Optimizing the timing of an end-of-outbreak declaration: Ebola virus disease in the Democratic Republic of the Congo.

Following the apparent final case in an Ebola virus disease (EVD) outbreak, the decision to declare the outbreak over must balance societal benefits of relaxing interventions against the risk of resurgence. Estimates of the end-of-outbreak probability (the probability that no future cases will occur) provide quantitative evidence that can inform the timing of an end-of-outbreak declaration. An existing modeling approach for estimating the end-of-outbreak probability requires comprehensive contact tracing data describing who infected whom to be available, but such data are often unavailable or incomplete during outbreaks. Here, we develop a Markov chain Monte Carlo-based approach that extends the previous method and does not require contact tracing data. Considering data from two EVD outbreaks in the Democratic Republic of the Congo, we find that data describing who infected whom are not required to resolve uncertainty about when to declare an outbreak over.

Strategies for isoniazid preventive therapy in HIV-positive patients who consume alcohol.

WHO guidance to defer isoniazid preventive therapy (IPT) among those with regular alcohol use because of hepatotoxicity concerns may exclude many people living with HIV (PLWH) at high TB risk in these settings.To evaluate hepatotoxicity during TB preventive therapy (TPT) in PLWH who report alcohol use in Uganda over 10 years.We developed a Markov model of latent TB infection, isoniazid preventive therapy (IPT - a type of TPT), and TB disease using data from the Alcohol Drinkers' Exposure to Preventive Therapy for TB (ADEPTT) study. We modeled several treatment scenarios, including no IPT, IPT with liver enzyme monitoring (AST/ALT) during treatment, and IPT with pre-screening using the tuberculin skin test (TST).The no IPT scenario had 230 TB deaths/100,000 population over 10 years, which is more than that seen in any IPT scenario. IPT, even with no monitoring, was preferred over no IPT when population TB disease incidence was >50 in 100,000.For PLWH who report alcohol use in high TB burden settings, IPT should be offered, ideally with regular AST/ALT monitoring. However, even if regular monitoring is not possible, IPT is still preferable to no IPT in almost every modeled scenario..

Cost-effectiveness of single-pill and separate-pill administration of antihypertensive triple combination therapy: a population-based microsimulation study.

BACKGROUND: Single-pill combination (SPC) of three antihypertensive drugs has been shown to improve adherence to therapy compared with free combinations, but little is known about its long-term costs and health consequences. This study aimed to evaluate the lifetime cost-effectiveness profile of a three-drug SPC of an angiotensin-converting enzyme inhibitor, a calcium-channel blocker, and a diuretic vs the corresponding two-pill administration (a two-drug SPC plus a third drug separately) from the Italian payer perspective. METHODS: A cost-effectiveness analysis was conducted using multi-state semi-Markov modeling and microsimulation. Using the healthcare utilization database of the Lombardy Region (Italy), 30,172 and 65,817 patients aged ≥ 40 years who initiated SPC and two-pill combination, respectively, between 2015 and 2018 were identified. The observation period extended from the date of the first drug dispensation until death, emigration, or December 31, 2019. Disease and cost models were parametrized using the study cohort, and a lifetime microsimulation was applied to project costs and life expectancy for the compared strategies, assigning each of them to each cohort member. Costs and life-years gained were discounted by 3%. Probabilistic sensitivity analysis with 1,000 samples was performed to address parameter uncertainty. RESULTS: Compared with the two-pill combination, the SPC increased life expectancy by 0.86 years (95% confidence interval [CI] 0.61-1.14), with a mean cost differential of -€12 (95% CI -9,719-8,131), making it the dominant strategy (ICER = -14, 95% CI -€15,871-€7,113). The cost reduction associated with the SPC was primarily driven by savings in hospitalization costs, amounting to €1,850 (95% CI 17-7,813) and €2,027 (95% CI 19-8,603) for patients treated with the SPC and two-pill combination, respectively. Conversely, drug costs were higher for the SPC (€3,848, 95% CI 574-10,640 vs. €3,710, 95% CI 263-11,955). The cost-effectiveness profile did not significantly change according to age, sex, and clinical status. CONCLUSIONS: The SPC was projected to be cost-effective compared with the two-pill combination at almost all reasonable willingness-to-pay thresholds. As it is currently prescribed to only a few patients, the widespread use of this strategy could result in benefits for both patients and the healthcare system.

Impact of community structure on the spread of epidemics on time-varying multiplex networks.

Community structure plays a crucial role in realistic networks and different communities can be created by groups of interest and activity events, and exploring the impact of community properties on collective dynamics is an active topic in the field of network science. Here, we propose a new coupled model with different time scales for online social networks and offline epidemic spreading networks, in which community structure is added into online social networks to investigate its role in the interacting dynamics between information diffusion and epidemic spreading. We obtain the analytical equations of epidemic threshold by MMC (Microscopic Markov Chain) method and conduct a large quantities of numerical simulations using Monte Carlo simulations in order to verify the accuracy of the MMC method, and more valuable insights are also obtained. The results indicate that an increase in the probability of the mobility of an individual can delay the spread of epidemic-related information in the network, as well as delaying the time of the peak of the infection density in the network. However, an increase in the contact ability of mobile individuals produces a facilitating effect on the spread of epidemics. Finally, it is also found that the stronger the acceptance of an individual to information coming from a different community, the lower the infection density in the network, which suggests that it has an inhibitory effect on the disease spreading.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM.

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Characterization of domain formation in complex membranes.

A widely known property of lipid membranes is their tendency to undergo a separation into disordered (Ld) and ordered (Lo) domains. This impacts the local structure of the membrane relevant for the physical (e.g., enhanced electroporation) and biological (e.g., protein sorting) significance of these regions. The increase in computing power, advancements in simulation software, and more detailed information about the composition of biological membranes shifts the study of these domains into the focus of classical molecular dynamics simulations. In this chapter, we present a versatile yet robust analysis pipeline that can be easily implemented and adapted for a wide range of lipid compositions. It employs Gaussian-based Hidden Markov Models to predict the hidden order states of individual lipids by describing their structure through the area per lipid and the average SCC order parameters per acyl chain. Regions of the membrane with a high correlation between ordered lipids are identified by employing the Getis-Ord local spatial autocorrelation statistic on a Voronoi tessellation of the lipids. As an example, the approach is applied to two distinct systems at a coarse-grained resolution, demonstrating either a strong tendency towards phase separation (1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1,2-dilinoleoyl-sn-glycero-3-phosphocholine (DIPC), cholesterol) or a weak tendency toward phase separation (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1-palmitoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine (PUPC), cholesterol). Explanations of the steps are complemented by coding examples written in Python, providing both a comprehensive understanding and practical guidance for a seamless integration of the workflow into individual projects.

Optimising Alzheimer's Disease Diagnosis and Treatment: Assessing Cost-Utility of Integrating Blood Biomarkers in Clinical Practice for Disease-Modifying Treatment.

BACKGROUND: Recent developments in blood biomarkers (BBM) have shown promising results in diagnosing amyloid pathology in Alzheimer's Disease (AD). However, information on how these BBMs can best be used in clinical settings to optimise clinical decision-making and long-term health outcomes for individuals with AD is still lacking. OBJECTIVES: We aim to assess the potential value of BBM in AD diagnosis within the context of disease-modifying treatment (DMT). DESIGN: We developed a decision analytic model to evaluate the long-term health outcomes using BBM in AD diagnosis. We compared standard of care (SOC) diagnosis workflow to the integration of BBM as a (1) referral decision tool in primary health center (PHC) and (2) triaging tool for invasive CSF examination in specialist memory clinic (MC). We combined a decision tree and a Markov model to simulate the patient's diagnostic journey, treatment decisions following diagnosis and long-term health outcomes. Input parameters for the model were identified from published literature and registry data analysis. We conducted a cost-utility analysis from the societal perspective using a one-year cycle length and a 30-year (lifetime) horizon. MEASUREMENTS: We reported the simulated outcomes in the percentage of correct diagnosis, costs (in 2022 Euros), quality-adjusted life year (QALY), and incremental cost-effectiveness ratios (ICER) associated with each diagnosis strategy. RESULTS: Compared to SOC, integrating BBM in PHC increased patient referrals by 8% and true positive AD diagnoses by 10.4%. The lifetime costs for individuals diagnosed with AD were € 249,685 and €250,287, and QALYs were 9.5 and 9.52 in SOC and PHC pathways, respectively. The cost increments were €603, and QALYs gained were 0.01, resulting in an ICER of €48,296. Using BBM in MC reduced the exposure to invasive CSF procedures and costs but also reduced true positive AD diagnoses and QALYs. CONCLUSIONS: Using BBM at PHC to make referral decisions might increase initial diagnostic costs but can prevent high costs associated with disease progression, providing a cost-effective DMT is available, whereas using BBM in MC could reduce the initial evaluation cost but incur high costs associated with disease progression.

Estimated Investment Need to Increase England's Capacity to Diagnose Eligibility for an Alzheimer's Treatment to G7 Average Capacity Levels.

BACKGROUND: As disease-modifying Alzheimer's (AD) treatments are becoming available, concerns have been raised that even high-income countries lack the diagnostic capacity to accurately identify eligible patients in a timely manner. OBJECTIVES: We analyze how much NHS England would have to invest in capacity for AD specialists, biomarker testing with PET scans or CSF testing and MRI scans to reach G7 average levels and estimate the effect on wait times in the diagnostic process. DESIGN: Desk research and expert interviews for cost and capacity data. Markov model to estimate wait times. SETTING: NHS England. MEASUREMENTS: AD specialists, and PET and MRI scanners per capita in G7 countries and wait times in England under different investment scenarios. RESULTS: England has the lowest number of PET and MRI scanners and the second-lowest of AD specialists per capita among the G7 countries. An investment of GBP 14 billion over ten years would be needed to reach G7 average levels, of which 31%, 22%, 10%, 37% would be devoted to capacity for memory assessment services, PET scanning, CSF analysis, and MRI scanning, respectively. This investment would reduce estimated average wait times by around 87% between 2023 and 2032. CONCLUSIONS: The NHS England has large gaps in diagnostic capacity for AD. Without substantial investments, AD patients in England would experience substantial wait times and avoidable disease progression.

Effects of behavioral observability and social proof on the coupled epidemic-awareness dynamics in multiplex networks.

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.

Long-term cost-effectiveness analysis of rugby fans in training-New Zealand: a body weight reduction programme for males.

OBJECTIVES: We sought to extrapolate the long-term costs and clinical impacts attributed to the rugby fans in training-New Zealand (RUFIT-NZ) trial in Aotearoa, New Zealand. DESIGN: A modelled cost-effectiveness analysis using efficacy data from RUFIT-NZ was conducted from the Aotearoa New Zealand healthcare perspective. SETTING: A Markov cohort model was constructed with a lifetime time horizon. The model simulated events of myocardial infarction (MI), stroke and type 2 diabetes mellitus (T2DM) occurring among a hypothetical cohort of 10 000 individuals receiving either the RUFIT-NZ intervention or no intervention. Efficacy data were based on the RUFIT-NZ trial, and the latest Global Burden of Disease study was used to extrapolate the impact of body weight reduction on clinical outcomes of T2DM, MI or stroke. Cost and utility data were drawn from the RUFIT-NZ trial and published sources. PRIMARY OUTCOME MEASURES: The incremental cost-effectiveness ratio (ICER). RESULTS: Over a lifetime time horizon, participants in the RUFIT-NZ intervention gained 0.02 (discounted) quality-adjusted life years (QALYs) at an additional cost of NZ$863, relative to no intervention. The estimated ICER was NZ$49 515 per QALY gained (discounted), which is above the arbitrary willingness-to-pay threshold of NZ$45 000 per QALY. Sensitivity analyses supported the robustness of these findings. CONCLUSIONS: RUFIT-NZ was associated with a reduction in cardiovascular and endocrine events for overweight and obese males. However, based on conservative assumptions, RUFIT-NZ was unlikely to be cost-effective from a healthcare system perspective. TRIAL REGISTRATION NUMBER: ACTRN12619000069156.

Cost-utility and budget impact analysis of neoadjuvant dual HER2 targeted therapy for HER2-positive breast cancer in Sri Lanka.

This study aimed to assess the cost-utility and budget impact of dual to single HER2 targeted neoadjuvant therapy for HER2-positive breast cancer in Sri Lanka. A five-health state Markov model with lifetime horizon was used to assess the cost-utility of neoadjuvant trastuzumab (T) plus pertuzumab (P) or lapatinib (L) compared to single therapy of T with chemotherapy (C), in public healthcare system and societal perspectives. Input parameters were estimated using local data, network meta-analysis, published reports and literature. Costs were adjusted to year 2021 (1USD = LKR194.78). Five-year budget impact for public healthcare system was assessed. Incremental cost-effectiveness ratios in societal perspective for neoadjuvantLTC plus adjuvantT (strategy 3), neoadjuvantPTC plus adjuvantT (strategy 2), neoadjuvantLTC plus adjuvantLT (strategy 5), and neoadjuvantPTC plus adjuvantPT (strategy 4) compared to neoadjuvantTC plus adjuvantT (strategy 1) were USD2716, USD5600, USD6878, and USD12127 per QALY gained, respectively. One GDP per-capita (USD3815) was considered as the cost-effectiveness threshold for the analysis. Even though only the ICER for strategy 3 was cost-effective, uncertainty of efficacy parameter was revealed. For strategy 2 neoadjuvant PTC plus adjuvant T, a 25% reduction of neoadjuvant regimen cost was required to be cost effective for use in early HER2 positive breast cancer.

Embedding of Markov matrices for d ⩽ 4.

The embedding problem of Markov matrices in Markov semigroups is a classic problem that regained a lot of impetus and activities through recent needs in phylogeny and population genetics. Here, we give an account for dimensions d ⩽ 4 , including a complete and simplified treatment of the case d = 3 , and derive the results in a systematic fashion, with an eye on the potential applications. Further, we reconsider the setup of the corresponding problem for time-inhomogeneous Markov chains, which is needed for real-world applications because transition rates need not be constant over time. Additional cases of this more general embedding occur for any d ⩾ 3 . We review the known case of d = 3 and describe the setting for future work on d = 4 .

Modelling the lymphatic metastatic progression pathways of OPSCC from multi-institutional datasets.

The elective clinical target volume (CTV-N) in oropharyngeal squamous cell carcinoma (OPSCC) is currently based mostly on the prevalence of lymph node metastases in different lymph node levels (LNLs) for a given primary tumor location. We present a probabilistic model for ipsilateral lymphatic spread that can quantify the microscopic nodal involvement risk based on an individual patient's T-category and clinical involvement of LNLs at diagnosis. We extend a previously published hidden Markov model (HMM), which models the LNLs (I, II, III, IV, V, and VII) as hidden binary random variables (RVs). Each represents a patient's true state of lymphatic involvement. Clinical involvement at diagnosis represents the observed binary RVs linked to the true state via sensitivity and specificity. The primary tumor and the hidden RVs are connected in a graph. Each edge represents the conditional probability of metastatic spread per abstract time-step, given disease at the edge's starting node. To learn these probabilities, we draw Markov chain Monte Carlo samples from the likelihood of a dataset (686 OPSCC patients) from three institutions. We compute the model evidence using thermodynamic integration for different graphs to determine which describes the data best.The graph maximizing the model evidence connects the tumor to each LNL and the LNLs I through V in order. It predicts the risk of occult disease in level IV is below 5% if level III is clinically negative, and that the risk of occult disease in level V is below 5% except for advanced T-category (T3 and T4) patients with clinical involvement of levels II, III, and IV. The provided statistical model of nodal involvement in OPSCC patients trained on multi-institutional data may guide the design of clinical trials on volume-deescalated treatment of OPSCC and contribute to more personal guidelines on elective nodal treatment.

hopsy - a methods marketplace for convex polytope sampling in Python.

SUMMARY: Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open-source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling library HOPS, hopsy inherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems. AVAILABILITY AND IMPLEMENTATION: Sources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/.

Toddlers strategically adapt their information search.

Adaptive information seeking is essential for humans to effectively navigate complex and dynamic environments. Here, we developed a gaze-contingent eye-tracking paradigm to examine the early emergence of adaptive information-seeking. Toddlers (N = 60, 18-36 months) and adults (N = 42) either learnt that an animal was equally likely to be found in any of four available locations, or that it was most likely to be found in one particular location. Afterwards, they were given control of a torchlight, which they could move with their eyes to explore the otherwise pitch-black task environment. Eye-movement data and Markov models show that, from 24 months of age, toddlers become more exploratory than adults, and start adapting their exploratory strategies to the information structure of the task. These results show that toddlers' search strategies are more sophisticated than previously thought, and identify the unique features that distinguish their information search from adults'.

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Cost-effectiveness of approaches to cervical cancer screening in Malawi: comparison of frequencies, lesion treatment techniques, and risk-stratified approaches.

BACKGROUND: Recently-updated global guidelines for cervical cancer screening incorporated new technologies-most significantly, the inclusion of HPV DNA detection as a primary screening test-but leave many implementation decisions at countries' discretion. We sought to develop recommendations for Malawi as a test case since it has the second-highest cervical cancer burden globally and high HIV prevalence. We incorporated updated epidemiologic data, the full range of ablation methods recommended, and a more nuanced representation of how HIV status intersects with cervical cancer risk and exposure to screening to model outcomes of different approaches to screening. METHODS: Using a Markov model, we estimate the relative health outcomes and costs of different approaches to cervical cancer screening among Malawian women. The model was parameterized using published data, and focused on comparing "triage" approaches-i.e., lesion treatment (cryotherapy or thermocoagulation) at differing frequencies and varying by HIV status. Health outcomes were quality-adjusted life years (QALYs) and deaths averted. The model was built using TreeAge Pro software. RESULTS: Thermocoagulation was more cost-effective than cryotherapy at all screening frequencies. Screening women once per decade would avert substantially more deaths than screening only once per lifetime, at relatively little additional cost. Moreover, at this frequency, it would be advisable to ensure that all women who screen positive receive treatment (rather than investing in further increases in screening frequency): for a similar gain in QALYs, it would cost more than four times as much to implement once-per-5 years screening with only 50% of women treated versus once-per-decade screening with 100% of women treated. Stratified screening schedules by HIV status was found to be an optimal approach. CONCLUSIONS: These results add new evidence about cost-effective approaches to cervical cancer screening in low-income countries. At relatively infrequent screening intervals, if resources are limited, it would be more cost-effective to invest in scaling up thermocoagulation for treatment before increasing the recommended screening frequency. In Malawi or countries in a similar stage of the HIV epidemic, a stratified approach that prioritizes more frequent screening for women living with HIV may be more cost-effective than population-wide recommendations that are HIV status neutral.

Estimating error rates for single molecule protein sequencing experiments.

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.

Differential Target Multiplexed Spinal Cord Stimulation: A UK Cost-Effectiveness Analysis.

OBJECTIVES: The aim of this economic analysis was to evaluate the cost-effectiveness of differential target multiplexed spinal cord stimulation (DTM-SCS) for treating chronic intractable low back pain, compared with conventional spinal cord stimulation (C-SCS) and conservative medical management (CMM), by updating and expanding the inputs for a previously published cross-industry model. MATERIALS AND METHODS: This model comprised a 12-month decision-tree phase followed by a long-term Markov model. Costs and outcomes were calculated from a UK National Health Service perspective, over a base-case horizon of 15 years and up to a maximum of 40 years. All model inputs were derived from published literature or other deidentified sources and updated to reflect recent clinical trials and costs. Deterministic and one-way sensitivity analyses were performed to calculate costs and quality-adjusted life-years (QALYs) across the 15-year time horizon and to explore the impact of individual parameter variability on the cost-effectiveness results. Probabilistic sensitivity analysis was undertaken to explore the impact of joint parameter uncertainty on the results. RESULTS: DTM-SCS was the most cost-effective option from a payer perspective. Compared with CMM alone, DTM-SCS was associated with an incremental cost-effectiveness ratio (ICER) of £6101 per QALY gained (incremental net benefit [INB] = £21,281). The INB for C-SCS compared with CMM was lower than for DTM-SCS, at £8551. For the comparison of DTM-SCS and C-SCS, an ICER of £897 per QALY gained was calculated, with a 99.5% probability of cost-effectiveness at a £20,000 per QALY threshold. CONCLUSIONS: Among patients with low back pain treated over a 15-year follow-up period, DTM-SCS and C-SCS are cost-effective compared with CMM, from both payer and societal perspectives. DTM-SCS is associated with a lower ICER than that of C-SCS. Wider uptake of DTM-SCS in the UK health care system is warranted to manage chronic low back pain.

Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review.

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics.