Dynamic Markov Research Articles

Cost-effectiveness analysis of trifluridine/tipiracil combined with bevacizumab vs. monotherapy for third-line treatment of colorectal cancer

BackgroundThe combination of trifluridine/tipiracil (FTD/TPI) and bevacizumab has demonstrated promising efficacy and safety in the treatment of colorectal cancer (CRC). This study aims to evaluate the cost-effectiveness of trifluridine/tipiracil combined with bevacizumab vs. trifluridine/tipiracil monotherapy as a third-line treatment regimen for colorectal cancer within the Chinese healthcare system, providing an economic basis for clinical application.MethodsBased on data from the SUNLIGHT Phase III clinical trial, a dynamic Markov model was constructed with a cycle length of 4 weeks and a simulation duration of 10 years. Direct medical costs and quality-adjusted life years (QALYs) were calculated. The incremental cost-effectiveness ratio (ICER) was compared with the willingness-to-pay threshold (WTP = ¥268,200.00/QALY) to assess the economic viability of the treatment regimen. One-way sensitivity analysis and probabilistic sensitivity analysis were conducted to verify the robustness of the model results.ResultsThe cost of trifluridine/tipiracil combined with bevacizumab treatment (¥838,492.74) was higher than that of trifluridine/tipiracil monotherapy (¥357,396.97), with greater health benefits (2.45 QALYs vs. 1.54 QALYs). The ICER was ¥527,577.36/QALY, exceeding the willingness-to-pay threshold. One-way sensitivity analysis indicated that drug costs and utility values during the progression-free period significantly impacted model outputs. Probabilistic sensitivity analysis further confirmed the robustness of the results, showing that at a willingness-to-pay threshold of ¥494,000.00, the probability of the combined treatment being cost-effective was 50%.ConclusionTrifluridine/tipiracil combined with bevacizumab, as a third-line treatment for colorectal cancer, does not have a cost-effectiveness advantage compared to trifluridine/tipiracil monotherapy in economic evaluations.

Budget Impact Analysis of Implementing Antenatal Care Recommendations for Positive Pregnancy Outcomes at Public Primary Facilities in Tanzania.

Tanzania recently changed its antenatal care (ANC) guidelines to reduce perinatal mortality and improve the experience of pregnancy care. The new guideline recommends increasing the number of ANC visits from four to eight and introducing one routine ultrasound scan, among other recommendations. We estimated the budget impact of implementing the new guideline compared to the previous focused ANC guideline at public dispensaries and health centers. In a dynamic Markov model, we prospectively followed annual cohorts of between 2.3 and 2.6 million pregnant women who will be attending ANC at dispensaries and health centers for 5years. We allowed a population of pregnant women into the model every year and women exit the model at delivery. We calculated the cost of medicines, medical supplies, and laboratory supplies required to produce services from a public health system perspective. Our model neither estimated condition-related costs nor health effects. The budget impact was calculated as the difference in the estimated costs between the two guidelines. We conducted scenario analyses to explore attending more visits and different assumptions to calculate the target population. We estimated that implementing the new ANC guideline would have a cumulative budget impact of around US$154 million over 5years. The budget required will increase from US$137 million under the focused ANC guideline to US$291 million under the new guideline. Laboratory supplies will consume 47% of the estimated budget under the new guideline. We expect the annual budget impact to be US$38 million in the first year of implementation and US$32 million in the fifth year. We assumed that by the fifth year, 82% of all pregnant women would have had four or more visits. The budget impact would increase to US$214 million, with the proportion of pregnant women attending four or more ANC visits reaching 90% within 5years. Scaling up the implementation of the new ANC guideline at public dispensaries and health centers may substantially increase the supplies required to produce ANC services, particularly laboratory supplies. Studies on the health impact of the new guideline are warranted to estimate the value for money.

Markov Genealogy Processes

The COVID-19 pandemic has underscored the need for efficient and mathematically rigorous methods to analyze epidemiological time series and genetic data. This project introduces and evaluates various methods for estimating infectious disease parameters. We employed a dynamic Markov chain model incorporating random variables tracking infected and susceptible individuals, as well as their coalescent times. Our objective was to assess the accuracy of different techniques in predicting disease characteristics based on this model. Simulation studies of various existing parameter estimation methods revealed that prediction accuracy falls drastically when a recovery rate is introduced. In response to the limitations of existing methods, we initiated the development of a novel model which would incorporate lineages, allowing us to use phylogenetic trees to estimate parameters. This would lead to an improvement in parameter estimates, especially with a recovery rate, as phylogenetic trees contain more information than the types of data used in existing parameter estimation methods.

Reinforced steering Evolutionary Markov Chain for high-dimensional feature selection

The increasing accessibility of extensive datasets has amplified the importance of extracting insights from high-dimensional data. However, the task of selecting relevant features in these high-dimensional spaces is made more difficult due to the curse of dimensionality. Although Evolutionary Algorithms (EAs) have shown promise in the literature for feature selection, creating EAs for high dimensions is still challenging. To address the problem of feature selection in high dimensions, a novel concept of Evolutionary Reinforced Markov Chain is proposed in this paper. The proposed work has the following contributions and merits: (i) The paradigms of evolutionary computation, reinforcement learning, and Markov chain are incorporated into an integrational framework for feature selection in high dimensional spaces in a recursive manner. (ii) To support the global convergence of the algorithm and manage its computational complexity, a restricted group of the most effective agents is maintained within the evolutionary population. (iii) The dynamic Markov chain process efficiently manages agent evolution and communication, ensuring effective navigation through the search space. (iv) Agents moving in the right way are rewarded with an increase in their associated transition probability, while the agents going in the wrong direction are discouraged with a decrease in their associated transition probabilities; this promotes the establishment of an equilibrium state and leads to convergence. (v) The effective size of successful agents is reduced recursively while progressing through different states to further facilitate the speed of convergence and decrease the number of features. (vi) The performance comparison with state-of-the-art feature selection methods shows a significant improvement and promise of the proposed method over the existing methods.

Intelligent Bayesian regularization backpropagation neuro computing paradigm for state features estimation of underwater passive object

In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach for instantaneous state features reckoning of passive marine object following dynamic Markov chains. This paper introduces the potential of intelligent Bayesian regularization backpropagation neuro computing (IBRBNC) for the precise estimation of state features of underwater passive object. The proposed paradigm combines the power of artificial neural network with Bayesian regularization technique to address the challenges associated with noisy and limited underwater sensor data. The IBRBNC paradigm leverages deep neural networks with a focus on backpropagation to model complex relationships in the underwater environment. Furthermore, Bayesian regularization is introduced to incorporate prior knowledge and mitigate overfitting, enhancing the model’s robustness and generalization capabilities. This dual approach results in a highly adaptive and intelligent system capable of accurately estimating the state features of passive object in real-time. To evaluate the efficacy of this intelligent computing approach, a controlled supervised maneuvering trajectory for underwater passive object is constructed. Real-time estimations of location, velocity, and turn rate for dynamic target are scrutinized across five distinct scenarios by varying the Gaussian observed noise’s standard deviation, aiming to minimize mean square errors (MSEs) between real and estimated values. The effectiveness of the proposed IBRBNC paradigm is demonstrated through extensive simulations and experimental trials. Results showcase its superiority over traditional nonlinear filtering methods like interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model unscented Kalman filter (IMMUKF), especially in the presence of noise, incomplete measurements and sparse data.

Global oil price and stock markets in oil exporting and European countries: Evidence during the Covid-19 and the Russia-Ukraine war

The relationship between oil price movements and stock markets during the COVID-19 pandemic and the geopolitical crisis like the ongoing Russian-Ukraine war is yet unexplored extensively. This study therefore examines the return-correlation effects of oil prices on stock markets and their spillover effects in oil-exporting and European countries using daily closing data. After estimating the GARCH process, we employ the static and dynamic Markov Switching model that allow the relationship between oil price and stock market to switch between two regimes coined the COVID-19 and the Russia-Ukraine war periods. The static model shows stock price returns to respond positively and significantly to oil price returns in Italy, Germany and the US during the Covid-19 period while the response is significantly positive only for US in the Russia-Ukraine war period. As regards the volatility spillover, significant spillover is found from stock to oil market for Nigeria, vice versa for Saudi Arabia and bi-directional volatility spillover found for the US, Italy and Germany during the COVID-19 period. The policy implication is that Nigeria and Saudi Arabia should prioritize financial policy and energy policy respectively while US, Italy and Germany should adopt policy coordination to stabilize oil-stock market volatility during low oil price period like the COVID-19 period.

Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning

This paper proposes a novel optimal control method for multi-zone HVAC systems to enhance energy efficiency and improve occupant comfort. To address the disturbances in outdoor weather and indoor loads, the proposed method formulates the HVAC control problem as a dynamic Markov decision process, and employs deep reinforcement learning (DRL) techniques to obtain the optimal control policy. To manage multiple goals among thermal comfort, indoor air quality (IAQ) and system energy efficiency, a preference-inspired (P-ins) mechanism is designed to achieve the optimal balance among different objectives. The P-ins mechanism effectively guides the agent towards the optimal control policy with high convergence rate. The proposed method has been validated through EnergyPlus-Python co-simulation testbed with real-world data traces, and assessed by an overall evaluation indicator. Results demonstrate that the proposed method can reduce energy consumption without compromising thermal comfort and IAQ. Specifically, the occurrence of temperature violations is reduced below 0.8 %, and a maximum energy saving of 9.41 % can be achieved, compared with traditional methods.

Cost-effectiveness analysis of the tislelizumab versus docetaxel for advanced or metastatic non-small-cell lung cancer in China.

Tislelizumab is the first PD-1 inhibitor in China to demonstrate superior efficacy in second-line or third-line treatment of patients with advanced or metastatic non-small-cell lung cancer (NSCLC). This study aimed to evaluate the cost-effectiveness of tislelizumab compared to docetaxel from a Chinese healthcare system perspective. A dynamic Markov model was developed to evaluate the cost-effectiveness of tislelizumab in comparison to docetaxel in second or third-line treatment. The efficacy data utilized in the model were derived from the RATIONALE-303 clinical trial, while cost and utility values were obtained from the drug data service platform and published studies. The primary outcomes of the model encompassed quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). One-way sensitivity analysis and probabilistic sensitivity analysis were conducted to validate the robustness of the base case analysis results. The tislelizumab group demonstrated a cost increase of CNY 117,473 and a gain of 0.58 QALYs compared to the docetaxel group, resulting in an ICER value of CNY 202,927 per QALY gained. The administration of tislelizumab in patients with advanced or metastatic NSCLC not only extends the progression-free survival (PFS) and overall survival (OS). Moreover, this treatment demonstrates a favorable cost-effectiveness profile across the Chinese population.

Deep clustering of reinforcement learning based on the bang-bang principle to optimize the energy in multi-boiler for intelligent buildings

The bang-bang relays of the multiple-boiler system (MBS) control, are characterized by complex limiter saturation functions and classified as fixed parameters. Their action signals cannot precisely control the nonlinear dynamic building heating demand over their entire range of operation. Moreover, in a mono-boiler system, the bang-bang controller endures increasing short cycling over partial load time due to the heating system being considered to have an oversized boiler at most times of running, thus promoting high energy consumption and fluctuating indoor thermal comfort. So, it is difficult to cope with uncertainties in outdoor environments and indoor heating load. Hence, this study formulates the MBS control problem as a dynamic Markov decision process and applies a deep clustering of reinforcement learning approach to obtain the optimal control policy through interaction with the environment based on multi-agent learning according to bang-bang action. With such an approach, adopting a new boiler sequencing control (BSC) strategy using deep clustering of reinforcement learning based on a bang-bang (DCRLBB) manner. The deep clustering is configured to break Lagrangian trajectory curves into piecewise segments to represent the RL agent's action policy. The agent's action policy signals are configured from the bang-bang reward formula based on trade-off implications to be more adjustable than traditional fixed parameters such as fuzzy bang-bang controller (FBBC). The agent of BSC significantly affects the energy performance of the MBS, whereas the other agent resizes boiler capacity by acting to adjust the boiler solenoid fuel valve. The comparison of results between the proposed strategy and conventional FBBC shows distinct differences in the superior response of DCRLBB under dynamic indoor/outdoor actual conditions and energy saving by more than 32% while maintaining the indoor thermal in the comfortable range.

Dynamic collision estimator for collaborative robots: A dynamic Bayesian network with Markov model for highly reliable collision detection

In this paper, we propose a highly reliable and accurate collision estimator for robot manipulators working in human–robot collaborative environments, based on the Bayesian approach for practical uses. We assume the robot collision as a dynamic Markov process, not a static event, to reflect the transient behavior of mechanical collisions. Thus, the collision estimator can integrate the prior belief on collision and the measurements on the robot state, to produce the current belief on the robot collision in a usual recursive form. An exponential form of observation model, serving as the likelihood function, is constructed on the projected observation domain by using the multi-variate statistical information of empirical models of collision and non-collision cases. The proposed method is validated by using a commercial 7 degree-of-freedom (DOF) collaborative robot arm with random impacts along the links while it is in motion. Results show that the proposed collision estimator achieves a compelling performance with collision estimation time of 8.86ms on average, an overall accuracy of 99.47%, and zero occurrence of false alarm.

Bird’s Eye View feature selection for high-dimensional data

In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird’s Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning

Influence maximization (IM) in complex networks tries to activate a small subset of seed nodes that could maximize the propagation of influence. The studies on IM have attracted much attention due to their wide applications such as item recommendation, viral marketing, information propagation and disease immunization. Existing works mainly model the IM problem as a discrete optimization problem, and use either approximate or meta-heuristic algorithms to address this problem. However, these works are hard to find a good tradeoff between effectiveness and efficiency due to the NP-hard and large-scale network properties of the IM problem. In this article, we propose an evolutionary deep reinforcement learning algorithm (called EDRL-IM) for IM in complex networks. First, EDRL-IM models the IM problem as a continuous weight parameter optimization of deep Q network (DQN). Then, it combines an evolutionary algorithm (EA) and a deep reinforcement learning algorithm (DRL) to evolve the DQN. The EA simultaneously evolves a population of individuals, and each of which represents a possible DQN and returns a solution to the IM problem through a dynamic markov node selection strategy, while the DRL integrates all information and network-specific knowledge of DQNs to accelerate their evolution. Systematic experiments on both benchmark and real-world networks show the superiority of EDRL-IM over the state-of-the-art IM methods in finding seed nodes.

Global prevalence, cascade of care, and prophylaxis coverage of hepatitis B in 2022: a modelling study

The 2016 World Health Assembly endorsed the elimination of hepatitis B virus (HBV) infection as a public health threat by 2030; existing therapies and prophylaxis measures make such elimination feasible, even in the absence of a virological cure. We aimed to estimate the national, regional, and global prevalence of HBV in the general population and among children aged 5 years and younger, as well as the rates of diagnosis, treatment, prophylaxis, and the future burden globally. In this modelling study, we used a Delphi process with data from literature reviews and interviews with country experts to quantify the prevalence, diagnosis, treatment, and prevention measures for HBV infection. The PRoGReSs Model, a dynamic Markov model, was used to estimate the country, regional, and global prevalence of HBV infection in 2022, and the effects of treatment and prevention on disease burden. The future incidence of morbidity and mortality in the absence of additional interventions was also estimated at the global level. We developed models for 170 countries which resulted in an estimated global prevalence of HBV infection in 2022 of 3·2% (95% uncertainty interval 2·7-4·0), corresponding to 257·5 million (216·6-316·4) individuals positive for HBsAg. Of these individuals, 36·0 million were diagnosed, and only 6·8 million of the estimated 83·3 million eligible for treatment were on treatment. The prevalence among children aged 5 years or younger was estimated to be 0·7% (0·6-1·0), corresponding to 5·6 million (4·5-7·8) children with HBV infection. Based on the most recent data, 85% of infants received three-dose HBV vaccination before 1 year of age, 46% had received a timely birth dose of vaccine, and 14% received hepatitis B immunoglobulin along with the full vaccination regimen. 3% of mothers with a high HBV viral load received antiviral treatment to reduce mother-to-child transmission. As 2030 approaches, the elimination targets remain out of reach for many countries under the current frameworks. Although prevention measures have had the most success, there is a need to increase these efforts and to increase diagnosis and treatment to work towards the elimination goals. John C Martin Foundation, Gilead Sciences, and EndHep2030.

The impact of immigration on hepatitis B burden in the United States: a modelling study

The 2016 World Health Assembly endorsed the elimination of hepatitis B virus (HBV) infections by 2030. However, the HBV prevalence in Western countries, where the historical prevalence is low and highly impacted by immigration trends, remains uncertain making planning difficult. We aimed to develop a more accurate estimate of HBV prevalence and identify key immigrant populations that need to be screened, vaccinated, and treated to achieve the elimination targets. US immigration data from 1900 forward and country-specific modeled prevalence by age and sex were used to estimate immigrated HBV infections entering the US, new infections in the US, mortality (all-cause and liver-related), and disease burden through2030. Using a dynamic Markov model, we estimated 1.8 million (95% uncertainty interval: 1.3-2.6 million) HBV infections in 2020 in all ages, higher than the NHANES national serosurvey. Infections between ages 30-74 accounted for 82% of all cases. Furthermore, HBV infections were concentrated among immigrants. New decompensated cirrhosis, hepatocellular carcinoma, and liver related deaths are expected to increase by 20%, 31% and 25% respectively from 2019 to 2030at current diagnosis and treatment rate. National serosurveys can underestimate total infections due to under-sampling in immigrant populations. To meet the WHO elimination targets, culturally appropriate screening and linkage to care programs in the immigrant populations are needed in the US. In their absence, there will be significant increases in the burden of HBV and the US will fail to meet the elimination targets by2030. This analysis was funded by a research grant from Gilead Sciences (IN-US-988-5786) and made possible by grants from John C Martin Foundation (2019-G024), ZeShan Foundation (2021-0101-1-CDA-HEP-10), and EndHep2030 who supported country analyses.

Smart Elderly Care: An Intelligent e-Procurement System for Elderly Supplier Selecting

(1) Objective: to accelerate the digitalization of the elderly care service industry and the construction of the smart elderly care industry, this paper designs an intelligent e-procurement system for elderly suppliers selecting from the perspective of smart elderly care, which can enhance the efficiency of elderly care supply chains and assist manufacturers of elderly products in choosing a reliable, high-quality supplier during trades. (2) Methods: the e-procurement system, including six modules, is built with an improved dynamic Markov Decision Process selection model combined with an Analytic Network Process, bringing dynamic evolution of both inventory cost and purchasing cost into long-term reward calculation, and taking into account 15 common indexes and 7 specific indexes when evaluating suppliers’ competitiveness. (3) Results: a real sample shows that when facing 50 suppliers with 50 different quotations, the e-procurement system selects a stable and reliable supplier that brings the best long-term profits for demand enterprises in ten purchase periods, and it makes the selecting process more efficient and more prompt. (4) Conclusions: the model can be used in the circumstance where an elderly product producer is forced to decide on a long-term strategy or reselect a new stable supplier since it is focused on choosing long-term and high-quality suppliers over numerous periods.

Rank the spreading influence of nodes using dynamic Markov process

Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node’s spreading ability is to evaluate the fraction of susceptible nodes being infected by the target node during the outbreak, i.e. the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbors. We have employed the susceptible-infected-recovered and susceptible-infected-susceptible models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes’ outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process.

Novel Approaches for Resource Management Across Edge Servers

Edge computing aims at reducing computation and storage across the cloud and provides service with reduced latency. Edge devices can be mobile devices, routers, cameras, printers or any Internet of Things (IoT) devices that generate vast amounts of data. The processing of these data is done by virtual machines (VMs) present in the edge servers that are located within close proximity of the edge devices. This work proposes two models which predict resource contention at the edge servers, namely, a Dynamic Markov model for Resource Contention Prediction in Edge Cloud (DMRCP) and a Hybrid Cascade of Regression and Markov model for Resource Contention Prediction (CRMRCP). In DMRCP, a history matrix is updated based on the CPU utilization of a Virtual Machine (VM). This history matrix is used to update a transition probability matrix. This matrix is used to predict the future state of the VM. In the CRMRCP approach, the past CPU utilization values of the virtual machines in the edge servers are used for predicting a set of future CPU utilization values using linear regression, polynomial regression, lasso regression and ridge regression. Then, the predicted future CPU utilization values are used by the dynamic and the second-order Markov models to classify the state of the edge servers as overloaded, underloaded or normally loaded. In both the approaches, the VMs that may cause resource contention are predicted and are migrated to other edge servers such that the destination edge server does not get overloaded after the migration. The DMRCP method is compared with the first-order and the second-order Markov models and the number of VM migrations is analysed to evaluate the performance. The number of VM migrations in the CRMRCP method is compared with that in the second-order Markov model. The overall results prove that the number of VM migrations for the DMRCP is 52.9% less compared to the first-order Markov model and 21.1% less when compared to the second-order Markov model. The number of VM migrations in CRMRCP is reduced by 81.8% when ridge regression cascaded with the second-order Markov model is used.

CHRONIC DISEASE AND LIFE EXPECTANCY DISPARITIES OF OLDER ADULTS IN SINGAPORE

Abstract Singapore is one of the countries with longest life expectancy, also one of the most rapidly ageing societies in the world. This study investigates the future demographic transitions in Singapore, focusing on the life expectancy disparities of the older adults. We developed a dynamic Markov microsimulation model based on the Singapore Multi-Ethnic Cohort study. The model was adapted to project future trends of chronic diseases, mental illnesses as well as hospitalisation costs of Singapore older people from 2020 to 2050. The prevalences of diabetes, heart disease, hypertension, stroke, disability, dementia and depression of people aged 51 and above was projected to be increasing. Overall projected life expectancy at age 51 for male is 27.8 years, and for female is 34.5 years. Moreover, for older people with secondary or upper education, the overall projected life expectancy at age 51 is 34.1 years, active life expectancy at age 51 is 28.6 years. For older people with primary or lower education, the overall life expectancy at age 51 is projected to be 27.9 years, and active life expectancy projected to be 23.9 years. Our study has significant social and economic implications for policymakers in the way that it demonstrates disparate trends in aging and costs among different education groups in Singapore.

Oncology Simulation Model: A Comprehensive and Innovative Approach to Estimate and Project Prevalence and Survival in Oncology.

We demonstrate a new model framework as an innovative approach to more accurately estimate and project prevalence and survival outcomes in oncology. We developed an oncology simulation model (OSM) framework that offers a customizable, dynamic simulation model to generate population-level, country-specific estimates of prevalence, incidence of patients progressing from earlier stages (progression-based incidence), and survival in oncology. The framework, a continuous dynamic Markov cohort model, was implemented in Microsoft Excel. The simulation runs continuously through a prespecified calendar time range. Time-varying incidence, treatment patterns, treatment rates, and treatment pathways are specified by year to account for guideline-directed changes in standard of care and real-world trends, as well as newly approved clinical treatments. Patient cohorts transition between defined health states, with transitions informed by progression-free survival and overall survival as reported in published literature. Model outputs include point prevalence and period prevalence, with options for highly granular prevalence predictions by disease stage, treatment pathway, or time of diagnosis. As a use case, we leveraged the OSM framework to estimate the prevalence of bladder cancer in the United States. The OSM is a robust model that builds upon existing modeling practices to offer an innovative, transparent approach in estimating prevalence, progression-based incidence, and survival for oncologic conditions. The OSM combines and extends the capabilities of other common health-economic modeling approaches to provide a detailed and comprehensive modeling framework to estimate prevalence in oncology using simulation modeling and to assess the impacts of new treatments on prevalence over time.

Inferable dynamic Markov model to predict interference effects

Common decision-making models assume that the system would change over time. But when system is in a particular moment, decision-maker has to make a specific judgment which would put the system in a determined state not an uncertain state. Most decision-making models emphasize the determined state of the system after the judgment and ignore the uncertain state prior to the judgment. So, it would be biased in simulating real-life decision experiments if these uncertain information were lost. Besides, some cognitive decision-making experiments have demonstrated that the law of total probability would not be applicable in practice. What is the reason for this phenomenon? Some researchers thought that interference effects in decision-making process is the main reason, which has been verified from the mathematical proof. Moreover, the interference effects is produced due existing of uncertain information. Quantum theory uses probability amplitude to model uncertainty information of system that would exist simultaneously in multiple states. It is able to better model uncertain states before making a judgement, where judgement can be understood as measurement in quantum theory. Hence, the paper proposed an inferable dynamic Markov decision-making model to quantitatively predict and determine the value of interference effects in the decision-making process. The new model used probability amplitude to represent uncertain information in the decision process, and combined it with Markov decision-making model to study the evolution between different moments in the decision process. Additionally, based on quantum theory principles, it is possible to estimate and infer the related parameters reasonably.