A new integrated agent-based framework for designing building emergency evacuation: a BIM approach

Today, safety control is considered one of the most important pillars of building construction processes due to maintaining security in major incidents such as fire, earthquake, and flood, and placing a basis of mutual trust between builders and residents for building design and construction. The evacuation process is a key aspect of safety control in case of an emergency such as a fire. This study develops a new integrated agent-based framework for designing building emergency evacuation by using Building Information Model (BIM). Three main steps of the framework include data collection, building model development, and evacuation simulation with a combination of Revit-MassMotion. The methodology is demonstrated through its application to a real case of a multi-story commercial building located in Iran. The building model is simulated through three scenarios with a different number of floors (i.e., one, two, and three floors). In each scenario, the safety of evacuation is evaluated for three designs of stairs in the building. The results show the best performance of the building evacuation in all scenarios can be achieved when two individual stairs are designed for each floor. Other influential factors including the maximum density, vision time, and agent count are more acceptable compared to other design factors. These parameters can also be used to design a control system by using smart conceptual models based on both decision tree and auto-work break structure methods

Meta-Analysis of COVID-19 Spread in Meat Processing Plants and Recommended Practical Actions

Recently, the spread of the coronavirus disease 2019 (COVID-19) has increased among workers of meat processing plants (MPPs) around the world. This study reviewed the possible routes of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and useful actions against it in slaughterhouses. The results revealed that the main factors for the spread of the virus included low indoor temperature, crowded area, wrong standing along production lines, contamination of high-touch surfaces, difficult education of workers with diverse native languages, low financial income, large MPPs with over 10 million Ib of packed meat per month, higher speed of production lines with 175 birds/minute, temporary contract of the workers, and weak approach of some meat processing companies against COVID-19 infection such as National Beef. COVID-19 transmission rate was 24 times higher among the workers of MPPs than among the population of the US. The practical actions against the spread of the virus were mainly marker using for remembering the previous location, mandatory mask use, especially FFP2/3 masks, and decentralization of large MPPs. By using the results of this study, slaughterhouse managers would be able to significantly control the spread of SARS-CoV-2 and future bio-threats to workers of MPPs and even to society

Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

Background: To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals. Methods: A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global and regional trends. We developed prevalence estimates based on modeled distance visual impairment and blindness due to cataract, producing location-, year-, age-, and sex-specific estimates of moderate to severe vision impairment (MSVI presenting visual acuity &lt;6/18, ≥3/60) and blindness (presenting visual acuity &lt;3/60). Estimates are age-standardized using the GBD standard population. Results: In 2020, among overall (all ages) 43.3 million blind and 295 million with MSVI, 17.0 million (39.6%) people were blind and 83.5 million (28.3%) had MSVI due to cataract blind 60% female, MSVI 59% female. From 1990 to 2020, the count of persons blind (MSVI) due to cataract increased by 29.7%(93.1%) whereas the age-standardized global prevalence of cataract-related blindness improved by −27.5% and MSVI increased by 7.2%. The contribution of cataract to the age-standardized prevalence of blindness exceeded the global figure only in South Asia (62.9%) and Southeast Asia and Oceania (47.9%). Conclusions: The number of people blind and with MSVI due to cataract has risen over the past 30 years, despite a decrease in the age-standardized prevalence of cataract. This indicates that cataract treatment programs have been beneficial, but population growth and aging have outpaced their impact. Growing numbers of cataract blind indicate that more, better-directed, resources are needed to increase global capacity for cataract surgery.</p

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Global estimates on the number of people blind or visually impaired by cataract : a meta-analysis from 2000 to 2020

DATA AVAILABILITY : Data sources for the Global Vision Database are listed at the following weblink http://www.anglia.ac.uk/verigbd. Fully disaggregated data is not available publicly due to data sharing agreements with some principal investigators yet requests for summary data can be made to the corresponding author.CHANGE HISTORY 16 July 2024 : A Correction to this paper has been published: https://doi.org/10.1038/s41433-024-03161-7.BACKGROUND : To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals. METHODS : A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global and regional trends. We developed prevalence estimates based on modeled distance visual impairment and blindness due to cataract, producing location-, year-, age-, and sex-specific estimates of moderate to severe vision impairment (MSVI presenting visual acuity <6/18, ≥3/60) and blindness (presenting visual acuity <3/60). Estimates are age-standardized using the GBD standard population. RESULTS : In 2020, among overall (all ages) 43.3 million blind and 295 million with MSVI, 17.0 million (39.6%) people were blind and 83.5 million (28.3%) had MSVI due to cataract blind 60% female, MSVI 59% female. From 1990 to 2020, the count of persons blind (MSVI) due to cataract increased by 29.7%(93.1%) whereas the age-standardized global prevalence of cataract-related blindness improved by −27.5% and MSVI increased by 7.2%. The contribution of cataract to the age-standardized prevalence of blindness exceeded the global figure only in South Asia (62.9%) and Southeast Asia and Oceania (47.9%). CONCLUSIONS : The number of people blind and with MSVI due to cataract has risen over the past 30 years, despite a decrease in the age-standardized prevalence of cataract. This indicates that cataract treatment programs have been beneficial, but population growth and aging have outpaced their impact. Growing numbers of cataract blind indicate that more, better-directed, resources are needed to increase global capacity for cataract surgery.Brien Holden Vision Institute, Fondation Thea, Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation (LCIF), Sightsavers International, and University of Heidelberg. Open Access funding enabled and organized by CAUL and its Member Institutions.https://www.nature.com/eyehj2024School of Health Systems and Public Health (SHSPH)SDG-03:Good heatlh and well-bein

Model Identification and Variable Selection for High-Dimensional Sparse Data

This dissertation is based on the results of four collaborative research projects and one R package including: “An R Package AZIAD for Analyzing Zero-Inflated and Zero-Altered Data”(2), “Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data” (4), “Model Selection and Regression Analysis for Zero-altered or Zero-inflated Data”, “A Trigamma-free Approach for Computing Information Matrices Related to Trigamma Function”, and “AZIAD: Analyz- ing Zero-Inflated and Zero-Altered Data” (5). In Chapter two of my dissertation, I focused on modeling sparse data using zero inflated and zero altered models with both discrete and continuous baseline distributions. I derived the formulas for the Fisher information matrix and developed a comprehensive R package called AIZAD for analyzing zero inflated and zero altered data. AIZAD can estimate maximum likelihood estimators for 27 different distributions and conduct KS tests using two different algorithms, one recommended for smaller sample sizes and another recommended for larger sample sizes. Additionally, it calculates Fisher information and confidence intervals for all parameters in the model. It performs model selection using the likelihood ratio test. I performed numerous simulation study in tems of the size of the test and the power of the test compared to other existing packages. To test the effectiveness of the package, I conducted analysis on two real data sets such as “DebTrivedi” data and omic data. The results showed that AIZAD is a powerful tool for selecting appropriate models through KS tests and model selection. One of my research interest is variable selection in high dimensional data which has numerous applications in computational statistics and computer science. In chapter 2 my primary research goal in this area is to investigate the performance of variable selection compared to dimension reduction in high dimensional data. I explored this subject with the application on (I) gene expression big data and (II) vaginal microbiome data. In this study, I proposed a new technique called “significance test on group labels”. The goal is to select the most informative covariates to predict the class labels. The procedure is as follows: first, we perform model selection for the covariates which is achieved by performing KS-test, and then we compute the Akaike information criterion (AIC) for each covariate. Second, for each label (assuming m classes), we perform model selection model and compute the AIC value. Then, a new aggregated AIC is calculated by summing up the AIC values from m classes. Third, we take the difference of the two AIC values with and without class labels. A larger difference indicates that the covariate is more informative for predicting the class labels. To test the accuracy of the proposed technique, two case studies with real data were conducted, as briefly elaborated below. As the first case study, I applied the proposed method on gene expression data. The RNA-seq gene expression dataset, is a high-dimensional dataset consisting of numerous genes of hundreds of patients that belong to different cancerous tumors. The data includes more than twenty thousand genes, many of which contain a high proportion of zeroes. Through applying the proposed method, I was able to rank the sparse genes based on their AIC differences. A critical question would be how many genes should be selected for predicting the class labels. To answer the question, I utilized a 1-nearest neighbor classifier with various numbers of selected genes to predict the class labels (cancer type) and found that the first significant 50 genes based on the smallest training error will be a good option. Also, in order to obtain a fair estimate for the prediction error, a 5-fold cross-validation was conducted. The best prediction error rate, 0, was attained at 50 genes. As shown in the article, the proposed method outperformed other clustering with dimension reduction methods. As the second case study, the proposed method was applied on a longitudinal vaginal microbiome dataset. The dataset available in (7) includes vaginal microbiome species of 32 non- pregnant women and 22 pregnant women who had a term delivery without complications. The purpose of the study was to characterize the changes in the composition of the vaginal microbiome (concentrating on Lactobacillus microbiome) during 38 weeks of pregnancy between two groups of women, pregnant or non-pregnant. As part of the data screening process, data imputation was used to address missing time points using the nearest neighbor along with linear interpolation. Then, the proposed method was applied to distinguish the significant change in the bacteria, Lactobacillus microbiome, over time. It was found that the two groups tend to be significantly different after week 22. To further investigate the differences between the two groups, before and after week 22 of pregnancy, we conducted a more detailed analysis of the estimated parameters over time. It was observed that Lactobacillus species are significantly smaller in the pregnant group compared to the non-pregnant group at the end of the pregnancy. This longitudinal study can be extended to any other vaginal microbiome species in the dataset as well. Chapter 4 of my dissertation focuses on regression analysis in zero-inflated and zero-altered models. These types of models are commonly used in statistics to handle data with an excess of zeros, which can occur in various fields such as ecology, epidemiology, and economics. The chapter includes an overview of zero-inflated and zero-altered regression models and how they can be fitted to data using statistical software. It contains the derivation formulas given different discrete distributions such as ZIBNB and ZIBB. It is tested on tow real data “DebTrivedi” data and “Insurance Claim” data. Basically the best model was found given 14 different regression models and 4 different link function. After selecting the best model for each data the corresponding fisher information and confidence interval for all the parameters in the model was calculated. Then variable selection was performed to find the most influential covariate for the corresponding model. It covers the interpretation of results and how to make inferences about the relationship between the response variable and the predictors in these models. Overall, the chapter provides a detailed exploration of a specialized area of regression analysis that can be extremely valuable for researchers working with count data. Also, a trigmama free approach was introduced. The motivation is to overcome this issue of difficulty and inefficiency of calculating the expectation of trigamma function by using Monte Carlo simulation. The introduces method is very efficient and fast and led to more accurate result. Finally, in chapter 5, I share my ideas and direction for future work in variable selection for high- dimensional data by performing categorical data analysis and a New R package that can perform regression analysis given different distribution

Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data

Sparse data with a high portion of zeros arise in various disciplines. Modeling sparse high-dimensional data is a challenging and growing research area. In this paper, we provide statistical methods and tools for analyzing sparse data in a fairly general and complex context. We utilize two real scientific applications as illustrations, including a longitudinal vaginal microbiome data and a high dimensional gene expression data. We recommend zero-inflated model selections and significance tests to identify the time intervals when the pregnant and non-pregnant groups of women are significantly different in terms of Lactobacillus species. We apply the same techniques to select the best 50 genes out of 2426 sparse gene expression data. The classification based on our selected genes achieves 100% prediction accuracy. Furthermore, the first four principal components based on the selected genes can explain as high as 83% of the model variability

Categorical Data Analysis for High-Dimensional Sparse Gene Expression Data

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios, including variable screening, model selection, order selection for response categories, and variable selection. We perform our procedure on high-dimensional gene expression data with 801 patients, 2426 genes, and five types of cancerous tumors. As a result, we recommend three finalized models: one with 74 genes achieves extremely low cross-entropy loss and zero predictive error rate based on a five-fold cross-validation; and two other models with 31 and 4 genes, respectively, are recommended for prognostic multi-gene signatures

An R Package AZIAD for Analyzing Zero-Inflated and Zero-Altered Data

Sparse data with a large portion of zeros arise in many scientific disciplines. Modeling sparse data is very challenging due to the skewness of the distribution. We adopt bootstrapped Monte Carlo method to estimate the p-value of the Kolmogorov-Smirnov test, as well as bootstrapped likelihood ratio tests for zero-inflated and zero-altered (or hurdle) model selection. Our new package AZIAD provides miscellaneous functions to simulate zero-inflated or zero-altered data and calculate maximum likelihood estimates of unknown parameters for a large class of discrete or continuous distributions. In addition, we calculate the Fisher information matrix and the confidence intervals of unknown parameters. Compared with other R packages available so far, our package covers many more types of zero-inflated and zero-altered distributions, provides more accurate estimates for unknown parameters, and achieves higher power for model selection. To facilitate the potential users, in this paper we provide theoretical justifications and detailed formulae for functions in AZIAD and illustrate the use of them with executable R code and real dataset.Comment: 13 table