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For older patients with displaced femoral neck fractures, in which primary osteosynthesis is usually not indicated, there are three primary prosthetic options-bipolar hemiarthroplasty (BHA), single-bearing total hip arthroplasty (SB-THA), and dual-mobility THA (DM-THA). However, the optimal choice for managing displaced femoral neck fractures remains controversial. Accordingly, this study aimed to evaluate the cost-effectiveness of BHA, SB-THA, and DM-THA in active older patients with displaced femoral neck fractures. A decision tree combined with a Markov model was employed to analyze the cost and quality-adjusted life years (QALYs) of BHA, SB-THA, and DM-THA for the management in the Japanese healthcare system. By simulating the five-year trajectory of a 75-year-old woman treated for a displaced femoral neck fracture, the cost-effectiveness of the three surgical options was evaluated. One-way sensitivity analysis and probabilistic sensitivity analysis (PSA) were used to assess parameter uncertainty. Additionally, two scenario analyses were conducted for other settings. The treatment was considered to be cost-effective when the incremental cost-effectiveness ratio (ICER) was below the 5,000,000 yen/QALY threshold. Compared with BHA, SB-THA exhibited higher costs but greater health benefits, resulting in an ICER of 1,499,440 yen/QALY. DM-THA offered additional health benefits compared with SB-THA, with an ICER of 4,145,777 yen/QALY. One-way sensitivity analysis revealed some influential parameters. PSA indicated that the probability of DM-THA, SB-THA, and BHA being cost-effective was 40.1%, 38.5%, and 21.4%, respectively. SB-THA was more cost-effective than BHA in patients aged 65-85 years, while DM-THA was more cost-effective than SB-THA in patients aged 65-75 years. The results suggest that SB-THA is a cost-effective alternative to BHA for displaced femoral neck fractures in active older patients, whereas DM-THA is more cost-effective than SB-THA in relatively younger patients. It is, therefore, recommended that orthopedic surgeons select the most appropriate surgical option based on the individual patient's physiological age., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yokoyama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study aimed to evaluate the cost-effectiveness of blood pressure (BP) screening strategies, including 1) home blood pressure measurement (HBPM), (2) serial screening by CBPM followed by HBPM among individuals with high BP i.e., clinic BP ≥140/90 mmHg (Serial1), (3) serial screening by CBPM followed by HBPM among individuals without high BP i.e., clinic blood pressure <140/90 mmHg (Serial2) compared to CBPM alone. A Markov model was applied among Thai population aged 35 years who had not been previously diagnosed with hypertension (HT) during a lifetime horizon with one-year cycle length from a societal perspective. One-way and probabilistic sensitivity analyses using Monte Carlo simulation with 1,000 replications were performed. The total cost of Serial2 (118,283 baht) was the highest and followed by HBPM (110,767 baht), CBPM (110,588 baht) and Serial1 (78,310 baht). The total quality adjusted life years (QALYs) for the population undergoing BP screening with CBPM, HBPM, Serial1, and Serial2 were 22.1557, 22.1511, 22.1286, and 22.1564, respectively. Compared to CBPM, Serial1 was associated with an incremental cost saving of 32,278 and an incremental QALY loss of 0.0271, whereas HBPM was dominated by CBPM due to higher cost (179 baht) and fewer QALY (-0.0046). Additionally, the incremental cost-effectiveness ratio (ICER) of Serial2 was the highest (10,992,000 baht per QALY gained). Moreover, the incidence rate of HT among individuals at age 40-49 years was the most sensitive factor influencing the ICER of HBPM, Serial1 and Serial2. At the Thai societal willingness-to-pay (WTP) threshold of 160,000 baht per QALY gained, the cost saving associated with Serial1 outweighed the QALY loss. Therefore, it is recommended that Serial1 be implemented as a BP screening option in Thailand. This evidence informed policy information could be invaluable for policymakers in making decision regarding BP screening through village health volunteer mechanism in Thailand and similar settings., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Karnjanapiboonwong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: This study aims to compare the cost-effectiveness of sintilimab in combination with chemotherapy, with or without bevacizumab biosimilar IBI305, versus chemotherapy alone for patients with epidermal growth factor receptor (EGFR)-mutated non-small-cell lung cancer (NSCLC) who have progressed on tyrosine-kinase inhibitor (TKI) treatment from the perspective of the Chinese healthcare system., Methods: 10-year Markov model was developed using a 21-day cycle length. Transition probabilities were derived from the ORIENT-31 trial, while cost and health state utilities were obtained from publicly databases, local hospitals, and published literature. Incremental cost-effectiveness ratios (ICERs) were calculated as the primary model output and compared to a willingness-to-pay (WTP) threshold range of $15,289.34 to $38,223.34 per quality-adjusted life-years (QALY). Sensitivity analyses were performed to assess the robustness of the model., Results: In the base-case analysis, sintilimab plus IBI305 and chemotherapy had an ICER of $53,266.32/QALYs, exceeding the upper WTP threshold. Sintilimab plus chemotherapy had an ICER of $15,329.11/QALY, slightly above the lower WTP threshold. Subgroup analysis yielded consistent results. Deterministic sensitivity analyses found no ICER for sintilimab plus chemotherapy beyond the upper WTP threshold. Most model input changes did not decrease the ICER of sintilimab plus IBI305 and chemotherapy below the upper WTP threshold. Probabilistic sensitivity analyses further demonstrated the cost-effectiveness superiority of sintilimab plus chemotherapy over sintilimab plus IBI305 and chemotherapy., Conclusion: This study supports the cost-effectiveness of using sintilimab in combination with chemotherapy. Nevertheless, the cost-effectiveness of combining sintilimab with IBI305 and chemotherapy in this particular patient group may be lacking., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Peng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such predictions, in order, for instance, to be able to ready hospitals and intensive care units for a worst-case scenario without needlessly wasting resources. In this work, we apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters and providing uncertainty quantification for pandemic projections. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020, achieving both a significantly more accurate calibration and prediction than Markov-Chain Monte Carlo (MCMC)-based sampling schemes. The uncertainties on our predictions provide meaningful confidence intervals e.g. on infection figures and hospitalisation rates, while training and running the neural scheme takes minutes where MCMC takes hours. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method's learning capabilities on a reduced dataset, where a complex model is learned from a small number of compartments for which data is available., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gaskin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of many science and engineering problems. However, these methods provide point estimates for the model parameters and are currently unable to accommodate noisy data. We address this challenge by developing and validating the following Bayesian inference methods: the Laplace approximation, Markov Chain Monte Carlo (MCMC) sampling methods, and variational inference. We have found the Laplace approximation to be the best method for this class of problems. Our work can be easily extended to the broader class of symbolic neural networks to which the polynomial neural network belongs., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Fronk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Recent evolution in connected devices modelled a massive stipulation for network traffic resources and classification. Software-defined networking (SDN) enables ML techniques with the Internet of Things (IoT) to automate network traffic. This helps to reduce accuracy and improves latency. Problems by conventional techniques to categorize network traffic acquired from IoT and assign resources can be resolved through SDN solutions. This manuscript proposes a proposed network traffic classification technique on IoT with SDN called Gauss Markov and Flow-balanced Vector Radial Learning (GM-FVRL). With the network traffic features acquired from the IoT devices, SDN-enabled Gauss Markov Correlation-based IoT Network Traffic Feature Extraction is applied to extort relevant network aspects. Next, the flow-balanced radial-based ML model for network traffic categorization uses the relevant extracted network traffic features. With the aid of flow, the balanced radial basis function reduces the influence of noise due to distinct network flow. This helps to improve accuracy and minimize latency. Due to this, better precision and recall is ensured. Performance of our method has been evaluated utilizing a scheme using an SDN traffic dataset. The results show that our method classifies the network traffic with high classification accuracy and minimum latency, ensuring better precision and recall., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Kulandaivel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: Proton beam therapy (PBT) has recently been included in Japan's health insurance benefit package for certain cancer types. This study aimed to determine the cost-effectiveness of PBT as a replacement for conventional three-dimensional conformal radiotherapy (3D-CRT) for locally advanced esophageal cancer (LAEC) that is not covered by social insurance., Methods: We estimated the incremental cost-effectiveness ratio (ICER) of PBT as a replacement for 3D-CRT, using clinical evidence from the literature and expert opinions. We used an economic model, decision tree, and Markov model to illustrate the courses followed by patients with LAEC. Effectiveness was estimated as quality-adjusted life years (QALY) using utility weights for the health state. Social insurance fees were calculated as costs. We assumed two base cases depending on the two existing levels of fees for PBT in social insurance: 2,735,000 Japanese yen (US$20,652) or 1,600,000 yen (US$13,913). The stability of the ICER against these assumptions was appraised using sensitivity analysis., Results: The effectiveness of PBT and 3D-CRT was 2.62 and 2.51 QALY, respectively. The estimated ICER was 14,025,268 yen (US$121,958) per QALY for the higher fee level and 7,026,402 yen (US$61,099) for the lower fee level. According to the Japanese threshold for cost-effectiveness of anticancer therapy of 7,500,000 yen (US$65,217) per QALY gain, the inclusion of PBT for LAEC in the benefit package of social insurance is cost-effective if a lower fee is applied., Conclusion: PBT is a cost-effective alternative to 3D-CRT for LAEC and making it available to patients under social insurance could be justifiable., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sawada et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Reverse epidemiology is a mathematical modelling tool used to ascertain information about the source of a pathogen, given the spatial and temporal distribution of cases, hospitalisations and deaths. In the context of a deliberately released pathogen, such as Bacillus anthracis (the disease-causing organism of anthrax), this can allow responders to quickly identify the location and timing of the release, as well as other factors such as the strength of the release, and the realized wind speed and direction at release. These estimates can then be used to parameterise a predictive mechanistic model, allowing for estimation of the potential scale of the release, and to optimise the distribution of prophylaxis. In this paper we present two novel approaches to reverse epidemiology, and demonstrate their utility in responding to a simulated deliberate release of B. anthracis in ten locations in the UK and compare these to the standard grid-search approach. The two methods-a modified MCMC and a Recurrent Convolutional Neural Network-are able to identify the source location and timing of the release with significantly better accuracy compared to the grid-search approach. Further, the neural network method is able to do inference on new data significantly quicker than either the grid-search or novel MCMC methods, allowing for rapid deployment in time-sensitive outbreaks., Competing Interests: The authors declare that they have no competing interests., (Copyright: © 2024 Shingleton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: HIV drug resistance (HIVDR) is an important challenge in the fight against HIV/AIDS and can threaten progress toward achieving the target of HIV elimination by 2030. Genotyping pretreatment HIVDR testing (DRT) has been proposed as a potential solution. However, the cost-effectiveness of this intervention needs to be evaluated to determine its feasibility and potential impact on healthcare systems. This study aimed to assess the cost-effectiveness of DRT among people living with HIV (PLHIV) in Iran., Methods: 1000 hypothetical PLHIV were simulated in terms of cost and effectiveness based on quality-adjusted life Years (QALY). The Markov Model was developed to calculate incremental cost-effectiveness ratio (ICER) using TreeAge Pro 2020. Deterministic and probabilistic analyses were performed for sensitivity analyses., Results: Results showed that compared to not performing pretreatment HIVDR testing, this intervention gained 0.035999 QALY with an incremental cost of 1,695.32 USD. The ICER was calculated as 47,093.53 USD, indicating that pretreatment DRT was not cost-effective. The probability of opportunistic infection (OI) in people with viral failure, the effectiveness of Dolutegravir in people without drug resistance, and the quality of life (QoL) of people in the AIDS stage were found to be the most important variables affecting ICER. With an increasing willingness to pay more than 53,000 USD, pretreatment DRT testing will become cost-effective., Conclusion: Based on our findings, pretreatment HIVDR testing is not currently cost-effective in Iran as it imposes high costs on healthcare systems with few benefits for People living with HIV (PLHIV). However, if resources are available, drug resistance testing can be a valuable tool in generating HIV molecular data and molecular surveillance of HIV., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mirzaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: In Malaysia, there is now a dearth of recommendations pertaining to the priority of biologic treatments for the effective management of psoriasis, given the multitude of available therapeutic alternatives. Present analysis reports results of a cost-effectiveness model that determines the most optimal arrangement of biologic treatments, with a particular focus of adding biosimilars to the existing treatment pathway for psoriasis in Malaysia., Methods: A Markov model was developed to compare the cost effectiveness of various biologic sequential treatments in a hypothetical cohort of moderate to severe psoriasis patient in Malaysia over a lifetime horizon. The model simulated the progression of patients through three lines of active biologic therapy, before transitioning to best supportive care. Costs and effects were discounted annually at a rate of 3%., Results: First line secukinumab has produced lowest incremental cost effectiveness ratios (ICERs) when compared to first line systemic [ICERs value; US$152,474 (first set analysis) and US$110,572 (second set analysis)] and first line phototherapy [ICERs value; US$147,057 (first set analysis) and US$107,616 (second set analysis)]. However, these values were slightly higher than the Malaysian based threshold of three times gross domestic product per capita, US$104,337. A 40% reduction in the unit costs of reference biologics renders most of the evaluated treatment sequences cost-effective., Conclusion: Adding biosimilar to the current treatment sequence could achieve cost savings ranging from 4.3% to 10.8% without significant loss of effectiveness. Given the significant impact of comorbidities and the resulting decline in quality of life among individuals with psoriasis, it may be justifiable to establish a threshold of up to US$184,000 per quality-adjusted life year (QALY) for the provision of therapies in the context of Malaysia., Competing Interests: The authors have no competing interests to declare that are relevant to the content of this article., (Copyright: © 2024 Azizam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Empagliflozin demonstrates promising clinical benefits in patients with heart failure (HF). While an early study demonstrates that empagliflozin is cost-effective for treating HF patients with reduced ejection fraction (HFrEF) in Malaysia, its cost-effectiveness for HF with ejection fraction (EF)>40% remains unclear. Therefore, the current study aimed to assess the cost-effectiveness of adding empagliflozin to the standard of care (SoC) for HF patients with EF>40% from the perspective of Malaysian healthcare system. Subsequently, the results were consolidated with the findings for HFrEF to evaluate the cost-effectiveness of empagliflozin when used for all HF patients in Malaysia, irrespective of EF., Methods: A cost-utility analysis was performed using a validated Markov model, which modelled a cohort of adult patients through health states related to symptom severity and functional impairment, to estimate costs and quality-adjusted life-years (QALYs). The influence of model inputs and assumptions, sensitivity, scenario, and subgroup analyses were explored. All costs were expressed in 2022 Malaysian ringgits (RM). Costs and QALYs were discounted at an annual rate of 3.0% as per local pharmacoeconomic guideline., Results: The base-case incremental cost-effectiveness ratio (ICER) for HF patients with EF>40% was RM 40,454 per QALY gained. At a cost-effectiveness threshold of RM 47,439/QALY gained, empagliflozin was cost-effective in 57% of replications. The model outcomes were sensitive to inputs related to the treatment effect of empagliflozin in reducing HF-related hospitalisation and cardiovascular mortality, and empagliflozin cost. For the overall HF population, the ICER was RM 29,463/QALY gained., Conclusion: The findings suggest that empagliflozin is a cost-effective treatment option for the Malaysian HF population, including those with EF>40%. As such, the intervention warrants consideration by the Malaysian healthcare provider to mitigate the burden of HF and address the unmet needs of the EF>40% population., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Mild cognitive impairment (MCI) is a pre-clinical stage of Alzheimer's disease (AD). Understanding the transition probabilities across the disease continuum of AD, ranging from MCI to AD to Mortality is crucial for the economic modeling of AD and effective planning of future interventions and healthcare resource allocation decisions. This study uses the Multi-state Markov model to quantify the transition probabilities along the disease progression and specifically investigates medications as modifiable risk factors of AD associated with accelerated or decelerated transition times from MCI to AD, MCI to mortality, and AD to mortality., Methods: Individuals with MCI were identified from the National Alzheimer's Coordinating Center between September 2005 and May 2021. A three-state Markov model was postulated to model the disease progression among three states: MCI, AD, and mortality with adjustment for demographics, genetic characteristics, comorbidities and medications. Transition probabilities, the total length of stay in each state, and the hazard ratios of the use of medications for diabetes, hypertension, and hypercholesterolemia (the known modifiable risk factors of AD) were evaluated for these transitions., Results: 3,324 individuals with MCI were identified. The probability of developing AD after one year since the initial diagnosis of MCI is 14.9%. After approximately 6 years from the initial diagnosis of MCI, the probability of transitioning to AD increases to nearly 41.7% before experiencing a subsequent decline. The expected total lengths of stay were 5.38 (95% CI: 0.002-6.03) years at MCI state and 7.61 (95%CI: 0.002-8.88) years at AD state. Patients with active use of lipid-lowering agents were associated with significantly lower hazards of transitioning from MCI to AD (HR: 0.83, 95%CI:0.71-0.96), MCI to mortality (HR: 0.51, 95%CI:0.34-0.77), and AD to mortality (HR: 0.81, 95%CI:0.66-0.99)., Conclusions: Results suggest that lipid-lowering agents may confer a protective effect, delaying the onset of AD. Additionally, lipid-lowering agents indicate a favorable association with a longer survival time., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Cochlear Implant (CI) has been shown to improve speech comprehension, sound localization and tinnitus in adults with Single-Sided-Deafness (SSD) compared to standard treatment currently available in the Dutch setting such as a CROS (Contralateral Routing of Signals) hearing device or a BCD (Bone Conduction Device). Also, for the pediatric population with SSD, CI has shown to be clinically meaningful. Because currently no information is available on the health economic effects of CI in adults and children with SSD in the Netherlands, a cost-utility analysis was conducted., Methods: We developed a Markov cohort model, for both the adult and pediatric SSD population, with three states: implant, no implant, and dead. CI was compared with the Bone Conduction Device (BCD) treatment, requiring surgery and no specific treatment. The time horizon of the model was lifelong, costs were discounted with 3% and effects with 1.5%. A societal perspective was taken, including productivity costs in the analysis, with costing data based on publicly available prices for the Netherlands. Values for clinical outcome parameters, i.e. hearing gain, and event probabilities were based on existing literature. Deterministic and probabilistic sensitivity analyses as well as scenario analyses were performed to outline uncertainty of individual and combined parameters., Results: Mean per patient costs for CI in the adult population were €194,051 (95%-CrI €177,274 to €211,108) compared to the total costs of €185,310 (95%-CrI €182,367 to €194,142) for BCD resulting in a cost difference of €8,826 (95%-CrI -€5,020 to €18,252). Compared to no treatment, the cost difference was -€25,089 (95%-CrI -€31,678 to -€6,003). Adults who were treated with CI gained 18.41 (95%-CrI 18.07 to 18.75) quality adjusted life years (QALY) whereas BCD patients gained 15.81 QALYs (95%-CrI 15.53 to 16.10), a difference of 2.60 QALYs (95%-CrI 2.15 to 3.05). The Incremental Cost Effectiveness Ratio (ICER) for adults with CI was determined to be €3,494/QALY gained. Patient without treatment gained 13.46 QALY (95%-CrI 13.20 to 13.73), a difference of 4.95 (95%-CrI 4.87 to 5.01) resulting in CI dominating no treatment. The ICER remained below the Dutch threshold of €20,000/QALY. The probabilistic sensitivity analyses confirmed the results. For children, CI dominated when compared to BCD and when compared to no treatment. Compared to BCD, CI led to a cost saving of €29,611 (95%-CrI -€126,800 to €54,375) and compared to no treatment, CI resulted in a cost saving of €57,658 (95%-CrI -€146,687 to €5,919). The incremental QALY gain compared to BCD was 7.22 (95%-CrI 4.19 to 8.55) and 26.03 (95%-CrI 20.82 to 31.06) compared to no treatment., Conclusions: Based on the results of this health economic evaluation with a Markov cohort model, it is very likely that CI is cost-effective compared to BCD and to no treatment in the Dutch adult and pediatric population with SSD. In both populations the ICER was below the Dutch cost-effectiveness threshold of €20,000/QALY., Competing Interests: A. L. Smit received unrestricted grands from Cochlear Ltd for studies on treatments for SSD. . The funding does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 van Lieshout et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Due to the complex internal working process of circulating cooling water systems, most traditional control methods struggle to achieve stable and precise control. Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). The structure is based on the Markov decision process of the recirculating cooling water system. Initially, the TD3 algorithm is employed to construct a deep reinforcement learning agent. Subsequently, a state space is selected, and a dense reward function is designed, considering the multivariable characteristics of the recirculating cooling water system. The agent updates its network based on different reward values obtained through interactions with the system, thereby gradually aligning the action values with the optimal policy. The TD3-RTM method introduces a reference trajectory model to accelerate the convergence speed of the agent and reduce oscillations and instability in the control system. Subsequently, simulation experiments were conducted in MATLAB/Simulink. The results show that compared to PID, fuzzy PID, DDPG and TD3, the TD3-RTM method improved the transient time in the flow loop by 6.09s, 5.29s, 0.57s, and 0.77s, respectively, and the Integral of Absolute Error(IAE) indexes decreased by 710.54, 335.1, 135.97, and 89.96, respectively, and the transient time in the temperature loop improved by 25.84s, 13.65s, 15.05s, and 0.81s, and the IAE metrics were reduced by 143.9, 59.13, 31.79, and 1.77, respectively. In addition, the overshooting of the TD3-RTM method in the flow loop was reduced by 17.64, 7.79, and 1.29 per cent, respectively, in comparison with the PID, the fuzzy PID, and the TD3., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Despite much progress in exploring the coupled epidemic-awareness dynamics in multiplex networks, little attention has been paid to the joint impacts of behavioral observability and social proof on epidemic spreading. Since both the protective actions taken by direct neighbors and the observability of these actions have essential influence on individuals' decisions. Thus, we propose a UAPU-SIR model by integrating the effects of these two factors into the decision-making process of taking preventive measures. Specifically, a new state called taken protective actions is introduced into the original unaware-aware-unaware (UAU) model to characterize the action-taken state of individuals after getting epidemic-related information. Using the Microscopic Markov Chain Approach (MMCA), the methods and model are described, and the epidemic threshold is analytically derived. We find that both observability of protecting behaviors and social proof can reduce the epidemic prevalence and raise the epidemic threshold. Moreover, only if observability of protection actions reaches a certain threshold can accelerating information diffusion is able to inhibit disease spreading and result in higher epidemic threshold. We also discover that, reducing the forgetting rate of information is able to decrease epidemic size., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Air-rail intermodal transportation (ARIT) plays a crucial role in China's intermodal transportation system. This study aims to model and optimize issues such as inefficiency and complexity in China's ARIT freight transportation using Business Process Reengineering and Stochastic Petri Nets theories. The Petri Net (PN) model for incoming freight transport in ARIT is based on actual operations, employing a new method involving Stochastic Petri Nets and isomorphic Markov Chains theory for performance analysis. Performance analysis results help intuitively identify areas needing optimization. Based on optimization principles, elements such as railway container packaging are improved, resulting in an optimized PN model for ARIT. Finally, data analysis shows that the optimized ARIT model reduces total delay by 7.7% compared to the original. This demonstrates that the new method, combining Markov Chain performance analysis and optimization principles, is feasible and effective for ARIT optimization., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Lei, Mu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a computational account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Schwöbel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.M.B. and E.M.M. are co-founders and shareholders of Erisyon, Inc., and are co-inventors on granted patents or pending patent applications related to single-molecule protein sequencing. A.M.B. serves on the board of directors and E.M.M. serves on the scientific advisory board. M.B.S., K.V., T.B., H.D.S., J.H.M., T.M.F., C.M., and A.M.B. are affiliated with Erisyon, Inc., as employees or shareholders. H.D.S. is currently employed by UT Austin with funding from a Sponsored Research Agreement from Erisyon, Inc., (Copyright: © 2024 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Several hepatitis A outbreaks have recently been reported in Kerala state, India. To inform coverage decision of hepatitis A vaccine in Kerala, this study aimed to examine the cost-effectiveness of 1) hepatitis A vaccination among children aged 1 year and individuals aged 15 years, and 2) serological screening of individuals aged 15 years and vaccination of susceptible as compared to no vaccination or vaccination without serological screening. Both live attenuated hepatitis A vaccine and inactivated hepatitis A vaccine were considered in the analysis. A combination of decision tree and Markov models with a cycle length of one year was employed to estimate costs and benefits of different vaccination strategies. Analysis were based on both societal and payer perspectives. The lifetime costs and outcomes were discounted by 3%. Our findings indicated that all strategies were cost-saving for both societal and payer perspectives. Moreover, budget impact analysis revealed that vaccination without screening among individuals aged 15 years could save the government's budget by reducing treatment cost of hepatitis A. Our cost-effectiveness evidence supports the inclusion of hepatitis A vaccination into the vaccination program for children aged 1 year and individuals aged 15 years in Kerala state, India., Competing Interests: The authors have declared that no competing interest exist., (Copyright: © 2024 Gurav et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As the spatial arrangement of trees planted along streets in cities makes their bases potential ecological corridors for the flora, urban tree bases may be a key contributor to the overall connectivity of the urban ecosystem. However, these tree bases are also a highly fragmented environment in which extinctions are frequent. The goal of this study was to assess the plant species' ability to survive and spread through urban tree bases. To do so, we developed a Bayesian framework to assess the extinction risk of a plant metapopulation using presence/absence data, assuming that the occupancy dynamics was described by a Hidden Markov Model. The novelty of our approach is to take into account the combined effect of low-distance dispersal and the potential presence of a seed bank on the extinction risk. We introduced a metric of the extinction risk and examined its performance over a wide range of metapopulation parameters. We applied our framework to yearly floristic inventories carried out in 1324 tree bases in Paris, France. While local extinction risks were generally high, extinction risks at the street scale varied greatly from one species to another. We identified 10 plant species that could survive and spread through urban tree bases, and three plant traits correlated with the extinction risk at the metapopulation scale: the maximal height, and the beginning and end of the flowering period. Our results suggest that some plant species can use urban tree bases as ecological corridors despite high local extinction risks by forming a seed bank. We also identified other plant traits correlated with the ability to survive in tree bases, related to the action of gardeners. Moreover, our findings demonstrate that our Bayesian estimation framework based on percolation theory has the potential to be extended to more general metapopulations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Louvet et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Time series studies of microbiome interventions provide valuable data about microbial ecosystem structure. Unfortunately, existing models of microbial community dynamics have limited temporal memory and expressivity, relying on Markov or linearity assumptions. To address this, we introduce a new class of models based on transfer functions. These models learn impulse responses, capturing the potentially delayed effects of environmental changes on the microbial community. This allows us to simulate trajectories under hypothetical interventions and select significantly perturbed taxa with False Discovery Rate guarantees. Through simulations, we show that our approach effectively reduces forecasting errors compared to strong baselines and accurately pinpoints taxa of interest. Our case studies highlight the interpretability of the resulting differential response trajectories. An R package, mbtransfer, and notebooks to replicate the simulation and case studies are provided., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sankaran, Jeganathan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: To examine the cost-effectiveness of using granulocyte colony-stimulating factor (G-CSF) for primary or secondary prophylaxis in patients with breast cancer from the perspective of Taiwan's National Health Insurance Administration., Methods: A Markov model was constructed to simulate the events that may occur during and after a high-risk chemotherapy treatment. Various G-CSF prophylaxis strategies and medications were compared in the model. Effectiveness data were derived from the literature and an analysis of the National Health Insurance Research Database (NHIRD). Cost data were obtained from a published NHIRD study, and health utility values were also obtained from the literature. Sensitivity analyses were performed to assess the uncertainty of the cost-effectiveness results., Results: In the base-case analysis, primary prophylaxis with pegfilgrastim had an incremental cost-effectiveness ratio (ICER) of NT$269,683 per quality-adjusted life year (QALY) gained compared to primary prophylaxis with lenograstim. The ICER for primary prophylaxis with lenograstim versus no G-CSF prophylaxis was NT$61,995 per QALY gained. The results were most sensitive to variations in relative risk of febrile neutropenia (FN) for pegfilgrastim versus no G-CSF prophylaxis. Furthermore, in the probabilistic sensitivity analysis, at a willingness-to-pay threshold of one times Taiwan's gross domestic product per capita, the probability of being cost-effective was 88.1% for primary prophylaxis with pegfilgrastim., Conclusions: Our study suggests that primary prophylaxis with either short- or long-acting G-CSF could be considered cost-effective for FN prevention in breast cancer patients receiving high-risk regimens., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tseng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Epidemic or pathogen emergence is the phenomenon by which a poorly transmissible pathogen finds its evolutionary pathway to become a mutant that can cause an epidemic. Many mathematical models of pathogen emergence rely on branching processes. Here, we discuss pathogen emergence using Markov chains, for a more tractable analysis, generalizing previous work by Kendall and Bartlett about disease invasion. We discuss the probability of emergence failure for early epidemics, when the number of infected individuals is small and the number of the susceptible individuals is virtually unlimited. Our formalism addresses both directly transmitted and vector-borne diseases, in the cases where the original pathogen is 1) one step-mutation away from the epidemic strain, and 2) undergoing a long chain of neutral mutations that do not change the epidemiology. We obtain analytic results for the probabilities of emergence failure and two features transcending the transmission mechanism. First, the reproduction number of the original pathogen is determinant for the probability of pathogen emergence, more important than the mutation rate or the transmissibility of the emerged pathogen. Second, the probability of mutation within infected individuals must be sufficiently high for the pathogen undergoing neutral mutations to start an epidemic, the mutation threshold depending again on the basic reproduction number of the original pathogen. Finally, we discuss the parameterization of models of pathogen emergence, using SARS-CoV1 as an example of zoonotic emergence and HIV as an example for the emergence of drug resistance. We also discuss assumptions of our models and implications for epidemiology., Competing Interests: The author has declared that no competing interests exist., (Copyright: © 2024 Romulus Breban. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Trastuzumab has significantly enhanced the survival and prognosis of individuals diagnosed with human epidermal growth factor receptor 2 (HER2)-positive early breast cancer. Considering its relatively high costs, we aimed to examine the cost-effectiveness of trastuzumab plus chemotherapy compared with chemotherapy alone in HER2-positive early breast cancer from an Indonesian healthcare payer's perspective., Methods: A Markov model was developed to project the lifetime health benefits and costs associated with trastuzumab treatment for a cohort of women with HER2-positive early breast cancer. Efficacy data and baseline characteristics in the base-case analysis were primarily derived from the 11-year results of the HERA trial. Costs were based on verified reimbursement data from Indonesia's Health and Social Security Agency (BPJS Kesehatan) of the year 2020. A scenario analysis was conducted with efficacy data based on the joint analysis from the NSABP B-31 and NCCTG N9831 trials, allowing for subgroup analysis by age at diagnosis. Univariate and probabilistic sensitivity analyses were conducted to assess the influence of parameter uncertainty., Results: In the base-case analysis, the results indicated that the lifetime costs for trastuzumab plus chemotherapy and chemotherapy alone were US$33,744 and US$22,720, respectively, resulting in substantial incremental savings of US$11,024 per patient for the former. Trastuzumab plus chemotherapy also led to higher total quality-adjusted life years (QALYs) and life years gained (LYG), resulting in incremental cost-effectiveness ratios (ICERs) of US$6,842 per QALY and US$5,510 per LYG. In scenario analysis, the subgroup with an age at diagnosis <40 years old reflected the most cost-effective subgroup. Both the base-case and scenario analyses demonstrated cost-effectiveness with a willingness-to-pay threshold of three-times Gross Domestic Product (GDP). Sensitivity analyses confirmed the robustness of the findings and conclusions., Conclusion: In Indonesia, trastuzumab plus chemotherapy can be considered cost-effective compared to chemotherapy alone at a willingness-to-pay threshold of three times GDP, and it is likely most cost-effective in women <40 years of age., Competing Interests: The authors declare that they have no competing interests, (Copyright: © 2024 Khoirunnisa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The following paper describes a steady-state model of concurrent choice, termed the active time model (ATM). ATM is derived from maximization principles and is characterized by a semi-Markov process. The model proposes that the controlling stimulus in concurrent variable-interval (VI) VI schedules of reinforcement is the time interval since the most recent response, termed here "the active interresponse time" or simply "active time." In the model after a response is generated, it is categorized by a function that relates active times to switch/stay probabilities. In the paper the output of ATM is compared with predictions made by three other models of operant conditioning: melioration, a version of scalar expectancy theory (SET), and momentary maximization. Data sets considered include preferences in multiple-concurrent VI VI schedules, molecular choice patterns, correlations between switching and perseveration, and molar choice proportions. It is shown that ATM can account for all of these data sets, while the other models produce more limited fits. However, rather than argue that ATM is the singular model for concurrent VI VI choice, a consideration of its concept space leads to the conclusion that operant choice is multiply-determined, and that an adaptive viewpoint-one that considers experimental procedures both as selecting mechanisms for animal choice as well as tests of the controlling variables of that choice-is warranted., Competing Interests: The author has declared that no competing interests exist., (Copyright: © 2024 J. Mark Cleaveland. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sun, Li. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Meng, Bouchard. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive but simple model able to generate sequences with a given autocorrelation structure. Our research establishes that the theoretical entropy rate of a LAMP model is equivalent to the theoretical entropy rate of the underlying first-order Markov Chain. The LAMP model captures complex relationships and long-range dependencies in data with similar expressibility to a higher-order Markov process. While a higher-order Markov process has a polynomial parameter space, a LAMP model is characterised only by a probability distribution and the transition matrix of an underlying first-order Markov Chain. This surprising result can be explained by the information balance between the additional structure imposed by the next state distribution of the LAMP model, and the additional randomness of each new transition. Understanding the entropy of the LAMP model provides a tool to model complex dependencies in data while retaining useful theoretical results. To emphasise the practical applications, we use the LAMP model to estimate the entropy rate of the LastFM, BrightKite, Wikispeedia and Reuters-21578 datasets. We compare estimates calculated using frequency probability estimates, a first-order Markov model and the LAMP model, also considering two approaches to ensure the transition matrix is irreducible. In most cases the LAMP entropy rates are lower than those of the alternatives, suggesting that LAMP model is better at accommodating structural dependencies in the processes, achieving a more accurate estimate of the true entropy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Smart et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The intensification of intervention activities against the fatal vector-borne disease gambiense human African trypanosomiasis (gHAT, sleeping sickness) in the last two decades has led to a large decline in the number of annually reported cases. However, while we move closer to achieving the ambitious target of elimination of transmission (EoT) to humans, pockets of infection remain, and it becomes increasingly important to quantitatively assess if different regions are on track for elimination, and where intervention efforts should be focused. We present a previously developed stochastic mathematical model for gHAT in the Democratic Republic of Congo (DRC) and show that this same formulation is able to capture the dynamics of gHAT observed at the health area level (approximately 10,000 people). This analysis was the first time any stochastic gHAT model has been fitted directly to case data and allows us to better quantify the uncertainty in our results. The analysis focuses on utilising a particle filter Markov chain Monte Carlo (MCMC) methodology to fit the model to the data from 16 health areas of Mosango health zone in Kwilu province as a case study. The spatial heterogeneity in cases is reflected in modelling results, where we predict that under the current intervention strategies, the health area of Kinzamba II, which has approximately one third of the health zone's cases, will have the latest expected year for EoT. We find that fitting the analogous deterministic version of the gHAT model using MCMC has substantially faster computation times than fitting the stochastic model using pMCMC, but produces virtually indistinguishable posterior parameterisation. This suggests that expanding health area fitting, to cover more of the DRC, should be done with deterministic fits for efficiency, but with stochastic projections used to capture both the parameter and stochastic variation in case reporting and elimination year estimations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Davis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Based on monthly economic data spanning from January 2015 to December 2022, we have established an analytical framework to examine the "Russia-Ukraine conflict-financial market pressure and energy market-China carbon emission trading prices." To achieve this objective, we developed indices for financial system pressure, the energy market, and investor sentiment, applying a mediation effects model to validate their transmission mechanisms. Subsequently, the TVP-SV-VAR model was employed to scrutinize the nonlinear impact of the Russia-Ukraine conflict on the valuation of China's carbon emission trading rights. This model integrates time-varying parameters (TVP) and stochastic volatility (SV), utilizing Markov Chain Monte Carlo (MCMC) technology for parameter estimation. Finally, various wavelet analysis techniques, including continuous wavelet transform, cross-wavelet transform, and wavelet coherence spectrum, were applied to decompose time series data into distinct time-frequency scales, facilitating an analysis of the lead-lag relationships within each time series. The research outcomes provide crucial insights for safeguarding the interests of trading organizations, refining the structure of the carbon market, and mitigating systemic risks on a global scale., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The high-quality development of service industry has become an important engine for promoting sustainable economic development. This paper first constructed the evaluation index system of high-quality development of service industry, based on panel data from 2005 to 2020. Second, Kernel density, Markov chain and Dagum Gini coefficient were used to represent the regional differences and dynamic evolution of service industry, and the Koo method was used to explore the characteristics of spatial agglomeration. Finally, social network analysis was used to identify core indicators. The study found that: (1) From 2005 to 2020, the overall level of service industry first decreases and then increases, with Chengdu and Chongqing leading other cities. (2) The development of service industry in the CCEC has large spatial differences, mainly due to inter-regional differences. (3) The level of spatial agglomeration is less variable, with high agglomeration mainly in Chengdu. (4) Indicators such as the level of human capital are the core factors of its high-quality development. This study is of great theoretical and practical significance for the optimization and upgrading of service industry in the CCEC and the synergetic development of the region., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In 2016, Hao and Friedman developed a deterministic model of Alzheimer's disease progression using a system of partial differential equations. This model describes the general behavior of the disease, however, it does not incorporate the molecular and cellular stochasticity intrinsic to the underlying disease processes. Here we extend the Hao and Friedman model by modeling each event in disease progression as a stochastic Markov process. This model identifies stochasticity in disease progression, as well as changes to the mean dynamics of key agents. We find that the pace of neuron death increases whereas the production of the two key measures of progression, Tau and Amyloid beta proteins, decelerates when stochasticity is incorporated into the model. These results suggest that the non-constant reactions and time-steps have a significant effect on the overall progression of the disease., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Meaghan Elizabeth Parks. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The stress-strength reliability (SSR) model ϕ = P(Y < X) is used in numerous disciplines like reliability engineering, quality control, medical studies, and many more to assess the strength and stresses of the systems. Here, we assume X and Y both are independent random variables of progressively first failure censored (PFFC) data following inverse Pareto distribution (IPD) as stress and strength, respectively. This article deals with the estimation of SSR from both classical and Bayesian paradigms. In the case of a classical point of view, the SSR is computed using two estimation methods: maximum product spacing (MPS) and maximum likelihood (ML) estimators. Also, derived interval estimates of SSR based on ML estimate. The Bayes estimate of SSR is computed using the Markov chain Monte Carlo (MCMC) approximation procedure with a squared error loss function (SELF) based on gamma informative priors for the Bayesian paradigm. To demonstrate the relevance of the different estimates and the censoring schemes, an extensive simulation study and two pairs of real-data applications are discussed., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Alharbi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Stochastic population models are widely used to model phenomena in different areas such as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond. Quantitative analysis of stochastic population models easily becomes challenging due to the combinatorial number of possible states of the population. Moreover, while the modeller easily hypothesises the mechanistic aspects of the model, the quantitative parameters associated to these mechanistic transitions are difficult or impossible to measure directly. In this paper, we investigate how formal verification methods can aid parameter inference for population discrete-time Markov chains in a scenario where only a limited sample of population-level data measurements-sample distributions among terminal states-are available. We first discuss the parameter identifiability and uncertainty quantification in this setup, as well as how the existing techniques of formal parameter synthesis and Bayesian inference apply. Then, we propose and implement four different methods, three of which incorporate formal parameter synthesis as a pre-computation step. We empirically evaluate the performance of the proposed methods over four representative case studies. We find that our proposed methods incorporating formal parameter synthesis as a pre-computation step allow us to significantly enhance the accuracy, precision, and scalability of inference. Specifically, in the case of unidentifiable parameters, we accurately capture the subspace of parameters which is data-compliant at a desired confidence level., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Klein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Cancer causes a substantial burden to our society, both from a health and an economic perspective. To improve cancer patient outcomes and lower society expenses, early diagnosis and timely treatment are essential. The recent COVID-19 crisis has disrupted the care trajectory of cancer patients, which may affect their prognosis in a potentially negative way. The purpose of this paper is to present a flexible decision-analytic Markov model methodology allowing the evaluation of the impact of delayed cancer care caused by the COVID-19 pandemic in Belgium which can be used by researchers to respond to diverse research questions in a variety of disruptive events, contexts and settings., Methods: A decision-analytic Markov model was developed for 4 selected cancer types (i.e. breast, colorectal, lung, and head and neck), comparing the estimated costs and quality-adjusted life year losses between the pre-COVID-19 situation and the COVID-19 pandemic in Belgium. Input parameters were derived from published studies (transition probabilities, utilities and indirect costs) and administrative databases (epidemiological data and direct medical costs). One-way and probabilistic sensitivity analyses are proposed to consider uncertainty in the input parameters and to assess the robustness of the model's results. Scenario analyses are suggested to evaluate methodological and structural assumptions., Discussion: The results that such decision-analytic Markov model can provide are of interest to decision makers because they help them to effectively allocate resources to improve the health outcomes of cancer patients and to reduce the costs of care for both patients and healthcare systems. Our study provides insights into methodological aspects of conducting a health economic evaluation of cancer care and COVID-19 including insights on cancer type selection, the elaboration of a Markov model, data inputs and analysis., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

We propose a multivariate Grey-Markov model to quantify traffic accident risk from different causality factors in roundabouts that is uniquely suited for the scarce and stochastic traffic crash data from roundabouts. A data sample of traffic crashes occurring in roundabouts in the U.S. State of Michigan from 2016 to 2021 was collected to investigate the capabilities of this modeling methodology. The multivariate grey model (MGM(1,4)) was constructed using grey relational analysis to determine the best dimensions for model optimization. Then, the Markov chain is introduced to address the unfitness of stochastic, fluctuating data in the MGM(1,4) model. Finally, our proposed hybrid MGM(1,4)-Markov model is compared with other models and validated. This study highlights the superior predictive performance of our MGM(1,4)-Markov model in fore-casting roundabout traffic accidents under data-limited conditions, achieving a 3.02% accuracy rate, in contrast to the traditional GM(1,1) model at 8.30% and the MGM(1,4) model at 4.47%. Moreover, incorporating human, vehicle, and environmental risk factors into a multivariate crash system yields more accurate predictions than merely aggregating crash counts., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Li, Li. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Wang, Mustafa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Cross-impact balance (CIB) analysis provides a system-theoretical view of scenarios useful for investigating complex socio-economic systems. CIB can synthesize a variety of qualitative or quantitative inputs and return information suggestive of system evolution. Current software tools for CIB are limited to identifying system attractors as well as describing system evolution from only one scenario of initial conditions at a time. Through this study, we enhance CIB by developing and applying a method that considers all possible system evolutions as transitions in a Markov chain. We investigated a simple three-variable system (27 possible scenarios) of the demographic transition and were able to generally replicate the findings of traditional CIB. Through our experiments with four possible approaches to produce CIB Markov chains, we found that information about transition pathways is gained; however, information about system attractors may be lost. Through a comparison of model results to a recent literature review on human demography, we found that low-income countries are more likely to remain stuck in a demographic trap if economic development is not prioritized alongside educational gains. Future work could test our comparative methodological findings for systems comprised of more than three variables., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Schweizer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Accessing information on an underlying network driving a biological process often involves interrupting the process and collecting snapshot data. When snapshot data are stochastic, the data's structure necessitates a probabilistic description to infer underlying reaction networks. As an example, we may imagine wanting to learn gene state networks from the type of data collected in single molecule RNA fluorescence in situ hybridization (RNA-FISH). In the networks we consider, nodes represent network states, and edges represent biochemical reaction rates linking states. Simultaneously estimating the number of nodes and constituent parameters from snapshot data remains a challenging task in part on account of data uncertainty and timescale separations between kinetic parameters mediating the network. While parametric Bayesian methods learn parameters given a network structure (with known node numbers) with rigorously propagated measurement uncertainty, learning the number of nodes and parameters with potentially large timescale separations remain open questions. Here, we propose a Bayesian nonparametric framework and describe a hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler directly addressing these challenges. In particular, in our hybrid method, Hamiltonian Monte Carlo (HMC) leverages local posterior geometries in inference to explore the parameter space; Adaptive Metropolis Hastings (AMH) learns correlations between plausible parameter sets to efficiently propose probable models; and Parallel Tempering takes into account multiple models simultaneously with tempered information content to augment sampling efficiency. We apply our method to synthetic data mimicking single molecule RNA-FISH, a popular snapshot method in probing transcriptional networks to illustrate the identified challenges inherent to learning dynamical models from these snapshots and how our method addresses them., Competing Interests: The authors declare no competing interest., (Copyright: © 2023 Kilic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics methods. Previous models have demonstrated that nucleotides flanking polymorphic sites-the local sequence context-explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways-first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Adams et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this work, we develop a new set of Bayesian models to perform registration of real-valued functions. A Gaussian process prior is assigned to the parameter space of time warping functions, and a Markov chain Monte Carlo (MCMC) algorithm is utilized to explore the posterior distribution. While the proposed model can be defined on the infinite-dimensional function space in theory, dimension reduction is needed in practice because one cannot store an infinite-dimensional function on the computer. Existing Bayesian models often rely on some pre-specified, fixed truncation rule to achieve dimension reduction, either by fixing the grid size or the number of basis functions used to represent a functional object. In comparison, the new models in this paper randomize the truncation rule. Benefits of the new models include the ability to make inference on the smoothness of the functional parameters, a data-informative feature of the truncation rule, and the flexibility to control the amount of shape-alteration in the registration process. For instance, using both simulated and real data, we show that when the observed functions exhibit more local features, the posterior distribution on the warping functions automatically concentrates on a larger number of basis functions. Supporting materials including code and data to perform registration and reproduce some of the results presented herein are available online., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Graduation rates are a key measure of the long-term efficacy of academic interventions. However, challenges to using traditional estimates of graduation rates for underrepresented students include inherently small sample sizes and high data requirements. Here, we show that a Markov model increases confidence and reduces biases in estimated graduation rates for underrepresented minority and first-generation students. We use a Learning Assistant program to demonstrate the Markov model's strength for assessing program efficacy. We find that Learning Assistants in gateway science courses are associated with a 9% increase in the six-year graduation rate. These gains are larger for underrepresented minority (21%) and first-generation students (18%). Our results indicate that Learning Assistants can improve overall graduation rates and address inequalities in graduation rates for underrepresented students., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Tedeschi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Compression as an accelerant of computation is increasingly recognized as an important component in engineering fast real-world machine learning methods for big data; c.f., its impact on genome-scale approximate string matching. Previous work showed that compression can accelerate algorithms for Hidden Markov Models (HMM) with discrete observations, both for the classical frequentist HMM algorithms-Forward Filtering, Backward Smoothing and Viterbi-and Gibbs sampling for Bayesian HMM. For Bayesian HMM with continuous-valued observations, compression was shown to greatly accelerate computations for specific types of data. For instance, data from large-scale experiments interrogating structural genetic variation can be assumed to be piece-wise constant with noise, or, equivalently, data generated by HMM with dominant self-transition probabilities. Here we extend the compressive computation approach to the classical frequentist HMM algorithms on continuous-valued observations, providing the first compressive approach for this problem. In a large-scale simulation study, we demonstrate empirically that in many settings compressed HMM algorithms very clearly outperform the classical algorithms with no, or only an insignificant effect, on the computed probabilities and infered state paths of maximal likelihood. This provides an efficient approach to big data computations with HMM. An open-source implementation of the method is available from https://github.com/lucabello/wavelet-hmms., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Bello et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot's hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: E.M.M and Z.B.S. are co-founders and shareholders of Erisyon, Inc., and are co-inventors on granted patents or pending patent applications related to single-molecule protein sequencing. Z.B.S. is an employee of Erisyon, Inc. E.M.M. serves on the scientific advisory board., (Copyright: © 2023 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model. The posterior distributions for the unknown parameters are derived, and the Markov chain Monte Carlo sampling algorithms are also given. The proposed method is illustrated by three simulation examples and a real dataset., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Yuan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Metaheuristic techniques have been utilized extensively to predict industrial systems' optimum availability. This prediction phenomenon is known as the NP-hard problem. Though, most of the existing methods fail to attain the optimal solution due to several limitations like slow rate of convergence, weak computational speed, stuck in local optima, etc. Consequently, in the present study, an effort has been made to develop a novel mathematical model for power generating units assembled in sewage treatment plants. Markov birth-death process is adopted for model development and generation of Chapman-Kolmogorov differential-difference equations. The global solution is discovered using metaheuristic techniques, namely genetic algorithm and particle swarm optimization. All time-dependent random variables associated with failure rates are considered exponentially distributed, while repair rates follow the arbitrary distribution. The repair and switch devices are perfect and random variables are independent. The numerical results of system availability have been derived for different values of crossover, mutation, several generations, damping ratio, and population size to attain optimum value. The results were also shared with plant personnel. Statistical investigation of availability results justifies that particle swarm optimization outdoes genetic algorithm in predicting the availability of power-generating systems. In present study a Markov model is proposed and optimized for performance evaluation of sewage treatment plant. The developed model is one that can be useful for sewage treatment plant designers in establishing new plants and purposing maintenance policies. The same procedure of performance optimization can be adopted in other process industries too., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Saini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)