Bayesian Markov Chain Monte Carlo Research Articles

Estimate Mass Density Value as A Priori Information for Gravity by using Bayesian Markov Chain Monte Carlo (MCMC)

<p class="Abstract">In the gravity method, information about mass density value is very important because it will influence the characteristic of the 1-D gravity acceleration graph. However, it is quite difficult to guess the mass density value so that is suitable to the 1-D acceleration graph. This is called “a priori” information. Trial and error way is one way to solve this problem. It is a very random value guess also. To make sure that the initial guess of mass density is a good parameter, Bayesian Markov Chain Monte Carlo (MCMC) can be used. It generates many possibilities from the guess value and then these possibilities will be selected to the best one by likelihood way. The validation is expressed by the random graph as a consequence of the iteration number step of the possibilities. This research is started by using certain values of mass density to create a synthetic model for the field data in Banggai Sula because the area has a complex geology. The synthetic model is used because the gravity forward modelling equation has the sinusoidal form. After Bayesian MCMC is applied to the initial mass density value, it will produce a new mass density value or the estimation value with its response to the 1-D gravity acceleration synthetic graph. Finally, this information will be very useful to create the 2D or 3D inverse modelling in Gravity.</p>

Benchmark Dose for Dioxin Based on Gestational Diabetes Mellitus Using Coexposure Statistical Methods and an Optimized Physiologically Based Toxicokinetic Model.

Dioxins are ubiquitous endocrine-disrupting substances, but determining the effects and benchmark doses in situations of coexposure is highly challenging. The objective of this study was to assess the relationship between dioxin andgestational diabetes mellitus (GDM), calculate the benchmark dose (BMD) of dioxin in coexposure scenarios, and derive a daily exposure threshold using an optimized physiologically based toxicokinetic (PBTK) model. Based on a nested case-control study including 77 cases with GDM and 154 controls, serum levels of 29 dioxin-like compounds (DLCs) along with 10 perfluoroalkyl acids (PFAAs), seven polybrominated diphenyl ethers (PBDEs), and five non-dioxin-like polychlorinated biphenyls (ndl-PCBs) were measured at 9-16 weeks of gestation. Bayesian machine kernel regression (BKMR) was employed to identify significant chemicals, and probit and logistic models were used to calculate BMD adjusted for significant chemicals. A physiologically based toxicokinetic (PBTK) model was optimized using polyfluorinated dibenzo-p-dioxins and dibenzofurans (PFDD/Fs) data by the Bayesian-Monte Carlo Markov chain method and was used to determine the daily dietary exposure threshold. The median serum level of total dioxin toxic equivalent (TEQ) was 7.72 pg TEQ/g fat. Logistic regression analysis revealed that individuals in the fifth quantile of total TEQ level had significantly higher odds of developing GDM compared to those in the first quantile (OR, 8.87; 95% CI 3.19, 27.58). The BKMR analysis identified dioxin TEQ and BDE-153 as the compounds with the greatest influence. The binary logistic and probit models showed that the BMD10 (benchmark dose corresponding to a 10% extra risk) and BMDL10 (lower bound on the BMD10) were 3.71 and 3.46 pg TEQ/g fat, respectively, when accounting for coexposure to BDE-153 up to the 80% level. Using the optimized PBTK model and modifying factor, it was estimated that daily exposure should be below 4.34 pg TEQ kg-1 bw week-1 in order to not reach a harmful serum concentration for GDM. Further studies should utilize coexposure statistical methods and physiologically based pharmacokinetic (PBTK) models in reference dose calculation.

Refining Credit Risk Analysis- Integrating Bayesian MCMC with Hamiltonian Monte Carlo

The accurate prediction of loan defaults is paramount for financial institutions to enhance decision-making processes, optimize loan approval rates, and mitigate associated risks. This study develops a predictive model utilizing Bayesian Markov Chain Monte Carlo (MCMC) techniques to forecast potential loan defaults. Employing a comprehensive dataset of 255,000 borrower profiles, which include detailed borrower characteristics and loan information, the model integrates advanced statistical methods to assess and interpret the factors influencing loan defaults. The Bayesian framework allows for robust uncertainty quantification and model complexity management, making it particularly suitable for the nuanced nature of credit risk assessment. Results from the model demonstrate a compelling accuracy rate, substantially aligning with industry benchmarks while providing deeper insights into the probability of default as influenced by various borrower attributes. This research underscores the efficacy of Bayesian MCMC modelling in financial risk management and offers a scalable approach for financial institutions aiming to refine their credit evaluation strategies.

Revisiting the Vertical Distribution of H i Absorbing Clouds in the Solar Neighborhood

The vertical distribution of cold neutral hydrogen (H i) clouds is a constraint on models of the structure, dynamics, and hydrostatic balance of the interstellar medium. In 1978, Crovisier pioneered a method to infer the vertical distribution of H i absorbing clouds in the solar neighborhood. Using data from the Nançay 21 cm absorption survey, Crovisier determined the mean vertical displacement of cold H i clouds, 〈∣z∣〉. We revisit that author’s analysis and explore the consequences of truncating the H i absorption sample in Galactic latitude. For any nonzero latitude limit, we find that the quantity inferred by Crovisier is not the mean vertical displacement but rather a ratio involving higher moments of the vertical distribution. The resultant distribution scale heights are thus ∼1.5 to ∼3 times smaller than previously determined. In light of this discovery, we develop a Bayesian Monte Carlo Markov Chain method to infer the vertical distribution of H i absorbing clouds. We fit our model to the original Nançay data and find a vertical distribution moment ratio 〈∣z∣3〉/〈∣z∣2〉 = 97 ± 15 pc, which corresponds to a Gaussian scale height σ z = 61 ± 9 pc, an exponential scale height λ z = 32 ± 5 pc, and a rectangular half-width W z,1/2 = 129 ± 20 pc. Consistent with recent simulations, the vertical scale height of cold H i clouds appears to remain constant between the inner Galaxy and the Galactocentric distance of the solar neighborhood. Local fluctuations might explain the large-scale height observed at the same Galactocentric distance on the far side of the Galaxy.

Ensemble learning methods of inference for spatially stratified infectious disease systems.

Individual level models are a class of mechanistic models that are widely used to infer infectious disease transmission dynamics. These models incorporate individual level covariate information accounting for population heterogeneity and are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. However, Bayesian MCMC methods of inference are computationally expensive for large data sets. This issue becomes more severe when applied to infectious disease data collected from spatially heterogeneous populations, as the number of covariates increases. In addition, summary statistics over the global population may not capture the true spatio-temporal dynamics of disease transmission. In this study we propose to use ensemble learning methods to predict epidemic generating models instead of time consuming Bayesian MCMC method. We apply these methods to infer disease transmission dynamics over spatially clustered populations, considering the clusters as natural strata instead of a global population. We compare the performance of two tree-based ensemble learning techniques: random forest and gradient boosting. These methods are applied to the 2001 foot-and-mouth disease epidemic in the U.K. and evaluated using simulated data from a clustered population. It is shown that the spatially clustered data can help to predict epidemic generating models more accurately than the global data.

Resolving heterogeneous fluxes from tundra halves the growing season carbon budget

Abstract. Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic.

Shear capacity model of rectangular RC columns based on Bayesian update

Reinforced concrete (RC) columns are function as structural member resisting vertical loading. The combined effects of vertical loading and transverse loading might induce shear failure of RC columns, posing a significant threat to the structure's integrity. While the accuracy of existing models to determine the shear strength of RC column is crucial for design, there is a need for further refinement. In this context, a study introduces a probabilistic shear capacity model based on the truss-arch theory and incorporating the Bayesian theory Monte Carlo Markov Chain (MCMC) method. This model delineates the shear contribution into four distinct components: concrete, stirrup, axial compression force and longitudinal reinforcement longitudinal reinforcement in the middle of the section parallel to the loading direction. A database, comprising 233 RC column specimen sets, facilitates the model's regression analysis. Calibration is achieved against the density distribution and confidence intervals of the posterior parameters. When set against four established models, the new proposal offers a mean value closely aligned with experimental data and showcases reduced variance. To substantiate the shear mechanism of the proposed model, a thorough contribution of each shear component's contribution is made with those in four existing models: Sezen, ACI 318, Priestley, and GB50010. While the Sezen and ACI 318 models recognize the shear contributions of concrete and stirrup, the Priestley and GB50010 models extend to factor in the beneficial impact of the axial compression force component. Comparative analysis underscores the superior predictive capability of the newly proposed model.

Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania

This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for flood protection measures and infrastructure design. Trend analysis using p value, Kendall’s tau, and Sen’s slope indicated no statistically significant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region.

Monte Carlo Full-Waveform Inversion of Cross-Hole Ground-Penetrating Radar Data Based on Improved Residual Network

A full-waveform inversion (FWI) of ground-penetrating radar (GPR) data can be used to effectively obtain the parameters of a shallow subsurface. Introducing the Markov chain Monte Carlo (MCMC) algorithm into the FWI can reduce the dependence on the initial model and obtain the global optimal solution, but it requires a large number of computations. In order to better detect underground targets based on ground-penetrating radar data, this paper proposes a joint scheme of an improved ResNet and an MCMC full-waveform inversion. This scheme combines the Bayesian MCMC algorithm and an improved ResNet to accurately invert the target dielectric permittivity. The introduction of deep learning networks into the forward calculation part of the MCMC inversion algorithm replaced the complex forward simulation process, greatly improving the inversion speed. It is worth noting that the neural network model was an approximation of complex forward modeling, and, therefore, it contained modeling errors. The MCMC method could quantify and explain modeling errors during the inversion process, reducing the impact of modeling errors on the inversion results. Finally, a simulation dataset was constructed for training and testing, and the errors were statistically analyzed. The results showed that this method can accurately reconstruct underground medium targets, and it has strong robustness and efficiency.

Reliability characteristics of COVID-19 death rate using generalized progressive hybrid censored data

PurposeA progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.Design/methodology/approachIn this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.FindingsThe Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.Originality/valueThe ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.

Multi-population mortality modelling: a Bayesian hierarchical approach

AbstractModelling mortality co-movements for multiple populations has significant implications for mortality/longevity risk management. This paper assumes that multiple populations are heterogeneous sub-populations randomly drawn from a hypothetical super-population. Those heterogeneous sub-populations may exhibit various patterns of mortality dynamics across different age groups. We propose a hierarchical structure of these age patterns to ensure the model stability and use a Vector Error Correction Model (VECM) to fit the co-movements over time. Especially, a structural analysis based on the VECM is implemented to investigate potential interdependence among mortality dynamics of the examined populations. An efficient Bayesian Markov Chain Monte-Carlo method is also developed to estimate the unknown parameters to address the computational complexity. Our empirical application to the mortality data collected for the Group of Seven nations demonstrates the efficacy of our approach.

Genomic characterization and phylogenetic evolution of the canine parvoviruses in Iranian dogs, a nationwide study: CPV evolutionary analysis in Iran

Canine Parvo Virus 2 (CPV-2) culminated in lots of fatalities in domestic dogs since its emergence in 1978. Mainly, it is responsible for severe hemorrhagic diarrhea, vomiting, and dehydration. CPV-2 has three main variants known as 2a, 2b, and 2c. Due to the necessity of monitoring the evolutionary parameters of the virus, and also the lack of comprehensive study of CPV2 in Iran, this study is done for the first time in this country not only to characterize Iranian CPV genomes but also to study the evolutionary parameters and phylodynamics of CPV. The phylogenetic trees were constructed using the Maximum Likelihood (ML) method. By the use of the Bayesian Monte Carlo Markov Chain (BMCMC) method, evolutionary analysis and phylodynamics of the virus were investigated. Phylogenetic results showed that all Iranian isolates were classified in the CPV-2a variant. The central part of Iran was suggested to be the origin of the virus, especially the Alborz province. Before its prevalence throughout the country, the virus circulated in the central part, in Thran, Karaj, and Qom. Mutational analysis showed a positive selection pressure of CPV-2a. Investigating the evolutionary parameters of the virus proposed 1970 to be the date of birth of the virus, with a 95% credible interval between 1953 and 1987. The effective number of infections increased dramatically from 2012 to 2015, then faced a slightly decreasing trend from 2015 to 2019. A considerable up warding pattern was witnessed from the middle of 2019, which can be taken as a concern about the risk of vaccination failure.

Probabilistic Models for the Shear Strength of RC Deep Beams

A new shear strength determination of reinforced concrete (RC) deep beams was proposed by using a statistical approach. The Bayesian–MCMC (Markov Chain Monte Carlo) method was introduced to establish a new shear prediction model and to improve seven existing deterministic models with a database of 645 experimental data. The bias correction terms of deterministic models were described by key explanatory terms identified by a systematic removal process. Considering multi-parameters, the Gibbs sampling was used to solve the high dimensional integration problem and to determine optimum and reliable model parameters with 50,000 iterations for probabilistic models. The model continuity and uncertainty for key parameters were quantified by the partial factor that was investigated by comparing test and model results. The partial factor for the proposed model was 1.25. The proposed model showed improved accuracy and continuity with the mean and coefficient of variation (CoV) of the experimental-to-predicted results ratio as 1.0357 and 0.2312, respectively.

Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.

An appraisal of gene targets for phylogenetic classification of canine distemper virus: Is the hemagglutinin the best candidate?

Sequence analysis of the canine distemper virus (CDV) hemagglutinin (H) gene may provide important insights on virus-host interactions and has also been frequently used for CDV phylogenetic classification. Herein, we performed an in silico analysis of CDV complete genomes (CGs) available in GenBank in order to investigate the suitability of H for CDV classification into lineages/genotypes. In addition, we analyzed the other viral genes for their potential use in CDV classification. Initially, we collected 116 CDV CGs from GenBank and compared their phylogenetic classification with that of their respective H nucleotide (nt) and amino acid (aa) sequences. Subsequently, we calculated the geodesic distance between the CG and H phylogenetic trees. These analyses were later performed with other CDV genes. All CDV CGs were also evaluated for possible recombination events. Nucleotide and aa analyses of H misclassified some Vaccine/America 1/Asia 3 lineage sequences compared to CG analysis, finding supported by both Maximum Likelihood (ML) and Bayesian Markov Chain Monte Carlo (B-MCMC) methods. Moreover, aa-based H analysis showed additional disagreements with the classification obtained by CG. The geodesic distance between the H and CG trees was 0.0680. Strong recombination signals were identified in the H gene, including Vaccine/America 1/Asia 3 lineage sequences. In contrast, C and P were the only genes that fully reproduced the CG classification (by ML and/or B-MCMC) and that did not show strong recombination signals. Furthermore, the P phylogenetic tree showed the lowest geodesic distance from the CG tree (0.0369). These findings suggest C and P as potential targets for CDV phylogenetic classification, especially when full genome sequencing is not possible. Finally, since our results were obtained considering the CDV CGs available to date, future analyses performed as more CDV sequences become available will be useful to assess probable issues of H-based phylogeny and to consolidate the suitability of the C and P genes for CDV classification.

DIGS: deep inference of galaxy spectra with neural posterior estimation

With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 106 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 105 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

New Constraint on the Tensor-to-scalar Ratio from the Planck and BICEP/Keck Array Data Using the Profile Likelihood

Motivated by the discrepancy between Bayesian and frequentist upper limits on the tensor-to-scalar ratio parameter r found by the SPIDER collaboration, we investigate whether a similar trend is also present in the latest Planck and BICEP/Keck Array data. We derive a new upper bound on r using the frequentist profile likelihood method. We vary all the relevant cosmological parameters of the ΛCDM model, as well as the nuisance parameters. Unlike the Bayesian analysis using Markov Chain Monte Carlo (MCMC), our analysis is independent of the choice of priors. Using Planck Public Release 4, BICEP/Keck Array 2018, Planck cosmic microwave background lensing, and baryon acoustic oscillation data, we find an upper limit of r < 0.037 at 95% Confidence Level (C.L.), similar to the Bayesian MCMC result of r < 0.038 for a flat prior on r and a conditioned Planck lowlEB covariance matrix.

New Surveillance Metrics for Alerting Community-Acquired Outbreaks of Emerging SARS-CoV-2 Variants Using Imported Case Data: Bayesian Markov Chain Monte Carlo Approach.

Global transmission from imported cases to domestic cluster infections is often the origin of local community-acquired outbreaks when facing emerging SARS-CoV-2 variants. We aimed to develop new surveillance metrics for alerting emerging community-acquired outbreaks arising from new strains by monitoring the risk of small domestic cluster infections originating from few imported cases of emerging variants. We used Taiwanese COVID-19 weekly data on imported cases, domestic cluster infections, and community-acquired outbreaks. The study period included the D614G strain in February 2020, the Alpha and Delta variants of concern (VOCs) in 2021, and the Omicron BA.1 and BA.2 VOCs in April 2022. The number of cases arising from domestic cluster infection caused by imported cases (Dci/Imc) per week was used as the SARS-CoV-2 strain-dependent surveillance metric for alerting local community-acquired outbreaks. Its upper 95% credible interval was used as the alert threshold for guiding the rapid preparedness of containment measures, including nonpharmaceutical interventions (NPIs), testing, and vaccination. The 2 metrics were estimated by using the Bayesian Monte Carlo Markov Chain method underpinning the directed acyclic graphic diagram constructed by the extra-Poisson (random-effect) regression model. The proposed model was also used to assess the most likely week lag of imported cases prior to the current week of domestic cluster infections. A 1-week lag of imported cases prior to the current week of domestic cluster infections was considered optimal. Both metrics of Dci/Imc and the alert threshold varied with SARS-CoV-2 variants and available containment measures. The estimates were 9.54% and 12.59%, respectively, for D614G and increased to 14.14% and 25.10%, respectively, for the Alpha VOC when only NPIs and testing were available. The corresponding figures were 10.01% and 13.32% for the Delta VOC, but reduced to 4.29% and 5.19% for the Omicron VOC when NPIs, testing, and vaccination were available. The rapid preparedness of containment measures guided by the estimated metrics accounted for the lack of community-acquired outbreaks during the D614G period, the early Alpha VOC period, the Delta VOC period, and the Omicron VOC period between BA.1 and BA.2. In contrast, community-acquired outbreaks of the Alpha VOC in mid-May 2021, Omicron BA.1 VOC in January 2022, and Omicron BA.2 VOC from April 2022 onwards, were indicative of the failure to prepare containment measures guided by the alert threshold. We developed new surveillance metrics for estimating the risk of domestic cluster infections with increasing imported cases and its alert threshold for community-acquired infections varying with emerging SARS-CoV-2 strains and the availability of containment measures. The use of new surveillance metrics is important in the rapid preparedness of containment measures for averting large-scale community-acquired outbreaks arising from emerging imported SARS-CoV-2 variants.

Robust Method for Reservoir Simulation History Matching Using Bayesian Inversion and Long-Short-Term Memory Network-Based Proxy

Summary History matching is a critical process used for calibrating simulation models and assessing subsurface uncertainties. This common technique aims to align the reservoir models with the observed data. However, achieving this goal is often challenging due to the nonuniqueness of the solution, underlying subsurface uncertainties, and usually the high computational cost of simulations. The traditional approach is often based on trial and error, which is exhaustive and labor-intensive. Some analytical and numerical proxies combined with Monte Carlo simulations are used to reduce the computational time. However, these approaches suffer from low accuracy and may not fully capture subsurface uncertainties. This study proposes a new robust method using Bayesian Markov chain Monte Carlo (MCMC) to perform assisted history matching under uncertainties. We propose a novel three-step workflow that includes (1) multiresolution low-fidelity models to guarantee high-quality matching; (2) long-short-term memory (LSTM) network as a low-fidelity model to reproduce continuous time response based on the simulation model, combined with Bayesian optimization to obtain the optimum low-fidelity model; and (3) Bayesian MCMC runs to obtain the Bayesian inversion of the uncertainty parameters. We perform sensitivity analysis on the LSTM’s architecture, hyperparameters, training set, number of chains, and chain length to obtain the optimum setup for Bayesian-LSTM history matching. We also compare the performance of predicting the recovery factor (RF) using different surrogate methods, including polynomial chaos expansions (PCE), kriging, and support vector machines for regression (SVR). We demonstrate the proposed method using a water flooding problem for the upper Tarbert formation of the 10th SPE comparative model. This study case represents a highly heterogeneous nearshore environment. Results showed that the Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow ranges of uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and accuracy of the workflow. This approach provides an efficient and practical history-matching method for reservoir simulation and subsurface flow modeling with significant uncertainties.

Molecular evolution and antigenic drift of type 3 iVDPVs excreted from a patient with immunodeficiency in Ningxia, China.

A 2.5-year-old pediatric patient with acute flaccid paralysis was diagnosed with primary immunodeficiency (PID) in Ningxia Province, China, in 2011. Twelve consecutive stool specimens were collected from the patient over a period of 10 months (18 February 2011 to 20 November 2011), and 12 immunodeficiency vaccine-derived poliovirus (iVDPV) strains (CHN15017-1 to CHN15017-12) were subsequently isolated. Nucleotide sequencing analysis of the plaque-purified iVDPVs revealed 2%-3.5% VP1-region differences from their parental Sabin 3 strain. Full-length genome sequencing showed they were all Sabin 3/Sabin 1 recombinants, sharing a common 2C-region crossover site, and the two key determinants of attenuation (U472C in the 5' untranslated region and T2493C in the VP1 region) had reverted. Temperature-sensitive experiments demonstrated that the first two iVDPV strains partially retained the temperature-sensitive phenotype's nature, while the subsequent ten iVDPV strains distinctly lost it, possibly associated with increased neurovirulence. Nineteen amino-acid substitutions were detected between 12 iVDPVs and the parental Sabin strain, of which only one (K1419R) was found on the subsequent 10 iVDPV isolates, suggesting this site's potential as a temperature-sensitive determination site. A Bayesian Monte Carlo Markov Chain phylogenetic analysis based on the P1 coding region yielded a mean iVDPV evolutionary rate of 1.02 × 10-2 total substitutions/site/year, and the initial oral-polio-vaccine dose was presumably administered around June 2009. Our findings provide valuable information regarding the genetic structure, high-temperature growth sensitivity, and antigenic properties of iVDPVs following long-term evolution in a single PID patient, thus augmenting the currently limited knowledge regarding the dynamic changes and evolutionary pathway of iVDPV populations with PID during long-term global replication.