A Roadway Safety Sustainable Approach: Modeling for Real-Time Traffic Crash with Limited Data and Its Reliability Verification

With the development of freeway system informatization, it is easier to obtain the traffic flow data of freeway, which are widely used to study the relationship between traffic flow state and traffic safety. However, as the development degree of the freeway system is different in different regions, the sample size of traffic data collected in some regions is insufficient, and the precision of data is relatively low. In order to study the influence of limited data on the real-time freeway traffic crash risk modeling, three data sets including high precision data, small sample data, and low precision data were considered. Firstly, Bayesian Logistic regression was used to identify and predict the risk of three data sets. Secondly, based on the Bayesian updating method, the migration test towards high and low precision data sets was established. Finally, the applicability of machine learning and statistical methods to low precision data set was compared. The results show that the prediction performance of Bayesian Logistic regression improves with the increasing of sample size. Bayesian Logistic regression can identify various significant risk factors when data sets are of different precision. Comparatively, the prediction performance of the support vector machine is better than that of Bayesian Logistic. In addition, Bayesian updating method can improve the prediction performance of the transplanted model.

TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome

Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host–gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host–microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods.

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

Introduction The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. Objectives Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient’s infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). Methods High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. Results The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74–0.91) and 0.76 (CI 0.67–0.86). Conclusion Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Bayesian Analysis of Trends in Utilization of Maternal Healthcare Services in Pakistan during 2006-2018

Objectives. This study is aimed at investigating the time trends and disparities in access to maternal healthcare in Pakistan using Bayesian models. Study Design. A longitudinal study from 2006 to 2018. Methods. The detailed analysis is based on the data from Pakistan Demographic and Health Survey (PDHS) conducted during 2006-2018. We have proposed Bayesian logistic regression models (BLRM) to investigate the trends of maternal healthcare in the country. Based on different goodness-of-fit criteria, the performance of proposed models has also been compared with repeatedly used classical logistic regression models (CLRM). Results. The results from the analysis suggested that BLRM perform better than CLRM. The access to antenatal healthcare increased from 61% to 86% during years 2006-18. The utilization of medication also improved from 44% in 2006 to 60% in 2018. Despite the improvements from 2006 to 2018, every three out of ten women were not protected against neonatal tetanus, neither delivered in the health facility place nor availed with the skilled health provider at the time of delivery during 2018. Similarly, two-fifth mothers did not received any skilled postnatal checkup within two days after delivery. Additionally, the likelihood of MHS provided to mothers is in favor of mothers with lower ages, lower birth orders, urban residences, higher education, higher wealth quintiles, and residents of Sindh and Punjab. Conclusions. The gaps in utilization of MHS in different socioeconomic groups of the society have not decreased significantly during 2006-2018. Any future maternal health initiative in the country should focus to reduce the observed disparities among different socioeconomic sectors of the society.

Effectiveness of BNT162b2 and ChAdOx1 against SARS-CoV-2 household transmission - a prospective cohort study in England

Background: The ability of SARS-CoV-2 vaccines to protect against infection and onward transmission determines whether immunisation can control global circulation. We estimated effectiveness of BNT162b2 and ChAdOx1 vaccines against acquisition and transmission of the Alpha and Delta variants in a prospective household study in England. Methods: Adult index cases in the community and their household contacts took oral-nasal swabs on days 1, 3 and 7 after enrolment. Swabs were tested by RT-qPCR with genomic sequencing conducted on a subset. We used Bayesian logistic regression to infer vaccine effectiveness against acquisition and transmission, adjusted for age, vaccination history and variant. Findings: Between 2 February 2021 and 10 September 2021 213 index cases and 312 contacts were followed up. After excluding households lacking genomic proximity (N=2) or with unlikely serial intervals (N=16), 195 households with 278 contacts remained of whom 113 (41%) became PCR positive. Delta lineages had 1.64 times the risk (95% Credible Interval: 1.15-2.44) of transmission than Alpha; contacts older than 18 years were 1.19 times (1.04-1.52) more likely to acquire infection than children. Effectiveness of two doses of BNT162b2 against transmission of Delta was 31% (-3%, 61%) and 42% (14%, 69%) for ChAdOx1, similar to their effectiveness for Alpha. Protection against infection with Alpha was higher than for Delta, 71% (12%,95%) vs 24% (-2%, 64%) respectively for BNT162b2 and 26% (-39%, 73%) vs 14% (-5%, 46%) respectively for ChAdOx1. Interpretation: BNT162b2 and ChAdOx1 reduce transmission of the Delta variant from breakthrough infections in the household setting though their protection against infection is low. Funding: This study was funded by the UK Health Security Agency (formerly Public Health England) as part of the COVID-19 response.

A Bayesian Analysis of the Molnupiravir Trial Data

On October 1st 2021, Merck issued a press release claiming that "molnupiravir (MK-4482, EIDD-2801), an investigational oral antiviral medicine, significantly reduced the risk of hospitalization or death at a planned interim analysis of the Phase 3 MOVe-OUT trial in at risk, non-hospitalized adult patients with mild-to-moderate COVID-19." Specifically, 28/385 (7.3%) of patients who received molnupiravir were hospitalized or died, compared to 53/377 (14.1%) in the placebo control group. In order to quantify the evidence that molnupiravir is indeed effective, we conducted a Bayesian logistic regression. Our Bayesian analysis confirms that the molnupiravir data are indeed promising. A default analysis raises the probability for H+ from 0.50 to about 0.97 and is robust to a range of prior specifications. Nevertheless, the evidence is not so compelling as to rule out H0 almost entirely – its strength is the statistical equivalent of throwing two consecutive sixes with a fair die.

Profit-Driven Methodology for Servo Press Motion Selection under Material Variability

Servo presses enable new types of forming motion profiles that can be used to stamp difficult materials, such as high strength steels. This paper presents an application of Bayesian statistics to intelligently select which motion profile maximizes the expected utility given the properties of the incoming material. Bayesian logistic regression was used in conjunction with expected utility to estimate manufacturing returns, which can be used to make informed process decisions. A use case is presented, which demonstrates that the Smart Forming Algorithm can increase expected returns by more than 20%.

Designing and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality

Introduction Addiction consult services (ACS) engage hospitalized patients with opioid use disorder (OUD) in care and help meet their goals for substance use treatment. Little is known about how ACS affect mortality for patients with OUD. The objective of this study was to design and validate a model that estimates the impact of ACS care on 12-month mortality among hospitalized patients with OUD. Methods We developed a Markov model of referral to an ACS, post-discharge engagement in SUD care, and 12-month drug-related and non-drug related mortality among hospitalized patients with OUD. We populated our model using Oregon Medicaid data and validated it using international modeling standards. Results There were 6,654 patients with OUD hospitalized from April 2015 through December 2017. There were 114 (1.7%) drug-related deaths and 408 (6.1%) non-drug related deaths at 12 months. Bayesian logistic regression models estimated four percent (4%, 95% CI = 2%, 6%) of patients were referred to an ACS. Of those, 47% (95% CI = 37%, 57%) engaged in post-discharge OUD care, versus 20% not referred to an ACS (95% CI = 16%, 24%). The risk of drug-related death at 12 months among patients in post-discharge OUD care was 3% (95% CI = 0%, 7%) versus 6% not in care (95% CI = 2%, 10%). The risk of non-drug related death was 7% (95% CI = 1%, 13%) among patients in post-discharge OUD treatment, versus 9% not in care (95% CI = 5%, 13%). We validated our model by evaluating its predictive, external, internal, face and cross validity. Discussion Our novel Markov model reflects trajectories of care and survival for patients hospitalized with OUD. This model can be used to evaluate the impact of other clinical and policy changes to improve patient survival.

Computing Joint Action Costs: Co-Actors Minimize the Aggregate Individual Costs in an Action Sequence

Successful performance in cooperative activities relies on efficient task distribution between co-actors. Previous research found that people often forgo individual efficiency in favor of co-efficiency (i.e., joint-cost minimization) when planning a joint action. The present study investigated the cost computations underlying co-efficient decisions. We report a series of experiments that tested the hypothesis that people compute the joint costs of a cooperative action sequence by summing the individual action costs of their co-actor and themselves. We independently manipulated the parameters quantifying individual and joint action costs and tested their effects on decision-making by fitting and comparing Bayesian logistic regression models. Our hypothesis was confirmed: people weighed their own and their partner’s costs similarly to estimate the joint action costs as the sum of the two individual parameters. Participants minimized the aggregate cost to ensure co-efficiency. The results provide empirical support for behavioral economics and computational approaches that formalize cooperation as joint utility maximization based on a weighted sum of individual action costs.