Estimation Of Transition Probabilities Research Articles

Competing treatments for migraine: a headache for decision-makers

BackgroundMigraine is the world’s second most common disabling disorder, affecting 15% of UK adults and costing the UK over £1.5 billion per year. Several costly new drugs have been approved by National Institute for Health and Care Excellence.AimTo assess the cost-effectiveness of drugs used to treat adults with chronic migraine.MethodsWe did a systematic review of placebo-controlled trials of preventive drugs for chronic migraine. We then assessed the cost-effectiveness of the currently prescribable drugs included in the review: Onabotulinum toxin A (BTA), Eptinezumab (100mg or 300mg), Fremanezumab (monthly or quarterly dose), Galcanezumab or Topiramate, each compared to placebo, and we evaluated them jointly. We developed a Markov (state-transition) model with a three-month cycle length to estimate the costs and quality-adjusted life years (QALYs) for the different medications from a UK NHS and Personal Social Services perspective. We used a two-year time horizon with a starting age of 30 years for the patient cohort. We estimated transition probabilities based on monthly headache days using a network meta-analysis (NMA) developed by us, and from published literature. We obtained costs from published sources and applied discount rates of 3.5% to both costs and outcomes.ResultsDeterministic results suggest Topiramate was the least costly option and generated slightly more QALYs than the placebo, whereas Eptinezumab 300mg was the more costly option and generated the most QALYs. After excluding dominated options, the incremental cost-effectiveness ratio (ICER) between BTA and Topiramate was £68,000 per QALY gained and the ICER between Eptinezumab 300mg and BTA was not within plausible cost-effectiveness thresholds. The cost-effectiveness acceptability frontier showed that Topiramate is the most cost-effective medication for any amount the decision maker is willing-to-pay per QALY.ConclusionsAmong the various prophylactic medications for managing chronic migraine, only Topiramate was within typical cost-effectiveness threshold ranges. Further research is needed, ideally an economic evaluation alongside a randomised trial, to compare these newer, expensive CGRP MAbs with the cheaper oral medications.

Cost-Effectiveness Analysis for Therapy Sequence in Advanced Cancer: A Microsimulation Approach with Application to Metastatic Prostate Cancer.

Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. We developed a general microsimulation framework to study therapy sequence and applied it to metastatic prostate cancer. We constructed a discrete-time state transition model to study 2 lines of therapy. Using digitized published survival curves (progression-free survival, time to progression, and overall survival [OS]), we inferred event types (progression or death) and estimated transition probabilities using cumulative incidence functions with competing risks. We incorporated within-patient dependence over time; first-line therapy response informed subsequent event probabilities. Parameters governing within-patient dependence calibrated the model-based results to a target clinical trial. We applied these methods to 2 therapy sequences for metastatic prostate cancer, wherein both docetaxel (DCT) and abiraterone acetate (AA) are appropriate for either first- or second-line treatment. We assessed costs and quality-adjusted life-years (5-y QALYs) for 2 treatment strategies: DCT → AA versus AA → DCT. Models assuming within-patient independence overestimated OS time, which corrected with the calibration approach. With generic pricing, AA → DCT dominated DCT → AA, (higher 5-y QALYs and lower costs), consistent for all values of calibration parameters (including no correction). Model calibration increased the difference in 5-y QALYs between treatment strategies (0.07 uncorrected v. 0.15 with base-case correction). Applying the correction decreased the estimated difference in cost (-$5,360 uncorrected v. -$3,066 corrected). Results were strongly affected by the cost of AA. Under a lifetime horizon, AA → DCT was no longer dominant but still cost-effective (incremental cost-effectiveness ratio: $19,463). We demonstrate a microsimulation approach to study the cost-effectiveness of therapy sequences for advanced prostate cancer, taking care to account for within-patient dependence. We developed a discrete-time state transition model for studying therapy sequence in advanced cancers.Results are sensitive to dependence within patients.A calibration approach can introduce dependence across lines of therapy and closely match simulation outcomes to target trial outcomes.

Improving Prediction of Tobacco Use Over Time: Findings from Waves 1-4 of the Population Assessment of Tobacco and Health Study.

First-order Markov models assume future tobacco use behavior is dependent on current tobacco use and are often used to characterize patterns of tobacco use over time. Higher-order Markov models that assume future behavior is dependent on current and prior tobacco use may better estimate patterns of tobacco use. This study compared Markov models of different orders to examine whether incorporating information about tobacco use history improves model estimation of tobacco use and estimated tobacco use transition probabilities. We used data from four waves of the Population Assessment of Tobacco and Health Study. In each Wave, a participant was categorized into one of the following tobacco use states: never smoker, former smoker, menthol cigarette smoker, non-menthol cigarette smoker, or e-cigarette/dual user. We compared first-, second-, and third-order Markov models using multinomial logistic regression and estimated transition probabilities between tobacco use states. The third-order model was the best fit for the data. The percentage of former smokers, menthol cigarette smokers, non-menthol cigarette smokers, and e-cigarette/dual users in Wave 3 that remained in the same tobacco use state in Wave 4 ranged from 63.4% to 97.2%, 29.2% to 89.8%, 34.8% to 89.7%, and 20.5% to 80.0%, respectively, dependent on tobacco use history. Individuals who were current tobacco users, but former smokers in the prior two years, were most likely to quit. Transition probabilities between tobacco use states varied widely depending on tobacco use history. Higher-order Markov models improve estimation of tobacco use over time and can inform understanding of trajectories of tobacco use behavior. Findings from this study suggest that transition probabilities between tobacco use states vary widely depending on tobacco use history. Tobacco product users (cigarette or e-cigarette/dual users) who were in the same tobacco use state in the prior two years were least likely to quit. Individuals who were current tobacco users, but former smokers in the prior two years, were most likely to quit. Quitting smoking for at least two years is an important milestone in the process of cessation.

Economic evaluation of acupuncture as an adjunctive treatment with usual care for mild-to-moderate knee osteoarthritis: A Markov model-based analysis

BackgroundCollaborative care (CC), in which acupuncture is combined with usual care (UC), improves clinical outcomes and increases costs in knee osteoarthritis (KOA). We evaluated the economic feasibility of CC for Korean female patients with mild-to-moderate KOA by using a cost-effectiveness approach. MethodsTwo alternatives for KOA (1. UC as a comparator; and 2. CC as an intervention) were defined based on clinical guidelines, official Korean statistics, and expert validation. Each alternative was simulated in a Markov model every 6 months for 10 years. Estimates of costs, utilities, and transition probabilities were obtained from official statistics and previous studies. The effectiveness of CC was synthesized from randomized controlled trials. A base-case analysis of a limited societal perspective, univariate sensitivity analysis, and probability sensitivity analysis were performed. An annual discount rate of 4.5% and threshold of 20,000 United States dollar per Quality-adjusted life year (USD/QALY) were applied. ResultsEvery incremental cost-effectiveness ratio (ICER) of CC calculated from the analyses was sub-threshold. In the base-case analysis, with a limited societal perspective, the ICER was 11,085 USD/QALY. The ICERs from the univariate sensitivity analyses were -2,577–16,748 USD/QALY. The average ICER in the probability sensitivity analysis was 12,412 USD/QALY. When the threshold surpassed 8,000 USD/QALY, the cost-effectiveness of CC exceeded 50%. The probability was 70.27% when the threshold was 20,000 USD/QALY. ConclusionsCC for Korean female patients with mild-to-moderate KOA was generally cost-effective. Considering the limitations of the evidence, we propose a re-evaluation using further clinical studies in the future.

Adaptive Residual Useful Life Prediction for the Insulated-Gate Bipolar Transistors with Pulse-Width Modulation Based on Multiple Modes and Transfer Learning

Currently, residual useful life (RUL) prediction models for insulated-gate bipolar transistors (IGBT) do not focus on the multi-modal characteristics caused by the pulse-width modulation (PWM). To fill this gap, the Markovian stochastic process is proposed to model the mode transition process, due to the memoryless properties of the grid operation. For the estimation of the mode transition probabilities, transfer learning is utilized between different control signals. With the continuous mode switching, fractional Weibull motion (fWm) of multiple modes is established to model the stochasticity of the multi-modal IGBT degradation. The drift and diffusion coefficients are adaptively updated in the proposed RUL prediction model. In the case study, two sets of the real thermal-accelerated IGBT aging data are used. Different degradation modes are extracted from the meta degradation data, and then fused to be a complex health indicator (CHI) via a multi-sensor fusion algorithm. The RUL prediction model based on the fWm of multiple modes can reach a maximum relative prediction error of 2.96% and a mean relative prediction error of 1.78%. The proposed RUL prediction model with better accuracy can reduce the losses of the power grid caused by the unexpected IGBT failures.

De Novo Cost-Effectiveness Model Framework for Nonalcoholic Steatohepatitis-Modeling Approach and Validation.

Nonalcoholic steatohepatitis (NASH) is a chronic liver disease associated with hepatic morbidity and mortality and extra-hepatic comorbidities. Published NASH cost-effectiveness models (CEMs) are heterogeneous and consistently omit comorbid conditions that frequently co-exist alongside NASH. We aimed to develop a de novo CEM framework that incorporates extra-hepatic disease states and outcomes alongside hepatic components to enable future estimation of the cost-effectiveness of NASH interventions. Patient-level simulation and cohort-level Markov models were implemented in the same framework. Model inputs included fibrosis progression; late-stage liver disease outcomes; comorbidity outcomes for cardiovascular disease, type 2 diabetes, and obesity; mortality; health-related quality of life; and direct medical costs. The prototype analysis assessed the cost-effectiveness of obeticholic acid versus standard of care from a US payer perspective over a lifetime horizon with costs and effects discounted at 3% per annum. However, the CEM was designed for easy adaptation to other countries, time horizons, and other considerations. Efficacy and adverse event parameters were obtained from the 18-month interim analysis of the REGENERATE trial. Outputs include total and incremental costs, total life years, and quality-adjusted life years. In this model, total costs, total life years, and quality-adjusted life years were all higher with obeticholic acid compared with standard of care. Cross-validation of this model with the 2016 and 2020 Institute for Clinical and Economic Review models revealed marked differences, mainly driven by mortality inputs, transition probability estimates, and incorporation of the effect of treatment and comorbidities. This is the first CEM in NASH to incorporate the clinical consequences of several comorbidities. The flexible yet standardized framework permits estimation of the cost-effectiveness of NASH interventions in a variety of settings. The model currently includes several assumptions and will be further developed as more relevant data become available.

Characterization of cell-fate decision landscapes by estimating transcription factor dynamics

Time-specific modulation of gene expression during differentiation by transcription factors promotes cell diversity. However, estimating their dynamic regulatory activity at the single-cell level and in a high-throughput manner remains challenging. We present FateCompass, an integrative approach that utilizes single-cell transcriptomics data to identify lineage-specific transcription factors throughout differentiation. By combining a probabilistic framework with RNA velocities or differentiation potential, we estimate transition probabilities, while a linear model of gene regulation is employed to compute transcription factor activities. Considering dynamic changes and correlations of expression and activities, FateCompass identifies lineage-specific regulators. Our validation using in silico data and application to pancreatic endocrine cell differentiation datasets highlight both known and potentially novel lineage-specific regulators. Notably, we uncovered undescribed transcription factors of an enterochromaffin-like population during invitro differentiation toward ß-like cells. FateCompass provides a valuable framework for hypothesis generation, advancing our understanding of the gene regulatory networks driving cell-fate decisions.

EE30 Annual Disability-State Transition Probabilities Estimated from a Large Sample (>6000) of Australians with Multiple Sclerosis from MS-Base

Multiple sclerosis (MS) is a chronic autoimmune/neurodegenerative disease associated with progressing disability mostly diagnosed between 20-40 years of age in females (approximately 75%). We aimed to estimate transition probabilities from no disability through to severe disability and death. Transition probabilities are a vital input for health economics models that are used to inform healthcare resourcing decisions including for MS-specific disease-modifying therapies (DMT).

Estimating Transition Probabilities Across the Alzheimer's Disease Continuum Using a Nationally Representative Real-World Database in the United States.

Clinical Alzheimer's disease (AD) begins with mild cognitive impairment (MCI) and progresses to mild, moderate, or severe dementia, constituting a disease continuum that eventually leads to death. This study aimed to estimate the probabilities of transitions across those disease states. We developed a mixed-effects multi-state Markov model to estimate the transition probabilities, adjusted for 5 baseline covariates, using the Health and Retirement Study (HRS) database. HRS surveys older adults in the United States bi-annually. Alzheimer states were defined using the modified Telephone Interview of Cognitive Status (TICS-m). A total of 11,292 AD patients were analyzed. Patients were 70.8±9.0years old, 54.9% female, and with 12.0±3.3years of education. Within 1year from the initial state, the model estimated a higher probability of transition to the next AD state in earlier disease: 12.8% from MCI to mild AD and 5.0% from mild to moderate AD, but<1% from moderate to severe AD. After 10years, the probability of transition to the next state was markedly higher for all states, but still higher in earlier disease: 29.8% from MCI to mild AD, 23.5% from mild to moderate AD, and 5.7% from moderate to severe AD. Across all AD states, the probability of transition to death was<5% after 1year and>15% after 10years. Older age, fewer years of education, unemployment, and nursing home stay were associated with a higher risk of disease progression (p<0.01). This analysis shows that the risk of progression is greater in earlier AD states, increases over time, and is higher in patients who are older, with fewer years of education, unemployed, or in a nursing home at baseline. The estimated transition probabilities can provide guidance for future disease management and clinical trial design optimization, and can be used to refine existing cost-effectiveness frameworks.

Evaluation of transitions from early hypertension to hypertensive chronic kidney disease, coronary artery disease, stroke and mortality: a Thai real-world data cohort.

Systemic arterial hypertension (HT) is a major modifiable risk factor for cardiovascular disease (CVDs), associated with all-cause death (ACD). Understanding its progression from the early state to late complications should lead to more timely intensification of treatment. This study aimed to construct a real-world cohort profile of HT and to estimate transition probabilities from the uncomplicated state to any of these long-term complications; chronic kidney disease (CKD), coronary artery disease (CAD), stroke, and ACD. This real-world cohort study used routine clinical practice data for all adult patients diagnosed with HT in the Ramathibodi Hospital, Thailand from 2010 to 2022. A multi-state model was developed based on the following: state 1-uncomplicated HT, 2-CKD, 3-CAD, 4-stroke, and 5-ACD. Transition probabilities were estimated using Kaplan-Meier method. A total of 144,149 patients were initially classified as having uncomplicated HT. The transition probabilities (95% CI) from the initial state to CKD, CAD, stroke, and ACD at 10-years were 19.6% (19.3%, 20.0%), 18.2% (17.9%, 18.6%), 7.4% (7.1%, 7.6%), and 1.7% (1.5%, 1.8%), respectively. Once in the intermediate-states of CKD, CAD, and stroke, 10-year transition probabilities to death were 7.5% (6.8%, 8.4%), 9.0% (8.2%, 9.9%), and 10.8% (9.3%, 12.5%). In this 13-year cohort, CKD was observed as the most common complication, followed by CAD and stroke. Among these, stroke carried the highest risk of ACD, followed by CAD and CKD. These findings provide improved understanding of disease progression to guide appropriate prevention measures. Further investigations of prognostic factors and treatment effectiveness are warranted.

Adjustment of initial estimates of voter transition probabilities to guarantee consistency and completeness

The estimation of voter shifts (stayers and switchers) between elections is an active area of research that, for decades, has attracted the interest of many scholars. The voter transitions are typically summarised in a row-standardised proportion (probability) matrix. This matrix is usually unknown, despite it being of interest to many agents, including party teams, the media and political scientists. When surveys are used to approximate this matrix, it is not uncommon for the estimated matrix to be inconsistent and even incomplete. The iterative proportional fitting algorithm solves inconsistency but cannot fix incompleteness. Hierarchical Bayesian models that combine aggregate and survey estimates can solve both problems, but are extremely complex and data-demanding. This paper details all the scenarios concerning the available information that can be reasonably considered and, within the linear programming framework, develops specific models to reach consistency and completeness. The models are, moreover, quite flexible as they allow analysts to have missing values and to introduce through weights their relative confidences in the different a priori transition proportions. The usefulness of the proposed models is illustrated with real data. Interested readers can easily use these new models with their data as they have been programmed in the function lp_apriori of the R-package lphom.

Detecting business cycles for Hungarian leading and coincident indicators with a Markov switching dynamic model to improve sustainability in economic growth

This paper applies the hidden Markov switching dynamic regression (MSDR) model to estimate transition probabilities of the Hungarian GDP between recessionary and expansionary periods. The transition probabilities are then compared to the OECD Hungarian binary business cycle indicator to assess the predictive power of the model. The paper proposes a linear model with a mean and a homoscedastic component. The level of symmetricity between the GDP and business cycles is explained by the panel data variables (Unemployment rate, IPI index, Inflation, BUX year-on-year change, and 10-3 Year sovereign bond yield spreads). It is assumed in this paper that by extending the model to encompass an exogenous variable listed in the panel data, essentially making the model bivariate, the maximum likelihood function would capture the business cycle more accurately. The results show that by plugging the unemployment rate as the exogenous variable in the regression, our model’s accuracy is 70%.

Ongoing High Prevalence of Severe Immune Suppression Among Children in South Africa.

Among children in Southern Africa severe immune suppression (SIS) has declined, but most continue to initiate antiretroviral therapy (ART) with SIS. Using data from South Africa, we describe SIS at ART start and on ART between 2007 and 2020, among children <5 years with a CD4%/cell count at ART start and ≥1 subsequent measure. Gap in care was defined as >9 months without a recorded visit. We defined SIS according to age and CD4%/cell count. A multistate model was used to estimate transition probabilities between 5 states: SIS on ART; Stable, not SIS; Early Gap, commencing <9 months from ART start; Late Gap, commencing ≥9 months on ART; and Death. Among 2536 children, 70% had SIS at ART start, and 36% experienced SIS on ART. An increasing proportion were age <1 year at ART initiation (2007-2009: 43% to 2013-2020: 55%). Increasingly, SIS on ART occurred after a gap, in those with SIS on ART for >1 year, and after a period of unknown immune status. Later year of ART initiation was associated with reduced transition from SIS on ART to Stable. Infants and those initiating ART with SIS were more likely to transition from Stable to SIS. Viremia strongly predicted death from both the on ART states. Increasingly SIS occurred among ART-experienced children. Those starting ART with SIS and during infancy remained especially vulnerable to SIS once on treatment. Managing ART in these children may be more complex and further reducing AIDS-related mortality is likely to remain challenging.

An approach towards the implementation of a reliable resilience model based on machine learning

Machine Learning tools to enhance systems’ resilience received an increased impetus driven by energy transition, climate change and digitalization, but critical challenges on system requirement definition and reliability of learning processes need to be addressed. This study proposes a systematic framework based on system engineering and focused on the reliability of the learning process of the Hidden Markov Model (HMM) coupled with the Baum-Welsh algorithm. The HMM hidden states may represent the precursors of accidental events, being the states between a regular performance and a failure of a sub-system. The Baum-Welch algorithm, estimating the parameters of the HMM, iteratively updates the estimates of the state transition and observation probabilities. The framework was applied to a real case of LNG bunkering, showing that the system can learn from incomplete data, improve the learning quality given a new set of observations, make predictions about the latent states and enhance system resilience. The novelty of this work lies in ensuring the learning process and contributing to the attainment of an explainable, robust, and interpretable data-driven approach.

Cost-effectiveness of immune checkpoint inhibition and targeted treatment in combination as adjuvant treatment of patient with BRAF-mutant advanced melanoma

BackgroundImmune checkpoint inhibitors (ICIs) and targeted treatments have improved the health outcomes of patients with advanced melanoma. However, due to the high cost of novel therapies, it is crucial to evaluate their value by considering both effectiveness and cost. To compare the cost-effectiveness of these novel agents (atezolizumab-vemurafenib-cobimetinib, vemurafenib-plus-cobimetinib, dabrafenib-plus-trametinib, and encorafenib-plus-binimetinib) for first-line treatment of metastatic melanoma with the BRAFV600 mutation.MethodsA patient-level model was developed to project the health outcomes of 4 strategies for patients with advanced melanoma. We estimated transition probabilities from the IMspire150 (ClinicalTrials.gov, NCT02908672), COMBI-AD (NCT01682083), and COLUMBUS (NCT01909453) trials using a parametric survival model. All health outcomes, including direct cost, quality-adjusted life-years (QALYs) and the incremental cost-effectiveness ratio (ICER), were estimated from the US payer perspective. Lifetime cost, QALYs, life-years (LYs), and ICERs were calculated. Univariable and probabilistic sensitivity analyses were performed to test model robustness, along with multiple scenario analyses.ResultsOf the 4 competing strategies, atezolizumab-vemurafenib-cobimetinib produced the best health outcomes, and the vemurafenib-cobimetinib strategy was the least expensive option. Atezolizumab-vemurafenib-cobimetinib, dabrafenib-plus-trametinib, and vemurafenib-cobimetinib formed the cost-effective frontier, indicating that the ordered ICERs were $325,113/QALYs for dabrafenib-plus-trametinib vs. vemurafenib-cobimetinib strategies and $2,247,500/QALYs for atezolizumab-vemurafenib-cobimetinib vs. dabrafenib-plus-trametinib strategies. Encorafenib-plus-binimetinib was dominated by the other 3 competing strategies. The drug price and first-line utility significantly influenced the model utcomes.ConclusionsFor BRAF-mutant advanced melanoma, the vemurafenib-cobimetinib strategy could be considered the most cost-effective treatment at the willingness-to-pay threshold of $150,000.

Likelihood and quasi - likelihood estimation of transition probabilities

Likelihood and quasi - likelihood estimation of transition probabilities

Multi-objective learning and explanation for stroke risk assessment in Shanxi province

Stroke is the leading cause of death in China (Zhou et al. in The Lancet, 2019). A dataset from Shanxi Province is analyzed to predict the risk of patients at four states (low/medium/high/attack) and to estimate transition probabilities between various states via a SHAP DeepExplainer. To handle the issues related to an imbalanced sample set, the quadratic interactive deep model (QIDeep) was first proposed by flexible selection and appending of quadratic interactive features. The experimental results showed that the QIDeep model with 3 interactive features achieved the state-of-the-art accuracy 83.33%(95% CI (83.14%; 83.52%)). Blood pressure, physical inactivity, smoking, weight, and total cholesterol are the top five most important features. For the sake of high recall in the attack state, stroke occurrence prediction is considered an auxiliary objective in multi-objective learning. The prediction accuracy was improved, while the recall of the attack state was increased by 17.79% (to 82.06%) compared to QIDeep (from 71.49%) with the same features. The prediction model and analysis tool in this paper provided not only a prediction method but also an attribution explanation of the risk states and transition direction of each patient, a valuable tool for doctors to analyze and diagnose the disease.

488. RADIOFREQUENCY ABLATION OF LOW-GRADE DYSPLASIA IS COST-EFFECTIVE FOR PATIENTS WITH LONG-SEGMENT BARRETT'S OESOPHAGUS

Abstract Barrett’s oesophagus is a precursor to oesophageal adenocarcinoma (OAC), which can be lethal if found at late stages. Stages of Barrett's oesophagus are generally divided into non-dysplasia or dysplasia. There is little doubt that patients with high-grade dysplasia require endoscopic intervention, usually with ablative techniques. Endoscopic treatment of low-grade dysplasia (LGD) has been more controversial, however. Our main aim was to improve cost-effectiveness of LGD ablation using length of Barrett's oesophagus to select high-risk individuals. A cost-utility analysis was undertaken using a Markov cohort model, which simulated progression and regression of non-dysplastic Barrett’s oesophagus (NDBE) to EAC (cycle length of 6 months, time horizon of 35 years). Health states included: NDBE, regression of metaplasia, low-grade dysplasia (LGD), high-grade dysplasia (HGD), EAC, and death. Cost pertaining to investigations and treatments were sourced from local health network. Quality-adjusted life-year (QALY) data were taken from literature values. Initial estimates of transition probabilities between health states were derived from literature and calibrated to lifetime risk of HGD and OAC using standardised methodology. One-way and probabilistic sensitivity analysis were conducted. Several strategies (39 total) were tested in this cost-utility analysis and were broadly divided into selective (length-based) or non-selective LGD ablation. In addition to LGD ablation, surveillance intervals for NDBE were altered to improve cost-effectiveness. In non-selective group of strategies, only 1 was found to be cost-effective, whereas 24 of 28 length-based LGD ablation strategies were found to be cost-effective (at a willingness to pay threshold of AU$50,000/QALY). Probabilistic analysis (1000 simulations) showed that exclusion of individuals with short segment Barrett's oesophagus (SSBE) from surveillance with ablation of LGD in long-segment Barrett's oesophagus (LSBE) was the most cost-effective strategy (AU$33,031/QALY). Patients with long-segment Barrett's oesophagus are at significantly higher risk of progression to adenocarcinoma. Results of this study indicate reducing endoscopic surveillance for SSBE with routine surveillance for LSBE with ablation of LGD when detected is cost-effective in 100% of the simulations. Several studies in recent years have shown similar results. Thus ablation of LGD in Barrett's oesophagus requires serious consideration, especially for high-risk individuals.

Analysis of State Transition of COVID-19 Positive Cases in Tokyo, Japan and its Application to Agent Simulation

This paper estimates the state transition of COVID-19 positive cases by analyzing the data about confirmed positive cases in Tokyo, Japan. The prediction of the number of newly infected persons is one of the active research topics for the COVID-19 pandemic. Although such a prediction is important for recognizing the future risk of spreading infectious diseases, understanding the state transition after they are confirmed to be positive is also important for estimating the number of required ICUs, hotel rooms for isolation, etc. This paper classifies the state after being positive into “in hotel/home for isolation,” “in hospital with a mild state,” “in hospital with a severe state,” “recovered,” and “dead” and estimates the transition probabilities among those states from the data about confirmed positive cases in Tokyo, Japan. This paper shows the parameters estimated from different periods and discusses the difference considering the pandemic situation. An agent simulation using the estimated transition probabilities as its parameters is also proposed. The result of the simulation from August to November 2020 shows the predicted number of agents is close to the actual data. As one of the possible applications to the proposed agent simulation, this paper shows the simulation result from December 2020 to January 2021 under a hypothetical situation.

Stochastic approach to Markovian interrelationship assessment of solar activity indices

This paper employs a discrete-time Markov chain (DTMC) stochastic process to investigate a state/event based Markovian interrelationship between various solar activity indices (SAI) (including 10.7 cm solar radio flux (SF10.7), coronal index (CI), solar flare index (SFI) and total solar irradiance (TSI)) in relation to sunspot number (SSN). First, we applied the first order DTMC model as a first approximation to the total number of transitions between different states of SAI in order to estimate the probability of occurrence corresponding with each transition. Next, several DTMC descriptors like persistency, state dependency, stationarity, mean first passage time and entropy are derived from estimated transition probability matrices. These descriptors are very useful as they related to time series characteristics (like randomness, nature of cycles and predictability) within a stochastic dynamical system as well as crucial for checking the applicability of Markov chain method. Therefore, via the DTMC analysis and derived descriptors, this study found remarkable similarities in the formation of transition matrices and diagrams, significant 2-dimensional correlation values, robust self-communication behaviour among states, existence of dependent successive transitions and stationary nature of data throughout the space. Further, the resemblance in the average transit time from one state to another, probabilistically disordered symmetrical time series and existence of randomness in transition states has been observed. Therefore, results obtained in this paper provide a new insight to increase the level of knowledge of the possible linkage between underlying SAI that could be helpful in enhanced understanding of the potential future climate changes and other solar energy-related objectives.