A systematic review of the use of in-vehicle telematics in monitoring driving behaviours

BACKGROUND

Road traffic deaths are increasing globally, and preventable driving behaviours are a significant cause of these deaths. In-vehicle telematics has been seen as technology that can improve driving behaviour. The technology has been adopted by many insurance companies to track the behaviours of their consumers. This systematic review presents a summary of the ways that in-vehicle telematics has been modelled and analysed.

METHODOLOGY

Electronic searches were conducted on Scopus and Web of Science. Studies were only included if they had a sample size of 10 or more participants, collected their data over at least multiple days, and were published during or after 2010. 45 relevant papers were included in the review. 27 of these articles received a rating of "good" in the quality assessment.

RESULTS

We found a divide in the literature regarding the use of in-vehicle telematics. Some articles were interested in the utility of in-vehicle telematics for insurance purposes, while others were interested in determining the influence that in-vehicle telematics has on driving behaviour. Machine learning analyses were the most common forms of analysis seen throughout the review, being especially common in articles with insurance-based outcomes. Acceleration, braking, and speed were the most common variables identified in the review.

CONCLUSION

We recommend that future studies provide the demographical information of their sample so that the influence of in-vehicle telematics on the driving behaviours of different groups can be understood. It is also recommended that future studies use multi-level models to account for the hierarchical structure of the telematics data. This hierarchical structure refers to the individual trips for each driver.

Survival with primary lung cancer in Northern Ireland: 1991-1992

Lung cancer is a major cause of death in Western countries, but survival had never been studied in Northern Ireland (NI) on a population basis prior to this study.

AIMS

The primary aims were to describe the survival of patients with primary lung cancer, evaluate the effect of treatment, identify patient characteristics influencing survival and treatment and describe current trends in survival.

METHODS

A population-based study identified all incident cases of primary lung cancer in NI during 1991-2 and followed them for 21 months. Their clinical notes were traced and relevant details abstracted. Survival status was monitored via the Registrar General's Office, and ascertainment is thought to be near-complete. Appropriate statistical methods were used to analyse the survival data.

RESULTS

Some 855 incident cases were studied. Their 1-year survival was 24.5% with a median survival time of 4.7 months. Surgical patients had the best 1-year survival, 76.8%; however, adjustment suggested that about half of the benefit could be attributed to case-mix factors. Factors influencing treatment allocation were also identified, and a screening test showed the discordance between 'model' and 'medic': 210 patients were misclassified. Finally, the current trend in 1-year survival observed in the Republic of Ireland was best in the British Isles.

CONCLUSIONS

Overall, survival remains poor. The better survival of surgical patients is due, in part, to their superior case-mix profiles. Survival with other therapies is less good suggesting that the criteria for treatment might be relaxed with advantage using a treatment model to aid decision-making.

The risk of mpox importation and subsequent outbreak potential in Chinese mainland: a retrospective statistical modelling study

BACKGROUND

The 2022-2023 mpox (monkeypox) outbreak has spread rapidly across multiple countries in the non-endemic region, mainly among men who have sex with men (MSM). In this study, we aimed to evaluate mpox's importation risk, border screening effectiveness and the risk of local outbreak in Chinese mainland.

METHODS

We estimated the risk of mpox importation in Chinese mainland from April 14 to September 11, 2022 using the number of reported mpox cases during this multi-country outbreak from Global.health and the international air-travel data from Official Aviation Guide. We constructed a probabilistic model to simulate the effectiveness of a border screening scenario during the mpox outbreak and a hypothetical scenario with less stringent quarantine requirement. And we further evaluated the mpox outbreak potential given that undetected mpox infections were introduced into men who have sex with men, considering different transmissibility, population immunity and population activity.

RESULTS

We found that the reduced international air-travel volume and stringent border entry policy decreased about 94% and 69% mpox importations respectively. Under the quarantine policy, 15-19% of imported infections would remain undetected. Once a case of mpox is introduced into active MSM population with almost no population immunity, the risk of triggering local transmission is estimated at 42%, and would rise to > 95% with over six cases.

CONCLUSIONS

Our study demonstrates that the reduced international air-travel volume and stringent border entry policy during the COVID-19 pandemic reduced mpox importations prominently. However, the risk could be substantially higher with the recovery of air-travel volume to pre-pandemic level. Mpox could emerge as a public health threat for Chinese mainland given its large MSM community.

Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data

BACKGROUND

Nowadays, the chance of discovering the best antibody candidates for predicting clinical malaria has notably increased due to the availability of multi-sera data. The analysis of these data is typically divided into a feature selection phase followed by a predictive one where several models are constructed for predicting the outcome of interest. A key question in the analysis is to determine which antibodies  should be included in the predictive stage and whether they should be included in the original or a transformed scale (i.e. binary/dichotomized).

METHODS

To answer this question, we developed three approaches for antibody selection in the context of predicting clinical malaria: (i) a basic and simple approach based on selecting antibodies via the nonparametric Mann-Whitney-Wilcoxon test; (ii) an optimal dychotomizationdichotomization approach where each antibody was selected according to the optimal cut-off via maximization of the chi-squared (χ2) statistic for two-way tables; (iii) a hybrid parametric/non-parametric approach that integrates Box-Cox transformation followed by a t-test, together with the use of finite mixture models and the Mann-Whitney-Wilcoxon test as a last resort. We illustrated the application of these three approaches with published serological data of 36 Plasmodium falciparum antigens for predicting clinical malaria in 121 Kenyan children. The predictive analysis was based on a Super Learner where predictions from multiple classifiers including the Random Forest were pooled together.

RESULTS

Our results led to almost similar areas under the Receiver Operating Characteristic curves of 0.72 (95% CI = [0.62, 0.82]), 0.80 (95% CI = [0.71, 0.89]), 0.79 (95% CI = [0.7, 0.88]) for the simple, dichotomization and hybrid approaches, respectively. These approaches were based on 6, 20, and 16 antibodies, respectively.

CONCLUSIONS

The three feature selection strategies provided a better predictive performance of the outcome when compared to the previous results relying on Random Forest including all the 36 antibodies (AUC = 0.68, 95% CI = [0.57;0.79]). Given the similar predictive performance, we recommended that the three strategies should be used in conjunction in the same data set and selected according to their complexity.

Prognostic models: What the statistician wants the clinician to know

Prognostic model building is a process that begins much earlier than data analysis and ends later than when a model is reached. It requires careful delineation of a clinical question, methodical planning of the approach and attentive exploration of the data before attempting model building. Once following these important initial steps, the researcher may postulate a model to describe the process of interest and build such model. Once built, the model will need to be checked, validated and the exercise may take the researcher back a few steps - for instance, to adapt the model to fit a variable that displays a 'curved' pattern - to then return to check and validate the model again. To interpret and report the results it is vital to relate the output to the original question, to be transparent in the methodology followed and to understand the limitations of the data and the approach.

Selective adsorption of organic dyes from aqueous environment using fermented maize extract-enhanced graphene oxide-durian shell derived activated carbon composite

A secure aquatic environment is essential for both aquatic and terrestrial life. However, rising populations and the industrial revolution have had a significant impact on the quality of the water environment. Despite the implementation of strong and adapted environmental policies for water treatment worldwide, the issue of organic dyes in wastewater remains challenging. Thus, this study aimed to develop an efficient, cost-effective, and sustainable material to treat methylene blue (MB) in an aqueous environment. In this research, maize extract solution (MES) was utilized as a green cross-linker to induce precipitation, conjugation, and enhance the adsorption performance of graphene oxide (GO) cross-linked with durian shell activated carbon (DSAC), resulting in the formation of a GO@DSAC composite. The composite was investigated for its adsorptive performance toward MB in aqueous media. The physicochemical characterization demonstrated that the cross-linking method significantly influenced the porous structure and surface chemistry of GO@DSAC. BET analysis revealed that the GO@DSAC exhibited dominant mesopores with a surface area of 803.67 m2/g. EDX and XPS measurements confirmed the successful cross-linking of GO with DSAC. The adsorption experiments were well described by the Harkin-Jura model and they followed pseudo-second order kinetics. The maximum adsorption capacity reached 666.67 mg/g at 318 K. Thermodynamic evaluation indicated a spontaneous, feasible, and endothermic in nature. Regenerability and reusability investigations demonstrated that the GO@DSAC composite could be reused for up to 10 desorption-adsorption cycles with a removal efficiency of 81.78%. The selective adsorptive performance of GO@DSAC was examined in a binary system containing Rhodamine B (RhB) and methylene orange (MO). The results showed a separation efficiency (α) of 98.89% for MB/MO and 93.66% for MB/RhB mixtures, underscoring outstanding separation capabilities of the GO@DSAC composite. Overall, the GO@DSAC composite displayed promising potential for the effective removal of cationic dyes from wastewater.

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review

BACKGROUND AND OBJECTIVES

When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model.

METHODS

We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020.

RESULTS

In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%).

CONCLUSION

Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model.

Modeling vaccination coverage during the 2022 central Ohio measles outbreak: a cross-sectional study

Background

Of the eight large (>50 cases) US postelimination outbreaks, the first and last occurred in Ohio. Ohio's vaccination registry is incomplete. Community-level immunity gaps threaten more than two decades of measles elimination in the US. We developed a statistical model, VaxEstim, to rapidly estimate the early-phase vaccination coverage and immunity gap in the exposed population during the 2022 Central Ohio outbreak.

Methods

We used reconstructed daily incidence (from publicly available data) and assumptions about the distribution of the serial interval, or the time between symptom onset in successive measles cases, to estimate the effective reproduction number (i.e., the average number of secondary infections caused by an infected individual in a partially immune population). We estimated early-phase measles vaccination coverage by comparing the effective reproduction number to the basic reproduction number (i.e., the average number of secondary infections caused by an infected individual in a fully susceptible population) while accounting for vaccine effectiveness. Finally, we estimated the early-phase immunity gap as the difference between the estimated critical vaccination threshold and vaccination coverage.

Findings

VaxEstim estimated the early-phase vaccination coverage as 53% (95% credible interval, 21%-77%), the critical vaccination threshold as 93%, and the immunity gap as 42% (95% credible interval, 18%-74%).

Interpretation

This study estimates a significant immunity gap in the exposed population during the early phase of the 2022 Central Ohio measles outbreak, suggesting a robust public health response is needed to identify the susceptible community and develop community-specific strategies to close the immunity gap.

Funding

This work was supported in part by the National Institute of General Medical Sciences, National Institutes of Health; the UK Medical Research Council (MRC); the Foreign, Commonwealth and Development Office; the National Institute for Health Research (NIHR) Health Protection Research Unit in Modelling Methodology; Imperial College London, and the London School of Hygiene & Tropical Medicine, Community Jameel; the EDCTP2 programme, supported by the EU; and the Sergei Brin Foundation.

Towards a holistic paradigm for long-term snow avalanche risk assessment and mitigation

In mountain territories, snow avalanches are a prevalent threat. Long-term risk management involves defining meaningful compromises between protection and overall sustainability of communities and their environment. Methods able to (i) consider all sources of losses, (ii) account for the high uncertainty levels that affect all components of the risk and (iii) cope for marked non-stationarities should be employed. Yet, on the basis of a literature review and an analysis of relations to Sustainable Development Goals (SDGs), it is established that snow avalanche risk assessment and mitigation remain dominated by approaches that can be summed up as deterministic, hazard oriented, stationary and not holistic enough. A more comprehensive paradigm relying on formal statistical modelling is then proposed and first ideas to put it to work are formulated. Application to different mountain environments and broader risk problems is discussed.

Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiology

Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles' effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.

Quantifying Spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach

Harmful algal blooms (HABs) intoxicate and asphyxiate marine life, causing devastating environmental and socio-economic impacts, costing at least $8bn/yr globally. Accumulation of phycotoxins from HAB phytoplankton in filter-feeding shellfish can poison human consumers, prompting harvesting closures at shellfish production sites. To quantify long-term intoxication risk from Dinophysis HAB species, we used historical HAB monitoring data (2009-2020) to develop a new modelling approach to predict Dinophysis toxin concentrations in a range of bivalve shellfish species at shellfish sites in Western Scotland, South-West England and Northern France. A spatiotemporal statistical modelling framework was developed within the Generalized Additive Model (GAM) framework to quantify long-term HAB risks for different bivalve shellfish species across each region, capturing seasonal variations, and spatiotemporal interactions. In all regions spatial functions were most important for predicting seasonal HAB risk, offering the potential to inform optimal siting of new shellfish operations and safe harvesting periods for businesses. A 10-fold cross-validation experiment was carried out for each region, to test the models' ability to predict toxin risk at harvesting locations for which data were withheld from the model. Performance was assessed by comparing ranked predicted and observed mean toxin levels at each site within each region: the correlation of ranks was 0.78 for Northern France, 0.64 for Western Scotland, and 0.34 for South-West England, indicating our approach has promise for predicting unknown HAB risk, depending on the region and suitability of training data.

Modeling road traffic fatalities in Iran's six most populous provinces, 2015-2016

BACKGROUND

Prevention of road traffic injuries (RTIs) as a critical public health issue requires coordinated efforts. We aimed to model influential factors related to traffic safety.

METHODS

In this cross-sectional study, the information from 384,614 observations recorded in Integrated Road Traffic Injury Registry System (IRTIRS) in a one-year period (March 2015-March 2016) was analyzed. All registered crashes from Tehran, Isfan, Fras, Razavi Khorasan, Khuzestan, and East Azerbaijan provinces, the six most populated provinces in Iran, were included in this study. The variables significantly associated with road traffic fatality in the uni-variate analysis were included in the multiple logistic regression.

RESULTS

According to the multiple logistic regression, thirty-two out of seventy-one different variables were identified to be significantly associated with road traffic fatality. The results showed that the crash scene significantly related factors were passenger presence(OR = 4.95, 95%CI = (4.54-5.40)), pedestrians presence(OR = 2.60, 95%CI = (1.75-3.86)), night-time crashes (OR = 1.64, 95%CI = (1.52-1.76)), rainy weather (OR = 1.32, 95%CI = (1.06-1.64)), no intersection control (OR = 1.40, 95%CI = (1.29-1.51)), double solid line(OR = 2.21, 95%CI = (1.31-3.74)), asphalt roads(OR = 1.95, 95%CI = (1.39-2.73)), nonresidential areas(OR = 2.15, 95%CI = (1.93-2.40)), vulnerable-user presence(OR = 1.70, 95%CI = (1.50-1.92)), human factor (OR = 1.13, 95%CI = (1.03-1.23)), multiple first causes (OR = 2.81, 95%CI = (2.04-3.87)), fatigue as prior cause(OR = 1.48, 95%CI = (1.27-1.72)), irregulation as direct cause(OR = 1.35, 95%CI = (1.20-1.51)), head-on collision(OR = 3.35, 95%CI = (2.85-3.93)), tourist destination(OR = 1.95, 95%CI = (1.69-2.24)), suburban areas(OR = 3.26, 95%CI = (2.65-4.01)), expressway(OR = 1.84, 95%CI = (1.59-2.13)), unpaved shoulders(OR = 1.84, 95%CI = (1.63-2.07)), unseparated roads (OR = 1.40, 95%CI = (1.26-1.56)), multiple road defects(OR = 2.00, 95%CI = (1.67-2.39)). In addition, the vehicle-connected factors were heavy vehicle (OR = 1.40, 95%CI = (1.26-1.56)), dark color (OR = 1.26, 95%CI = (1.17-1.35)), old vehicle(OR = 1.46, 95%CI = (1.27-1.67)), not personal-regional plaques(OR = 2.73, 95%CI = (2.42-3.08)), illegal maneuver(OR = 3.84, 95%CI = (2.72-5.43)). And, driver related factors were non-academic education (OR = 1.58, 95%CI = (1.33-1.88)), low income(OR = 2.48, 95%CI = (1.95-3.15)), old age (OR = 1.67, 95%CI = (1.44-1.94)), unlicensed driving(OR = 3.93, 95%CI = (2.51-6.15)), not-wearing seat belt (OR = 1.55, 95%CI = (1.44-1.67)), unconsciousness (OR = 1.67, 95%CI = (1.44-1.94)), driver misconduct(OR = 2.51, 95%CI = (2.29-2.76)).

CONCLUSION

This study reveals that driving behavior, infrastructure design, and geometric road factors must be considered to avoid fatal crashes. Our results found that the above-mentioned factors had higher odds of a deadly outcome than their counterparts. Generally, addressing risk factors and considering the odds ratios would be beneficial for policy makers and road safety stakeholders to provide support for compulsory interventions to reduce the severity of RTIs.

Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia

BACKGROUND

A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients.

METHODS

This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers.

FINDINGS

Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses.

INTERPRETATION

SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated.

FUNDING

This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).

Post-hydrolysis ammonia stripping as a new approach to enhance the two-stage anaerobic digestion of poultry manure: Optimization and statistical modelling

Post-hydrolysis ammonia stripping was investigated as a new approach to enhance the methane potential of high ammonia substrates, such as poultry manure. The objective of the proposed approach is to address some of the noticeable disadvantages in the existing ammonia-stripping techniques i.e., treatment of raw samples and side-stream stripping. Poultry manure (PM) and a co-substrate (mixed wastes from a cheese factory and a coffee house, referred to as MS) characterized by a high carbon-to-nitrogen ratio were mixed at five different ratios: PM:MS of 100:0, 75:25, 50:50, 25:75, and 0:100. Samples were hydrolyzed for six days to promote ammonia conversion from organic nitrogen and then the samples with higher ammonia levels (>2000 mg NH₃-N/L) were stripped with air at initial pH values of 9 and 10 and temperatures of 40 and 55 °C. Biochemical methane potential (BMP) test results showed that post-hydrolysis ammonia stripping had alleviated ammonia inhibition and improved methane potential up to 200% when compared with untreated samples. The ammonia removal efficiency was mostly affected by pH. On the other hand, methane potential was highest in the samples treated at a higher temperature as their biodegradability was enhanced when compared with the samples treated at lower temperatures. Post-BMP characterization showed that the proposed approach had also limited the increase of ammonia in the digestate which ensured proper growth of methanogenic microorganisms.

Route choice estimation in rail transit systems using smart card data: handling vehicle schedule and walking time uncertainties

Several cities around the world rely on urban rail transit systems composed of interconnected lines, serving massive numbers of passengers on a daily basis. Accessing the location of passengers is essential to ensure the efficient and safe operation and planning of these systems. However, passenger route choices between origin and destination pairs are variable, depending on the subjective perception of travel and waiting times, required transfers, convenience factors, and on-site vehicle arrivals. This work proposes a robust methodology to estimate passenger route choices based only on automated fare collection data, i.e. without privacy-invasive sensors and monitoring devices. Unlike previous approaches, our method does not require precise train timetable information or prior route choice models, and is robust to unforeseen operational events like malfunctions and delays. Train arrival times are inferred from passenger volume spikes at the exit gates, and the likelihood of eligible routes per passenger estimated based on the alignment between vehicle location and the passenger timings of entrance and exit. Applying this approach to automated fare collection data in Lisbon, we find that while in most cases passengers preferred the route with the least transfers, there were a significant number of cases where the shorter distance was preferred. Our findings are valuable for decision support among rail operators in various aspects such as passenger traffic bottleneck resolution, train allocation and scheduling, and placement of services.

Associations between ambient air pollution, meteorology, and daily hospital admissions for ischemic stroke: a time-stratified case-crossover study in Beijing

Air pollution and ischemic stroke (IS) are both vital factors affecting the health of Beijing citizens. This study aims at exploring the associations between air pollution, meteorology, and the hospital admission of IS (IS HA). Information on 476,659 IS inpatients in secondary and higher hospitals in Beijing from 2013 to 2018 were collected. A time-stratified case-crossover design with the generalized additive model and the distributed lag nonlinear model were used. In the single-pollutant models, an inter-quartile range increase in O_3, SO₂, CO, and NO₂ resulted in a significant highest increase in IS HA by 2.23% (95% CI: 1.56%, 2.90%), 1.53% (95% CI: 1.12%, 1.95%), 1.05% (95% CI: 0.70%, 1.40%), and 0.51% (95% CI: 0.24%, 0.79%) on the day of pollution, so did PM_2.5 and PM₁₀ by 1.13% (95% CI: 0.68%, 1.59%) and 1.19% (95% CI: 0.74%, 1.64%) at a lag of 0-5 days. There was a nonlinear relationship between meteorology and IS HA. In the multivariate model, the cumulative relative risks with a maximum lag time of 21 days of PM_2.5 and NO₂ were 1.11 (95% CI: 1.04, 1.19) and 0.88 (95% CI: 0.82, 0.94), while the effects of SO₂, O₃, and meteorology were insignificant. The findings suggested that particulate pollutants could increase the risk of IS, and the elderly were more sensitive to it, while the results of gaseous pollutants are still discordant. The control of air pollution and the protection of susceptible populations should receive higher attention from policymakers.

|tPRiors |: a tool for prior elicitation and obtaining posterior distributions of true disease prevalence

BACKGROUND

Tests have false positive or false negative results, which, if not properly accounted for, may provide misleading apparent prevalence estimates based on the observed rate of positive tests and not the true disease prevalence estimates. Methods to estimate the true prevalence of disease, adjusting for the sensitivity and the specificity of the diagnostic tests are available and can be applied, though, such procedures can be cumbersome to researchers with or without a solid statistical background. This manuscript introduces a web-based application that integrates statistical methods for Bayesian inference of true disease prevalence based on prior elicitation for the accuracy of the diagnostic tests. This tool allows practitioners to simultaneously analyse and visualize results while using interactive sliders and output prior/posterior plots.

METHODS - IMPLEMENTATION

Three methods for prevalence prior elicitation and four core families of Bayesian methods have been combined and incorporated in this web tool. |tPRiors| user interface has been developed with R and Shiny and may be freely accessed on-line.

RESULTS

|tPRiors| allows researchers to use preloaded data or upload their own datasets and perform analysis on either single or multiple population groups clusters, allowing, if needed, for excess zero prevalence. The final report is exported in raw parts either as.rdata or.png files and can be further analysed. We utilize a real multiple-population and a toy single-population dataset to demonstrate the robustness and capabilities of |tPRiors|.

CONCLUSIONS

We expect |tPRiors| to be helpful for researchers interested in true disease prevalence estimation and who are keen on accounting for prior information. |tPRiors| acts both as a statistical tool and a simplified step-by-step statistical framework that facilitates the use of complex Bayesian methods. The application of |tPRiors| is expected to aid standardization of practices in the field of Bayesian modelling on subject and multiple group-based true prevalence estimation.

Influenza-associated mortality in Australia, 2010 through 2019: High modelled estimates in 2017

INTRODUCTION

In Australia, the 2017 and 2019 influenza seasons were severe. High-dose or adjuvanted vaccines were introduced for ≥65 year-olds in 2018.

AIM

To compare influenza-associated mortality in 2017 and 2019 with the average for 2010-2019.

METHODS

We used time series modelling to obtain estimates of influenza-associated death rates for influenza A(H1N1)pdm09, A(H3N2) and B in Australia, in persons of all ages and <65, 65-74 and ≥75 years. Estimates were made for pneumonia and influenza (P&I, 2010-2018), respiratory (2010-2018), and all-cause outcomes (2010-2019).

RESULTS

During 2010 through 2018 (and 2019 for all-cause), influenza was estimated to be associated with an annual average of 2.1 (95% confidence interval (CI) 1.9, 2.4), 4.0 (95% CI 3.4, 4.6), and 11.6 (95% CI 8.4, 15.0) P&I, respiratory and all-cause deaths per 100,000 population, respectively. Influenza A(H1N1)pdm09 was estimated to be associated with less than one quarter of influenza-associated P&I and respiratory deaths, while A(H3N2) and B were each estimated to contribute approximately equally to the remaining influenza-associated deaths. In 2017, the respective rates were 7.8 (95% CI 7.1, 8.4), 12.3 (95% CI 10.9, 13.6) and 26.0 (95% CI 20.8, 32.0) per 100,000. In 2019, the all-cause estimate was 20.8 (95% CI 14.9, 26.7) per 100,000.

CONCLUSIONS

Seasonal influenza continues to be associated with substantial mortality in Australia, with at least double the average occurring in 2017. Age-specific monitoring of vaccine effectiveness is needed in Australia to understand higher mortality seasons.

The incidence of lung cancer in Northern Ireland: 1991-1992 : A comparative study

Introduction

Lung cancer is the leading cause of cancer deaths in many Western countries, but its incidence has never been studied in Northern Ireland.

Aims

Accordingly, the present study was mounted to determine, for the first time, the incidence of the condition in Northern Ireland and to compare the findings with other regions in the British Isles.

Methods

A notification study of the incidence of lung cancer (ICD 162) was conducted in Northern Ireland during 1991/1992. Notifications from 6 sources were computerised and linked. Incident cases were identified and analysed in relation to Age, Sex and Geographical region—Northern Ireland, England and Wales, Scotland and the Republic of Ireland.

Results

Some 900 incident cases of lung cancer were identified. The incidence rate per 100,000 population was found to be 57.04. Mortality underestimated incidence by 12.5%. (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05). The male to female incidence ratio was 2.1: 1, and this ratio was similar in other regions, except Scotland, where the ratio was 1.7:1. The null hypothesis of a common incidence distribution across regions was formally rejected. A variety of models were fitted and a model in which the log-odds on incidence was a quadratic function of age fitted most of the regional data.

Conclusions

Northern Ireland had the lowest incidence of lung cancer in the UK, but its overall rate was still 40% higher than that observed in the Republic of Ireland which had the lowest rate in the British Isles. Across regions, the pattern of incidence by age and sex was complicated, but a linear logistic model fitted all of the Irish data and the female data in Scotland, satisfactorily.

Real-time quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARS-CoV-2

BACKGROUND

The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 genomes, e.g., the D614G substitution, is one of the major challenges of disease control. Characterizing the role of the mutation activities is of importance to understand how the evolution of pathogen shapes the epidemiological outcomes at population scale.

METHODS

We developed a statistical framework to reconstruct variant-specific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihood-based approach, the model is exemplified with the COVID-19 surveillance data from January 1 to June 30, 2020 in California, USA. We explore the potential of this framework to generate early warning signals for detecting transmission advantage on a real-time basis.

RESULTS

The modelling framework in this study links together the mutation activity at molecular scale and COVID-19 transmissibility at population scale. We find a significant transmission advantage of COVID-19 associated with the D614G substitution, which increases the infectivity by 54% (95%CI: 36, 72). For the early alarming potentials, the analytical framework is demonstrated to detect this transmission advantage, before the mutation reaches dominance, on a real-time basis.

CONCLUSIONS

We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework.

Descriptive understanding and prediction in COVID-19 modelling

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding.

Data-driven flood hazard zonation of Italy

We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE in other areas. We expect the new data-driven inundation zonings to be useful where flood zonings built on hydrological modelling are not available, and to decide where improved flood hazard zoning is needed.

Shrinkage in serial intervals across transmission generations of COVID-19

One of the key epidemiological characteristics that shape the transmission of coronavirus disease 2019 (COVID-19) is the serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, recent studies reported a slight shrinkage (or contraction) of the mean of effective SI across transmission generations or over time. Here, we develop a likelihood-based statistical inference framework with truncation to explore the change in SI across transmission generations after adjusting the impacts of case isolation. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that for COVID-19, the mean of individual SI is likely to shrink with a factor at 0.72 per generation (95%CI: 0.54, 0.96) as the transmission generation increases, where a threshold may exist as the lower boundary of this shrinking process. We speculate that one of the probable explanations for the shrinkage in SI might be an outcome due to the competition among multiple candidate infectors within the same case cluster. Thus, the nonpharmaceutical interventive strategies are crucially important to block the transmission chains, and mitigate the COVID-19 epidemic.

Fluoride toxicity variably affects overall physiology and grain development in three contrasting rice genotypes, representing a potential biohazard

Ingestion of fluoride through consumption of contaminated food grains has been regarded to be hazardous for consumer health. The current study indicated the possible occurrence of such biohazard due to fluoride bioaccumulation in rice grains and straw (cattle feed). The effects of fluoride toxicity at three stages of grain development in three rice genotypes, viz., IR-64, Gobindobhog (aromatic), and Khitish, were also studied. Irrigation with fluoride-infested water inhibited grain formation in IR-64 and reduced grain yield in Gobindobhog. Fluoride toxicity promoted seed sterility in IR-64 by triggering reactive oxygen species (ROS) production and cellular necrosis, suppressing genes like GIF1, DEP1, and SPL14 (positively controlling seed formation) and inducing GW2 (negatively mediating grain development). Gobindobhog showed intermediate fluoride sensitivity and accumulated high levels of proline, anthocyanins, flavonoids, and phenolics due to the induction of genes like P5CS, ANS, and PAL in developing grains. The agronomic attributes in Khitish were unaffected by fluoride stress due to regulated fluoride uptake and high expression of GIF1, DEP1, and SPL14 along with an increased synthesis of anthocyanins, flavonoids, and phenolics. Khitish also accumulated low ROS as a result of which lowest lipoxygenase expression (among selected cultivars) was observed in developing grains. Fluoride entry was accelerated in the straw of Khitish, possibly due to the absence of regulated uptake mechanism in dead seedlings. Furthermore, the ecological concerns regarding fluoride bioaccumulation and reduced grain yield at the varietal level were also established, based on statistical modelling.

Multi-level longitudinal learning curve regression models integrated with item difficulty metrics for deliberate practice of visual diagnosis: groundwork for adaptive learning

Visual diagnosis of radiographs, histology and electrocardiograms lends itself to deliberate practice, facilitated by large online banks of cases. Which cases to supply to which learners in which order is still to be worked out, with there being considerable potential for adapting the learning. Advances in statistical modeling, based on an accumulating learning curve, offer methods for more effectively pairing learners with cases of known calibrations. Using demonstration radiograph and electrocardiogram datasets, the advantages of moving from traditional regression to multilevel methods for modeling growth in ability or performance are demonstrated, with a final step of integrating case-level item-response information based on diagnostic grouping. This produces more precise individual-level estimates that can eventually support learner adaptive case selection. The progressive increase in model sophistication is not simply statistical but rather brings the models into alignment with core learning principles including the importance of taking into account individual differences in baseline skill and learning rate as well as the differential interaction with cases of varying diagnosis and difficulty. The developed approach can thus give researchers and educators a better basis on which to anticipate learners' pathways and individually adapt their future learning.

Correlation of protection against varicella in a randomized Phase III varicella-containing vaccine efficacy trial in healthy infants

BACKGROUND

Varicella vaccination confers high and long-lasting protection against chickenpox and induces robust immune responses, but an absolute correlate of protection (CoP) against varicella has not been established. This study models the relationship between varicella humoral response and protection against varicella.

METHODS

This was a post-hoc analysis of data from a Phase IIIb, multicenter, randomized trial (NCT00226499) conducted in ten varicella-endemic European countries. Healthy children aged 12-22 months were randomized 3:3:1 to receive one dose of measles-mumps-rubella and one dose of varicella vaccine (one-dose group) or two doses of measles-mumps-rubella-varicella vaccine (two-dose group) or two doses of measles-mumps-rubella vaccine (control group) six weeks apart. The study remained observer-blind until completion, except in countries with obligatory additional immunizations. The objective was to correlate varicella-specific antibody concentrations with protection against varicella and probability of varicella breakthrough, using Cox proportional hazards and Dunning and accelerated failure time statistical models. The analysis was guided by the Prentice framework to explore a CoP against varicella.

RESULTS

The trial included 5803 participants, 5289 in the efficacy (2266: one-dose group, 2279: two-dose group and 744: control group) and 5235 (2248, 2245 and 742 in the same groups) in the immunogenicity cohort. The trial ended in 2016 with a median follow-up time of 9.8 years. Six weeks after vaccination with one- or two-dose varicella-containing vaccine, more than 93.0% of vaccinees were seropositive for varicella-specific antibodies. Estimated vaccine efficacy correlated positively with antibody concentrations. The fourth Prentice CoP criterion was not met, due to predicted positive vaccine efficacy in seronegative participants. Further modelling showed decreased probability of moderate to severe varicella breakthrough with increasing varicella-specific antibody concentrations (ten-year probability <0.1 for antibody concentrations ≥2-fold above the seropositivity cut-off).

CONCLUSIONS

Varicella-specific antibody concentrations are a good predictor of protection, given their inverse correlation with varicella occurrence.

CLINICAL TRIAL

NCT00226499.

Drug release from hydroxypropylcellulose gels cannot be statistically predicted from their viscometric and initial viscoelastic properties

This study questioned whether rheological properties can predict drug (metronidazole) release from Hydroxypropylcellulose (HPC) platforms. Viscometric and viscoelastic properties of aqueous, alcohols/diols and mixed solvent HPC solutions and gels were determined using viscometry and oscillatory analysis. Drug release was conducted at pH 7.4 under sink conditions. Relationships between rheological parameters and drug release were modelled using multiple linear stepwise regression. Viscometry identified ethanol and water as good solvents for HPC. Diol solvents were predicted to exhibit greater interactions with HPC (COSMO modelling) but possessed lowest intrinsic viscosities. Pentanediol or ethylene glycol prepared gels exhibited greatest elasticity. No relationships were observed between dilute solution properties and initial gel viscoelasticity. Drug release from HPC gels occurred via gel erosion and diffusion. No relationships were observed between initial gel viscoelasticity and drug release and thus, for gel platforms that undergo erosion in aqueous media, drug release cannot be predicted from initial gel viscoelasticity.

Exploring a model-based analysis of patient derived xenograft studies in oncology drug development

Purpose

To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs.

Methods

Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX).

Results

The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI.

Conclusions

The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.

Understanding flow properties of mannitol powder at a range of temperature and humidity

Inadequate flowability of powders in industries during handling can cause many problems. For example, lack of flow from hoppers, poor tablet weight consistency, and low production rate in tableting. Many factors are known to commonly affect flow properties of powders, such as temperature, humidity and conditioning duration. In this paper, flow properties of a mannitol powder, which was conditioned between 24 and 72 h at various high relative humidities and temperature, were measured using a shear tester. A statistical model was developed to investigate the relative importance of these variables on the mannitol flow properties. The developed model showed all independent variables are significant in estimating bulk cohesion. Two separate approaches were used to evaluate inter-particle forces in the bulk, and how these changed with environmental conditions. First, inter-particle forces were inferred from the measured bulk properties using the Rumpf model approach. Secondly, inter-particle forces were predicted based on a model of moisture present on the particle surface using a combination of Kelvin model with the Laplace-Young (KLY) equation. The second approach also involved a new method to measure surface energy of mannitol powder based on measurements using Finite Dilution Inverse Gas Chromatography (FD-IGC). The surface energies of the mannitol powder were measured at high temperature (35 °C) and at different range of relative humidities. In spite of the fundamentally different approaches to the two ways of inferring inter-particles forces, these forces came out within less than 1.5:1 in magnitude. The Rumpf approach from bulk behaviour data obviously reflected the measured change in behaviour with humidity in particular, but this was not predicted from the KLY approach, however the likely reasons for this are postulated and recommendations for improvement are made.

Assessment and statistical modelling of airborne microorganisms in Madrid

The limited evidence available suggests that the interaction between chemical pollutants and biological particles may intensify respiratory diseases caused by air pollution in urban areas. Unlike air pollutants, which are routinely measured, records of biotic component are scarce. While pollen concentrations are daily surveyed in most cities, data related to airborne bacteria or fungi are not usually available. This work presents the first effort to understand atmospheric pollution integrating both biotic and abiotic agents, trying to identify relationships among the Proteobacteria, Actinobacteria and Ascomycota phyla with palynological, meteorological and air quality variables using all biological historical records available in the Madrid Greater Region. The tools employed involve statistical hypothesis contrast tests such as Kruskal-Wallis and machine learning algorithms. A cluster analysis was performed to analyse which abiotic variables were able to separate the biotic variables into groups. Significant relationships were found for temperature and relative humidity. In addition, the relative abundance of the biological phyla studied was affected by PM₁₀ and O₃ ambient concentration. Preliminary Generalized Additive Models (GAMs) to predict the biotic relative abundances based on these atmospheric variables were developed. The results (r = 0.70) were acceptable taking into account the scarcity of the available data. These models can be used as an indication of the biotic composition when no measurements are available. They are also a good starting point to continue working in the development of more accurate models and to investigate causal relationships.

De novo RNA-Seq analysis in sensitive rice cultivar and comparative transcript profiling in contrasting genotypes reveal genetic biomarkers for fluoride-stress response

The fluoride-sensitive indica rice cultivar, IR-64 was subjected to NaF-treatment for 25 days, following which RNA-Seq analysis identified significant up and down regulation of 1,303 and 93 transcripts respectively. Gene ontology (GO) enrichment analysis classified transcripts into groups related to 'cellular part', 'membrane', 'catalytic activity', 'transporter activity', 'binding', 'metabolic processes' and 'cellular processes'. Analysis of differentially expressed genes (DEGs) revealed fluoride-mediated suppression of abscisic acid (ABA) biosynthesis and signaling. Instead, the gibberellin-dependent pathway and signaling via ABA-independent transcription factors (TFs) was activated. Comparative profiling of selected DEGs in IR-64 and fluoride-tolerant variety, Khitish revealed significant cytoskeletal and nucleosomal remodelling, accompanied with escalated levels of autophagy in stressed IR-64 (unlike that in stressed Khitish). Genes associated with ion, solute and xenobiotic transport were strongly up regulated in stressed IR-64, indicating potential fluoride entry through these channels. On the contrary, genes associated with xenobiotic mobility were suppressed in the tolerant cultivar, which restricted bioaccumulation and translocation of fluoride. Pairwise expression profile analysis between stressed IR-64 and Khitish, supported by extensive statistical modelling predicted that fluoride susceptibility was associated with high expression of genes like amino acid transporter, ABC transporter2, CLCd, MFS monosaccharide transporter, SulfT2.1 and PotT2 while fluoride tolerance with high expression of Sweet11.

Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data

This study estimates cumulative infection rates from Covid-19 in Great Britain by local authority districts (LADs) and council areas (CAs) and investigates spatial patterns in infection rates. We propose a model-based approach to calculate cumulative infection rates from data on observed and expected deaths from Covid-19. Our analysis of mortality data shows that 7% of people in Great Britain were infected by Covid-19 by the last third of June 2020. It is unlikely that the infection rate was lower than 4% or higher than 15%. Secondly, England had higher infection rates than Scotland and especially Wales, although the differences between countries were not large. Thirdly, we observed a substantial variation in virus infection rates in Great Britain by geographical units. Estimated infection rates were highest in the capital city of London where between 11 and 12% of the population might have been infected and also in other major urban regions, while the lowest were in small towns and rural areas. Finally, spatial regression analysis showed that the virus infection rates increased with the increasing population density of the area and the level of deprivation. The results suggest that people from lower socioeconomic groups in urban areas (including those with minority backgrounds) were most affected by the spread of coronavirus from March to June.

Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data

Background

Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs.

Methods

We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure.

Results

Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA’s error rates under almost all scenarios.

Conclusion

We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.

Risk factors for poor health and performance in European broiler production systems

BACKGROUND

Conventional broilers are currently one of the most efficient protein converters. Although decades of progress in genetic selection and feed formulation have lead to high standards of efficient broiler production, still a lot of variability is found between farms and between successive flocks. The aim of this study was to investigate risk- and/or protective factors for poor health and performance in conventional broiler-farms in Europe by developing eight multivariable linear mixed models. Three different models were used to investigate mortality (overall, first week, after first week), three models for performance variables (growth, feed conversion, European production index) and two models were related to slaughterhouse data (i.e. dead on arrival and condemnation rate).

RESULTS

Several factors related to management and housing were significantly associated with health and performance of broilers. The following factors were associated with increased mortality: floor quality, neonatal septicemia, ventilation type and other professional activities of the farmer. The factors associated with performance were chick sex, coccidiosis infections, necrotic enteritis, dysbacteriosis, light intensity adaptations, ventilation type, comparing daily flock results with previous flock results by farmer, daily check of feed and water system and type of feed. For dead on arrival three risk factors were identified i.e. daily growth, type of light adaptation and type of drinkers system. For condemnation rate seven risk factors were found, i.e. type of drinking system, daily growth, feed withdrawal time, type of ventilation, house size, septicemia after seven days and type of feed.

CONCLUSIONS

These results imply that a multifactorial approach is required with adaptations involving both improvements in management, housing, health programs and an increasing level of professionalism of the farmer in order to improve broiler performance and health.

The hidden Markov chain modelling of the COVID-19 spreading using Moroccan dataset

The World Health Organization (WHO) declared in March 12, 2020 the COVID-19 disease as pandemic. In Morocco, the first local transmission case was detected in March 13. The number of confirmed cases has gradually increased to reach 15,194 on July 10, 2020. To predict the COVID-19 evolution, statistical and mathematical models such as generalized logistic growth model [1], exponential model [2], segmented Poisson model [3], Susceptible-Infected-Recovered derivative models [4] and ARIMA [5] have been proposed and used. Herein, we proposed the use of the Hidden Markov Chain, which is a statistical system modelling transitions from one state (confirmed cases, recovered, active or death) to another according to a transition probability matrix to forecast the evolution of COVID-19 in Morocco from March 14, to October 5, 2020. In our knowledge the Hidden Markov Chain was not yet applied to the COVID-19 spreading. Forecasts for the cumulative number of confirmed, recovered, active and death cases can help the Moroccan authorities to set up adequate protocols for managing the post-confinement due to COVID-19. We provided both the recorded and forecasted data matrices of the cumulative number of the confirmed, recovered and active cases through the range of the studied dates.

Estimation of exponential growth rate and basic reproduction number of the coronavirus disease 2019 (COVID-19) in Africa

BACKGROUND

Since the first case of coronavirus disease 2019 (COVID-19) in Africa was detected on February 14, 2020, the cumulative confirmations reached 15 207 including 831 deaths by April 13, 2020. Africa has been described as one of the most vulnerable region with the COVID-19 infection during the initial phase of the outbreak, due to the fact that Africa is a great commercial partner of China and some other EU and American countries. Which result in large volume of travels by traders to the region more frequently and causing African countries face even bigger health threat during the COVID-19 pandemic. Furthermore, the fact that the control and management of COVID-19 pandemic rely heavily on a country's health care system, and on average Africa has poor health care system which make it more vulnerable indicating a need for timely intervention to curtail the spread. In this paper, we estimate the exponential growth rate and basic reproduction number (R0) of COVID-19 in Africa to show the potential of the virus to spread, and reveal the importance of sustaining stringent health measures to control the disease in Africa.

METHODS

We analyzed the initial phase of the epidemic of COVID-19 in Africa between 1 March and 13 April 2020, by using the simple exponential growth model. We examined the publicly available materials published by the WHO situation report to show the potential of COVID-19 to spread without sustaining strict health measures. The Poisson likelihood framework is adopted for data fitting and parameter estimation. We modelled the distribution of COVID-19 generation interval (GI) as Gamma distributions with a mean of 4.7 days and standard deviation of 2.9 days estimated from previous work, and compute the basic reproduction number.

RESULTS

We estimated the exponential growth rate as 0.22 per day (95% CI: 0.20-0.24), and the basic reproduction number, R0, as 2.37 (95% CI: 2.22-2.51) based on the assumption that the exponential growth starting from 1 March 2020. With an R0 at 2.37, we quantified the instantaneous transmissibility of the outbreak by the time-varying effective reproductive number to show the potential of COVID-19 to spread across African region.

CONCLUSIONS

The initial growth of COVID-19 cases in Africa was rapid and showed large variations across countries. Our estimates should be useful in preparedness planning against further spread of the COVID-19 epidemic in Africa.

Hand-eye coordination-based implicit re-calibration method for gaze tracking on ultrasound machines: a statistical approach

PURPOSE

Eye gaze tracking is proving to be beneficial in many biomedical applications. The performance of systems based on eye gaze tracking is very much dependent on how accurate their calibration is. It has been reported that the gaze tracking accuracy deteriorates cumulatively and significantly with usage time. This impedes the wide use of gaze tracking in user interfaces.

METHODS

Explicit re-calibration, typically requiring the user's active attention, is time-consuming and can interfere with the user's main activity. Therefore, we propose an implicit re-calibration method, which can rectify the deterioration of the gaze tracking accuracy without bringing about the user's deliberate attention. We make use of hand-eye coordination, with the reasonable assumption that the eye gaze follows the pointer during a selection task, to acquire additional calibration points during normal usage of a gaze-contingent system. We construct a statistical model for the calibration and the hand-eye coordination and apply the Gaussian process regression framework to perform the re-calibration.

RESULTS

To validate our model and method, we performed a user study on ultrasonography tasks on a gaze-contingent interface for ultrasound machines. Results suggest that our method can rectify the tracking accuracy deterioration for [Formula: see text] of all cases where deterioration occurs in our user study. With another benchmark dataset, our method can redress tracking accuracy to a level comparable to the initial calibration in more than [Formula: see text] of the cases.

CONCLUSIONS

Our implicit re-calibration method is a practical and convenient fix for tracking accuracy deterioration in gaze-contingent user interfaces, and in particular for gaze-contingent ultrasound machines.

The potential role of central obesity in male infertility: body mass index versus waist to hip ratio as they relate to selected semen parameters

Background

Little is known about the potential role of central obesity among men. Our first aim was to confirm what is already known from prior research, namely that both BMI and WHR are inversely associated with selected semen parameters. Our second aim was to examine the potential role of central obesity by assessing if there was a difference between BMI and WHR regarding their relationships to these selected semen parameters.

Methods

In this cross-sectional study between January 2011 to January 2018, we analyzed semen samples from 1169 patients who visited an andrology clinic in Budapest for infertility reasons. Variables assessed were: body measurements (height, weight, waist circumference, and hip circumference), and the results of semen analysis (sperm concentration, total sperm count, progressive sperm motility, and normal sperm morphology).

Results

The mean height and weight were 180.6 cm and 87.3 kg, respectively – the mean BMI was 26.8. The mean waist and hip circumferences were 100.9 cm and 94.8 cm, respectively – the mean waist to hip ratio was 0.94. The mean sperm concentration, total sperm count, and percents of progressive motility and normal morphology were 48.7 M/ml, 165 million, 21.2, and 4.8%, respectively. Both BMI and WHR were significant correlates in all semen parameter regression models. When comparing the parameter estimates for BMI with those for WHR for each semen parameter, the parameter estimate for WHR was significantly lower (indicating a stronger negative association) than that for BMI for progressive motility and total sperm count, but not for normal morphology or concentration.

Conclusions

Our study is the first to examine, using a large patient sample, the potential role of central obesity by comparing the difference between BMI and WHR as they relate to selected semen parameters. Our findings indicate a potential role of central obesity for progressive motility and total sperm count, but not for normal morphology and concentration. Despite the limitations and the exploratory nature of this study, we can conclude that our results point to a potential role of central obesity in male infertility, but this finding should be confirmed and further explored in future research.

Trial registration

The trial was retrospectively authorized after the data collection on September 24, 2018. Registration number: SE RKEB: 169/2018.

Flexible age-period-cohort modelling illustrated using obesity prevalence data

Background

Use of generalized linear models with continuous, non-linear functions for age, period and cohort makes it possible to estimate these effects so they are interpretable, reliable and easily displayed graphically. To demonstrate the methods we use data on the prevalence of obesity among Australian women from two independent data sources obtained using different study designs.

Methods

We used data from two long-running nationally representative studies: seven cross-sectional Australian National Health Surveys conducted between 1995 and 2017–18, each involving 6000–8000 women; and the Australian Longitudinal Study on Women’s Health which started in 1996 and involves more than 57,000 women in four age cohorts who are re-surveyed at three-yearly intervals or annually. Age-period-cohort analysis was conducted using generalized linear models with splines to describe non-linear continuous effects.

Results

When analysed in the same way both data sets showed similar patterns. Prevalence of obesity increased with age until late middle age and then declined; increased only slightly across surveys; but increased steadily with birth year until the 1960s and then accelerated.

Conclusions

The methods illustrated here make the estimation and visualisation of age, period and cohort effects accessible and interpretable. Regardless of how the data are collected (from repeated cross-sectional surveys or longitudinal cohort studies), it is clear that younger generations of Australian women are becoming heavier at younger ages. Analyses of trends in obesity should include cohort, in addition to age and period, effects in order to focus preventive strategies appropriately.

Statistical modelling of HER2-positivity in breast cancer: Final analyses from two large, multicentre, non-interventional studies in Germany

Background

The German NIU HER2 model was developed based on five variables found to have statistically significant influences on HER2-positivity, to allow exploration of deviations between model-predicted and actual HER2-positivity rates as a measure of testing quality. The prospective, non-interventional EPI HER2 BC study (NCT02666261) compared NIU and EPI data, aiming to validate the NIU model.

Methods

HER2 status and patient-/tumour-related information were collected from eligible patients with invasive breast cancer. The influence of variables on HER2-positivity was compared between studies and the NIU model validated using EPI data with cut-off and variable coefficients from the NIU study. The influences of additional variables, centre effects and laboratory-specific parameters were also explored.

Results

The study included 14,729 EPI and 15,281 NIU samples; HER2-positivity rates were comparable (13.5% versus 14.2%). The five covariates from NIU were shown to significantly affect HER2-positivity using EPI data. The Youden Index for the NIU model refitted to EPI data (0.3632) and the NIU model for prediction of HER2-positivity in EPI (0.3552) was close to that for the NIU model fitted to NIU data (0.3888), validating the NIU model. Replacing hormone receptor status with progesterone and oestrogen receptor expression, and adding method of sample extraction as a variable improved the model’s predictive strength (ROC AUC 0.7402; Youden Index 0.3935).

Conclusions

Reliable, high-quality HER2-testing methods are essential for selection of patients with HER2-positive breast cancer for HER2-tageted treatment. Integration of our model into a locally used software or website may improve its viability for use in clinical practice.

•

Five covariates significantly influenced HER2-positivity in the NIU study.

•

The same five covariates also influenced HER2-positivity in the EPI study.

•

The NIU HER2 model was successfully validated using the EPI study data.

•

Integration of novel variables into the EPI model improved the predictive strength.

The protection mechanism offered by Heterophragma adenophyllum extract against Fe-C steel dissolution at low pH: Computational, statistical and electrochemical investigations

An ethanolic extract of Heterophragma adenophyllum (HA) was investigated as a corrosion inhibitor for Fe-C steel in hydrochloric acid. The inhibition effect of the HA extract was examined using weight deterioration, electrochemical impedance spectroscopy (EIS) and polarization techniques. EIS showed enhanced charge transfer resistance with a maximum protection value of ~96% at 600 ppm concentration. Tafel extrapolation results revealed that corrosion was restricted by mixed inhibition. The i_corr values reduced considerably from 53.63 μA cm^-2 at 100 ppm to 20.11 μA cm^-2 at 600 ppm of HA extract. Interaction intensity was further examined through Langmuir, Freundlich, D-R, Frumkin, and Flory-Huggins adsorption isotherms. The computational study, statistical modelling and surface morphology by XPS, AFM, and SEM-EDX, indicated an excellent adsorption capability of HA on Fe-C steel.

Fewer ischemic strokes, despite an ageing population: stroke models from observed incidence in Norway 2010-2015

Background

Stroke incidence rates have fallen in high-income countries over the last several decades, but findings regarding the trend over recent years have been mixed. The aim of the study was to describe and model temporal trends in incidence of stroke by age and sex between 2010 and 2015 in Norway, and to generate incidence projections towards year 2040.

Methods

All recorded strokes in Norway between 2010 and 2015 were extracted from the National Patient Registry and the National Cause of Death Registry. We report incidence by age, sex, and year; in raw numbers, per 100,000 person-years, by WHO and European standard populations; and generated statistical models by stroke type, age, sex, and year; and projected stroke incidence toward year 2040.

Results

The data covered 30.1 million person-years at risk, 53431 unique individuals hospitalized with a primary stroke diagnosis, and 6315 additional individuals registered as dead due to stroke. From 2010 to 2015, individuals suffering stroke per 100,000 person-years dropped from 239 to 195 (208 to 177 excluding immediate deaths). The decline was driven by ischemic strokes, with a statistically non-significant time trend for hemorrhagic stroke.

Conclusions

The age-dependent incidence of ischemic strokes in Norway is declining rapidly, and more than compensates for the growth and ageing of the population. Comparisons with historic incidence statistics show that the reduction in incidence rates has accelerated over the last two decades.

Neuroconductor: an R platform for medical imaging analysis

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

Curation and analysis of clinical pathology parameters and histopathologic findings from eTOXsys, a large database project (eTOX) for toxicologic studies

Large data sharing projects amongst the pharmaceutical industry have the potential to generate new insights using data on a scale that has not been previously available. A retrospective analysis of the preclinical toxicology data collected as part of the eTOX project was conducted with the aim to provide background rates and treatment-related value analysis on both clinical pathology and histopathology datasets. Incorporated into this analysis was an extensive data consolidation task to standardise all data. Reference intervals for common clinical pathology parameters in rat and dog were generated, alongside background histopathology incidence rates in the liver, heart and kidney. Systematically applied decision thresholds allowed consistent relabelling of data points considered anomalous, and maximum fold change estimates. Relabelling of anomalous data points was conducted for the histopathology data using a Bayesian model to identify dose-dependent increases in pathologies. The results of this study allow: newly generated data to be analysed using the same methodology, rates and distributions to be used when building predictive dose-response models, and the possibility to correlate clinical pathology findings with concurrent histopathology findings. In the first half of this paper we discuss data curation, in the second half we report on the analytical methods and results.

Statistical modelling of groundwater contamination monitoring data: A comparison of spatial and spatiotemporal methods

Field monitoring of groundwater contamination plumes is an important component of managing risks for downgradient receptors and remedial strategies that rely on monitored natural attenuation. Collection of groundwater quality data can however take a considerable effort and be associated with high cost. Here, we investigated the relative merits of analyzing groundwater quality data using spatial compared to spatiotemporal statistical modelling and assessed the accuracy of both methods and implications for data collection requirements. The aim of this was to determine whether the quantity of data collected can be reduced, while retaining the same level of estimation accuracy, by analyzing groundwater contamination data using a spatiotemporal model which "borrows strength" across time, rather than a spatial model for individual sampling events. To capture the variability encountered under field conditions, we used three hypothetical groundwater contamination plumes with increasing complexity, and site data for a large groundwater gasoline additive plume. The results show that spatiotemporal methods can increase efficiency markedly so that, in comparison with repeated spatial analysis, spatiotemporal methods can achieve the same level of performance but with smaller sample sizes.

The reachable 3-D workspace volume is a measure of payload and body-mass-index: A quasi-static kinetic assessment

An experimental protocol with five tasks is proposed for a low-cost empirical assessment of the reachable 3-D workspace (RWS), including both close-to-torso and far-from-torso regions. Ten participants repeated the protocol for four distinct hand payloads. The RWS expressed as a point cloud and its non-convex alpha-shape were obtained for each case. Moreover, individual strength surrogates for glenohumeral flexion and abduction, and elbow flexion were collected using a dynamometer. The RWS volume was statistically modelled using payload, body-mass-index and the strength surrogates as predictors. For increasing payload, a significant (r = -0.736,p < 0.001) decrease in RWS volume was found for distinct payload cases across all subjects. The only significant predictors found for the RWS volume were normalized payload (F = 73.740,p < 0.001) and body-mass-index (F = 11.008,p = 0.003). No significant interactions were found. The consequent regression model (F(2,27) = 41.11, p < 0.001, R_adj² = 0.7345) explained around 73% of the variation in the data. The RWS volume is a function of payload and body-mass-index.

Bayesian statistical learning for big data biology

Bayesian statistical learning provides a coherent probabilistic framework for modelling uncertainty in systems. This review describes the theoretical foundations underlying Bayesian statistics and outlines the computational frameworks for implementing Bayesian inference in practice. We then describe the use of Bayesian learning in single-cell biology for the analysis of high-dimensional, large data sets.

Application of fourier transform and proteochemometrics principles to protein engineering

Background

Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.

Results

We show that the digitalized representation of the protein sequence in the form of a Fourier spectrum can be used as an efficient descriptor to model the sequence-activity relationship of proteins. The iSAR methodology that we have developed identifies high fitness mutants from mutant libraries relying on physicochemical properties of the amino acids, digital signal processing and regression techniques. iSAR correlates variations caused by mutations in spectra with biological activity/fitness. It takes into account the impact of mutations on the whole spectrum and does not focus on local fitness alone. The utility of the method is illustrated on 4 datasets: cytochrome P450 for thermostability, TNF-alpha for binding affinity, GLP-2 for potency and enterotoxins for thermostability. The choice of the datasets has been made such as to illustrate the ability of the method to perform when limited training data is available and also when novel mutations appear in the test set, that have not been featured in the training set.

Conclusion

The combination of Fast Fourier Transform and Partial Least Squares regression is efficient in capturing the effects of mutations on the function of the protein. iSAR is a fast algorithm which can be implemented with limited computational resources and can make effective predictions even if the training set is limited in size.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2407-8) contains supplementary material, which is available to authorized users.

Capsaicinoids supplementation decreases percent body fat and fat mass: adjustment using covariates in a post hoc analysis

BACKGROUND

Capsaicinoids (CAPs) found in chili peppers and pepper extracts, are responsible for enhanced metabolism. The objective of the study was to evaluate the effects of CAPs on body fat and fat mass while considering interactions with body habitus, diet and metabolic propensity.

METHODS

Seventy-five (N = 75) volunteer (male and female, age: 18 and 56 years) healthy subjects were recruited. This is a parallel group, randomized, double-blind, placebo controlled exploratory study. Subjects were randomly assigned to receive either placebo, 2 mg CAPs or 4 mg CAPs dosing for 12 weeks. After initial screening, subjects were evaluated with respect to fat mass and percent body fat at baseline and immediately following a 12-week treatment period. The current study evaluates two measures of fat loss while considering six baseline variables related to fat loss. Baseline measurements of importance in this paper are those used to evaluate body habitus, diet, and metabolic propensity. Lean mass and fat mass (body habitus); protein intake, fat intake and carbohydrate intake; and total serum cholesterol level (metabolic propensity) were assessed. Body fat and fat mass were respectively re-expressed as percent change in body fat and change in fat mass by application of formula outcome = (12-week value - baseline value) / baseline value) × 100. Thus, percent change in body fat and change in fat mass served as dependent variables in the evaluation of CAPs. Inferential statistical tests were derived from the model to compare low dose CAPs to placebo and high dose CAPs to placebo.

RESULTS

Percent change in body fat after 12 weeks of treatment was 5.91 percentage units lower in CAPs 4 mg subjects than placebo subjects after adjustment for covariates (p = 0.0402). Percent change in fat mass after 12 weeks of treatment was 6.68 percentage units lower in Caps 4 mg subjects than placebo subjects after adjustment for covariates (p = 0.0487).

CONCLUSION

These results suggest potential benefits of Capsaicinoids (CAPs) on body fat and fat mass in post hoc analysis. Further studies are required to explore pharmacological, physiological, and metabolic benefits of both chronic and acute Capsaicinoids consumption.

TRIAL REGISTRATION

ISRCTN10458693 'retrospectively registered'.