Province. © 2022, China Environmental Science Press. All rights reserved.

A Generalized Entropy Approach to Portfolio Selection under a Hidden Markov Model

This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk associated with investment decisions is measured by the exponential Rényi entropy criterion, which summarizes the uncertainty in portfolio returns. Assuming asset returns are projected by a regime-switching regression model on the two market risk factors, we develop an entropy-based dynamic portfolio selection model constrained with the wealth surplus being greater than or equal to the shortfall over a target and the probability of shortfall being less than or equal to a specified level. In the empirical analysis, we use the select sector ETFs to test the asset pricing model and examine the portfolio performance. Weekly financial data from 31 December 1998 to 30 December 2018 is employed for the estimation of the hidden Markov model including the asset return parameters, while the out-of-sample period from 3 January 2019 to 30 April 2022 is used for portfolio performance testing. It is found that, under both the empirical Sharpe and return to entropy ratios, the dynamic portfolio under the proposed strategy is much improved in contrast with mean variance models.

The Predictive Power of Financial Stress on the Financial Markets Dynamics: Hidden Markov Model

This study investigates the predictive power of the financial stress on the dynamic of the Middle East and North Africa (MENA) financial market returns from 2007 to 2021. Based on a Quantile Regression, we show that financial stress has highest predictive abilities at the lower quantiles when the market is bearish. Then, we propose a Hidden Markov Model (HMM) based on the transition matrix to understand the relationship between financial stress index and the MENA stock market dynamics. We find that the effect of financial stress on stock market return reveals the persistence of regimes: Bullish state exists and persists, and has the longest conditional expected duration for the majority of MENA markets, except Bahrain, Qatar and Jordan. However, the transition probability from the bullish to the calm regime is too low for the financial market of Bahrain, United Arab Emirates and Egypt. Besides, the estimated mean returns for each regime divulge that the bearish and calm states are more attractive destination for both portfolio managers and investors. © 2022, Academy of Economics and Finance.

Gamification: A Motivation Metric Based in a Markov Model

The current situation in the world with the COVID-19 pandemic has reinforced a pre-existing trend based on increasing the use of gamification tools in education to motivate students. In this work, a study based on a Markov model is proposed to assess motivation during the training process in higher education. The evolution of Faculty of Business Administration graduates when using a gamified smartphone application (HEgameApp) has been measured. The behavior of graduates is assessed through collaboration in fora created by HegameApp, and the recognition given by their classmates. A utility function is defined to obtain a statistical estimator used in the assignment of motivational states of the study participants. In addition, a decrement function is assigned to the value of the components of the utility function to estimate the time variation of motivation during the process of knowledge assimilation. The proposed solution shows that when graduates are involved in using the app, they significantly increase their academic outcomes and satisfaction while receiving the lectures. In addition, the positive feedback perceived through the application fora has a measurable effect on their motivation. © 2022. International Journal of Emerging Technologies in Learning. All Rights Reserved.

Finite mixture model of hidden Markov regression with covariate dependence

In recent days, a combination of finite mixture model (FMM) and hidden Markov model (HMM) is becoming popular for partitioning heterogeneous temporal data into homogeneous groups (clusters) with homogeneous time points (regimes). The regression mixtures commonly considered in this approach can also accommodate for covariates present in data. The classical fixed covariate approach, however, may not always serve as a reasonable assumption as it is incapable of accounting for the contribution of covariates in cluster formation. This paper introduces a novel approach for detecting clusters and regimes in time series data in the presence of random covariates. The computational challenges related to the proposed model has been discussed, and several simulation studies are performed. An application to United States COVID-19 data yields meaningful clusters and regimes.

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.

Cost-effectiveness analysis of BNT162b2 COVID-19 booster vaccination in the United States.

OBJECTIVES: To evaluate the cost-effectiveness of a booster strategy in the United States. METHODS: We developed a decision-analytic Markov model of COVID-19 to evaluate the cost-effectiveness of a booster strategy of the Pfizer-BioNTech BNT162b2 (administered 6 months after the second dose) among older adults from a healthcare system perspective. RESULTS: Compared with 2 doses of BNT162b2 without a booster, the booster strategy in a 100,000 cohort of older adults would incur an additional cost of $3.4 million in vaccination cost but save $6.7 million in direct medical cost and gain 3.7 quality-adjusted life-years in 180 days. This corresponds to a benefit-cost ratio of 1.95 and a net monetary benefit of $3.4 million. Probabilistic sensitivity analysis indicates that a booster strategy has a high chance (67%) of being cost-effective. Notably, the cost-effectiveness of the booster strategy is highly sensitive to the population incidence of COVID-19, with a cost-effectiveness threshold of 8.1/100,000 person-day. If vaccine efficacies reduce by 10%, 30%, and 50%, this threshold will increase to 9.7/100,000, 13.9/100,000, and 21.9/100,000 person-day, respectively. CONCLUSION: Offering the BNT162b2 booster to older adults aged ≥65 years in the United States is likely to be cost-effective. Less efficacious vaccines and boosters may still be cost-effective in settings of high SARS-CoV-2 transmission.

A dynamic case-based reasoning system for responding to infectious disease outbreaks

Infectious diseases are a global public health problem, which requires timely and effective responses. This study proposes a novel model that contributes to the development of such responses. First, the problem scenario features of infectious disease emergency scenarios are extracted, and the problem scenario is structurally described. A Markov model is adopted to analyze the scenario evolution of the infectious disease outbreaks. Then, a dynamic case-based reasoning model is built. Different matching algorithms are designed for crisp symbols, crisp numbers, interval numbers, and fuzzy linguistic variables. The similarity between the target scenario and various historical scenarios is calculated. Finally, an optimized dynamic emergency decision guide is provided. An experiment is conducted to test the validity and feasibility of the proposed method. The results suggest that the model can realistically simulate the process of infectious disease outbreaks and quickly match the recorded scenarios to generate effective and real-time responses.

A Blockchain Secured Pharmaceutical Distribution System to Fight Counterfeiting.

Counterfeiting drugs has been a global concern for years. Considering the lack of transparency within the current pharmaceutical distribution system, research has shown that blockchain technology is a promising solution for an improved supply chain system. This study aims to explore the current solution proposals for distribution systems using blockchain technology. Based on a literature review on currently proposed solutions, it is identified that the secrecy of the data within the system and nodes' reputation in decision making has not been considered. The proposed prototype uses a zero-knowledge proof protocol to ensure the integrity of the distributed data. It uses the Markov model to track each node's 'reputation score' based on their interactions to predict the reliability of the nodes in consensus decision making. Analysis of the prototype demonstrates a reliable method in decision making, which concludes with overall improvements in the system's confidentiality, integrity, and availability. The result indicates that the decision protocol must be significantly considered in a reliable distribution system. It is recommended that the pharmaceutical distribution systems adopt a relevant protocol to design their blockchain solution. Continuous research is required further to increase performance and reliability within blockchain distribution systems.

Cost-Utility Analysis of the Integrated Care Models for the Management of Hypertension Patients: A Quasi-Experiment in Southwest Rural China.

Background: Hypertension has become the second-leading risk factor for death worldwide. However, the fragmented three-level "county-township-village" medical and healthcare system in rural China cannot provide continuous, coordinated, and comprehensive health care for patients with hypertension, as a result of which rural China has a low rate of hypertension control. This study aimed to explore the costs and benefits of an integrated care model using three intervention modes-multidisciplinary teams (MDT), multi-institutional pathway (MIP), and system global budget and performance-based payments (SGB-P4P)-for hypertension management in rural China. Methods: A Markov model with 1-year per cycle was adopted to simulate the lifetime medical costs and quality-adjusted life-years (QALYs) for patients. The interventions included Option 1 (MDT + MIP), Option 2 (MDT + MIP + SGB-P4P), and the Usual practice (usual care). We used the incremental cost-effectiveness ratio (ICER), net monetary benefit (NMB), and net health benefit (NHB) to make economic decisions and a 5% discount rate. One-way and probability sensitivity analyses were performed to test model robustness. Data on the blood pressure control rate, transition probability, utility, annual treatment costs, and project costs were from the community intervention trial (CMB-OC) project. Results: Compared with the Usual practice, Option 1 yielded an additional 0.068 QALYs and an additional cost of $229.99, resulting in an ICER of $3,373.75/QALY, the NMB was -$120.97, and the NHB was -0.076 QALYs. Compared with the Usual practice, Option 2 yielded an additional 0.545 QALYs, and the cost decreased by $2,007.31, yielding an ICER of -$3,680.72/QALY. The NMB was $2,879.42, and the NHB was 1.801 QALYs. Compared with Option 1, Option 2 yielded an additional 0.477 QALYs, and the cost decreased by $2,237.30, so the ICER was -$4,688.50/QALY, the NMB was $3,000.40, and the NHB was 1.876 QALYs. The one-way sensitivity analysis showed that the most sensitive factors in the model were treatment cost of ESRD, human cost, and discount rate. The probability sensitivity analysis showed that when willingness to pay was $1,599.16/QALY, the cost-effectiveness probability of Option 1, Option 2, and the Usual practice was 0.008, 0.813, and 0.179, respectively. Conclusions: The integrated care model with performance-based prepaid payments was the most beneficial intervention, whereas the general integrated care model (MDT + MIP) was not cost-effective. The integrated care model (MDT + MIP + SGB-P4P) was suggested for use in the community management of hypertension in rural China as a continuous, patient-centered care system to improve the efficiency of hypertension management.