ABSTRACT
The COVID-19 pandemic has hit hard the Indonesian economy. Many businesses had to close because they could not cover operational costs, and many workers were laid off creating an unemployment crisis. Unemployment causes people's productivity and income to decrease, leading to poverty and other social problems, making it a crucial problem and great concern for the nation. Economic conditions during this pandemic have also provided an unusual pattern in economic data, in which outliers may occur, leading to biased parameter estimation results. For that reason, it is necessary to deal with outliers in research data appropriately. This study aims to find within-group estimators for unbalanced panel data regression model of the Open Unemployment Rate (OUR) in East Kalimantan Province and the factors that influence it. The method used is the within transformation with mean centering and median centering processing methods. The results of this study may provide advice on factors that can increase and decrease the OUR of East Kalimantan Province. The results show that the best model for estimating OUR data in East Kalimantan Province is the within-transformation estimation method using median centering. According to the best model, the Human Development Index (HDI) and Gross Regional Domestic Product (GRDP) are two factors that influence the OUR of East Kalimantan Province (GRDP). © 2023, International Association of Engineers. All rights reserved.
ABSTRACT
Urban scaling law quantifies the disproportional growth of urban indicators with urban population size, which is one of the simple rules behind the complex urban system. Infectious diseases are closely related to social interactions that intensify in large cities, resulting in a faster speed of transmission in large cities. However, how this scaling relationship varies in an evolving pandemic is rarely investigated and remains unclear. Here, taking the COVID- 19 epidemic in the United States as an example, we collected daily added cases and deaths from January 2020 to June 2022 in more than three thousand counties to explore the scaling law of COVID- 19 cases and city size and its evolution over time. Results show that COVID- 19 cases super- linearly scaled with population size, which means cases increased faster than population size from a small city to a large city, resulting in a higher morbidity rate of COVID- 19 in large cities. Temporally, the scaling exponent that reflects the scaling relationship stabilized at around 1.25 after a fast increase from less than one. The scaling exponent gradually decreased until it was close to one. In comparison, deaths caused by the epidemic did not show a super-linear scaling relationship with population size, which revealed that the fatality rate of COVID-19 in large cities was not higher than that in small or medium-sized cities. The scaling exponent of COVID- 19 deaths shared a similar trend with that of COVID- 19 cases but with a lag in time. We further estimated scaling exponents in each wave of the epidemic, respectively, which experienced the common evolution process of first rising, then stabilizing, and then decreasing. We also analyzed the evolution of scaling exponents over time from regional and provincial perspectives. The northeast, where New York State is located, had the highest scaling exponent, and the scaling exponent of COVID- 19 deaths was higher than that of COVID-19 cases, which indicates that large cities in this region were more prominently affected by the epidemic. This study reveals the size effect of infectious diseases based on the urban scaling law, and the evolution process of scaling exponents over time also promotes the understanding of the urban scaling law. The mechanism behind temporal variations of scaling exponents is worthy of further exploration. © 2023 Science Press. All rights reserved.
ABSTRACT
It is important to evaluate medical imaging artificial intelligence (AI) models for possible implicit discrimination (ability to distinguish between subgroups not related to the specific clinical task of the AI model) and disparate impact (difference in outcome rate between subgroups). We studied potential implicit discrimination and disparate impact of a published deep learning/AI model for the prediction of ICU admission for COVID-19 within 24 hours of imaging. The IRB-approved, HIPAA-compliant dataset contained 8,357 chest radiography exams from February 2020-January 2022 (12% ICU admission within 24 hours) and was separated by patient into training, validation, and test sets (64%, 16%, 20% split). The AI output was evaluated in two demographic categories: sex assigned at birth (subgroups male and female) and self-reported race (subgroups Black/African-American and White). We failed to show statistical evidence that the model could implicitly discriminate between members of subgroups categorized by race based on prediction scores (area under the receiver operating characteristic curve, AUC: median [95% confidence interval, CI]: 0.53 [0.48, 0.57]) but there was some marginal evidence of implicit discrimination between members of subgroups categorized by sex (AUC: 0.54 [0.51, 0.57]). No statistical evidence for disparate impact (DI) was observed between the race subgroups (i.e. the 95% CI of the ratio of the favorable outcome rate between two subgroups included one) for the example operating point of the maximized Youden index but some evidence of disparate impact to the male subgroup based on sex was observed. These results help develop evaluation of implicit discrimination and disparate impact of AI models in the context of decision thresholds © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.
ABSTRACT
Due to COVID-19, the shift to telecommuting became a widely used work set-up to maintain economic balance. This work set up is associated with risks to employees' wellness. As prevention to the risks, employees must be provided with ways to understand the telecommuting attributes. In relation, this study targets in understanding the links between the socio-economic demographic status, work engagement, and food intake of the education sector's tele-employees. The 110 samples are gathered from the Senior High school Department using convenience sampling, an online survey, and the mixed method. ANOVA and multi-linear regression are used as statistical treatments. The study found that the older generation with higher Income is more likely linked with higher work engagement. The younger generation, low-income earners, and males are inclined more toward unhealthy foods as compared to their counterparts. Low-income earners perceived that their work engagement falls under the category that energy to work is at a bare minimum level. The participants' education attainment revealed significance with energy-giving or carbohydrate-source foods. The qualitative data highlighted job position was perceived with a link to food intake and work engagement. Unhealthy food consumption is perceived with a beneficial association with work engagement, although it is suggested for further investigation. With these findings, the education sector's stakeholders, nutrition, mental health professionals, and future researchers would mainly benefit from this study for intervention generation. © 2023 ACM.
ABSTRACT
Closures of schools in the advent of COVID-19 all around the world have affected nearly about 87% of students in different aspects of life. The importance of school life and its impact on the social and psychological well-being of an adolescent has left a deep and evident impression. As per the Indian population statistics, we embrace the largest adolescent population in the world. The COVID-19 pandemic had a significant impact on the lives of millions around the world. The authors through an exhaustive survey specially designed to relate the present mental state and well-being have analyzed the critical phase. 315 participants that consisted of both males and females take up the survey. 'What we analyzed is the fact regarding the association between Stress anxiety and Depression among the Adolescent in Covid - 19 situation.' . © 2023 IEEE.
ABSTRACT
COVID-19 has spread around the world since 2019. Approximately 6.5% of COVID-19 a risk of developing severe disease with high mortality rate. To reduce the mortality rate and provide appropriate treatment, this research established an integrated models with to predict the clinical outcome of COVID-19 patients with clinical, deep learning and radiomics features. To obtain the optimal feature combination for prediction, 9 clinical features combination was selected from all available clinical factors after using LASSO, 18 deep learning features from U-Net architecture, and 9 radiomics features from segmentation result. A total of 213 COVID-19 patients and 335 non-COVID-19 patients from 5 hospitals were enrolled and used as training and test sample in this research. The proposed model obtained an accuracy, precision, recall, specificity, F1-score and ROC curve of 0.971, 0.943, 0.937, 0.974, 0.941 and 0.979, respectively, which exceeds the related work using only clinical, deep learning or radiomics factors. © 2023 SPIE.
ABSTRACT
The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily "world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates. © 2023, The Author(s).
ABSTRACT
The objective of this research paper is to investigate the impact of COVID-19 on the factors influencing on-time software project delivery in different Software Development Life Cycle (SDLC) models such as Agile, Incremental, Waterfall, and Prototype models. Also to identify the change of crucial factors with respect to different demographic information that influences on-time software project delivery. This study has been conducted using a quantitative approach. We surveyed Software Developers, Project Managers, Software Architect, QA Engineer and other roles using a Google form. Python has been used for data analysis purposes. We received 72 responses from 11 different software companies of Bangladesh, based on that we find that Attentional Focus, Team Stability, Communication, Team Maturity, and User Involvement are the most important factors for on-time software project delivery in different SDLC models during COVID-19. On the contrary, before COVID-19 Team Capabilities, Infrastructure, Team Commitment, Team Stability and Team Maturity are found as the most crucial factors. Team Maturity and Team Stability are found as common important factors for both before and during the COVID-19 scenario. We also identified the change in the impact level of factors with respect to demographic information such as experience, company size, and different SDLC models used by participants. Attentional focus is the most important factor for experienced developers while for freshers all factors are almost equally important. This study finds that there is a significant change among factors for on-time software project delivery before and during the COVID-19 scenario. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
ABSTRACT
According to World Bank statistics in 2019, Indonesia ranked two in the average unemployment rate with 5.28% in South East Asia. Although the unemployment rate can be reduced by an equitable distribution of human resource empowerment and national development, the global pandemic COVID-19 made a major impact on increasing the rate of unemployment. This paper tests the spatial autocorrelation on the average unemployment in Indonesia using Ordinary Least Squares (OLS) and Moran's I. The OLS method was used to examine the effects that affect the unemployment rate using an independent variable. In contrast, the Moran's I used to prove the existence of spatial effect on the level of movement in Indonesia. From the experiment, there are four variables that influence the unemployment rate by using the OLS modeling method. The Moran's I test showed a p-value = 0.006 with α = 0.05. Therefore, there is a spatial autocorrelation between provinces in Indonesia. In addition, the model is tested using the Variance Inflation Factor. The model showed a VIF score ¡10, therefore there is no collinearity and the assumption is fulfilled. The model is also being tested using dwtest, bptest, and Lilliefors test. The result showed p-value = 0.6231 for dwtest, p-value = 0.932 for bptest, and p-value = 0.08438 for Lilliefors test.. © 2022 IEEE.
ABSTRACT
Nowadays, spatial geographic data analysis and GIS related software are more and more applied to the planning of urban public facilities. Under the COVID-19, people pay more attention to the protection of medical facilities for people's health, and a reasonable distribution of hospital facilities is conducive to people's health. Taking Haikou City as an example, this research will optimize the location of hospital space layout according to the existing third-level first-class general hospitals in Haikou City by using GIS software, road analysis, spatial analysis, and other methods. The results show that the existing hospitals in Haikou are too concentrated in the central urban area, the overall distribution of medical facilities is lack of balance, and there is a serious lack of medical facilities in new urban development areas and suburbs. According to the comparison between population density analysis and traffic analysis and the service scope of existing hospitals, the author finds out the scope of hospitals that need to be supplemented, and then calculates the scope of service area after taking several random points within the scope, and finally finds the one with the largest service scope is the optimal location. The results obtained by optimizing the site selection can provide a scientific reference for the rational layout of medical facilities in Haikou City in the future. © 2023 SPIE.
ABSTRACT
The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available data commons as well as a sequestered commons for performance evaluation of algorithms. This work sought to evaluate the currently implemented methodology for apportioning data to the public and sequestered data commons by investigating the resulting distributions of joint demographic characteristics between the public and sequestered commons. 54,185 patients whose de-identified imaging studies and metadata had been submitted to MIDRC were previously separated into public and sequestered commons using a multi-dimensional stratified sampling method, resulting in 41,556 patients (77%) in the public commons and 12,629 patients (23%) in the sequestered commons. To compare the balance obtained in the joint distributions of patient characteristics from use of the developed sequestration method, patients from each commons were separated into bins, representing a unique combination of the demographic variables of COVID-19 status, age, race, and sex assigned at birth. The joint distributions of patients were visualized, and the absolute and percent difference in each bin from an exact 77:23 split of the data were calculated. Results indicated 75.9% of bins obtained differences of less than 15 patients, with a median difference of 3.6 from the total data for both public and sequestered commons. Joint distributions of patient characteristics in both the public and sequestered commons closely matched each other as well as that of the total data, indicating the sequestration by stratified sampling method has operated as intended. © 2023 SPIE.
ABSTRACT
Urban areas have interaction characteristics that favor the spread of the Covid-19 pandemic. The lifestyle of urban communities with higher close contact influences the speed of the spread of Covid19, which makes cities play an important role in the transmission of Covid19. Surgo Ventures' Covid19 Community Vulnerability Index variable is used to analyze the community vulnerability in Surakarta Greater Urban. Statistics from government agencies were used to collect data on population, heterogeneity, housing conditions, health care systems, and environmental risks, which were then analyzed in the sub-district spatial unit. The findings show a close correlation between the aggregate value of the Covid19 Community Vulnerability Index (CCVI) and the rate of spread of Covid19 both in the city center and the urban fringe. However, the variable with the strongest correlation in the urban area differs from the variable in the urban fringe area. Furthermore, there are differences in vulnerability in urban communities. This demonstrates the need for different Covid19 handling strategies in different communities, despite the fact that they are all part of the same urban service system. The identification of these determinants may subsequently contribute to the design of cities that are better prepared for future pandemics.
ABSTRACT
A dynamic view of the evolution of the infections of SARS-CoV-2 in Catalonia using a Digital Twin approach that forecasts the true infection curve is presented. The forecast model incorporates the vaccination process, the confinement, and the detection rate, and virtually allows to consider any nonpharmaceutical intervention, enabling to understand their effects on the disease's containment while forecasting the trend of the pandemic. A continuous validation process of the model is performed using real data and an optimization model that automatically provides information regarding the effects of the containment actions on the population. To simplify this validation process, a formal graphical language that simplifies the interaction with the different specialists and an easy modification of the model parameters are used. The Digital Twin of the pandemic in Catalonia provides a forecast of the future trend of the SARS-CoV-2 spread and information regarding the true cases and effectiveness of the NPIs to control the SARS-CoV-2 spread over the population. This approach can be applied easily to other regions and can become an excellent tool for decision-making. © 2023 The Authors. Advanced Theory and Simulations published by Wiley-VCH GmbH.
ABSTRACT
In this research, we analyzed the delivery service areas of restaurants, customer satisfaction, and restaurant sales of urban restaurants during the COVID-19 pandemic. We obtained the datasets on food ordering options and restaurant rankings based on Google Maps, Open Street Map, and widely known online food order applications in Iran. Based on this analysis we further modeled suitable areas for future extension of restaurants. We analyzed the online food order data of restaurants' sales and food delivery reports for 1050 restaurants in the city of Tabriz. We collected and analyzed data on the restaurant locations, the number of food orders for each restaurant, and the number of customers and their locations. Our results revealed that the spatial dimension of the newly emerging food delivery areas is of utmost importance for the success of restaurants. This indicates that an optimal location is not longer only dependent on factors like population density and competitors in the direct vicinity but on the services density even from more distant competitors. The results indicate that an optimized spatial distribution of the restaurants together with efficient quality in services can contribute to optimistic urban development. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.
ABSTRACT
When the pandemic was at its peak, it was a quite difficult task for the government to schedule vaccine supply in various districts of a state. This task became further difficult when vaccines were required to be supplied to various Covid Vaccination Centers (CVCs) at a granular level. This is because there was no data regarding the trend being acquired at each CVC and the population distribution is non-uniform across the district. This led to the arousal of an ambiguous situation for a certain period and hence mismanagement. Now that we have sufficient data across each CVC, we can work on a time series analysis of vaccine requirements in which we can essentially forecast the number of administered doses and optimize the wastage at all atomic CVC levels. © 2023 IEEE.
ABSTRACT
Optimal protocols of vaccine administration to minimize the effects of infectious diseases depend on a number of variables that admit different degrees of control. Examples include the characteristics of the disease and how it impacts on different groups of individuals as a function of sex, age or socioeconomic status, its transmission mode, or the demographic structure of the affected population. Here we introduce a compartmental model of infection propagation with vaccination and reinfection and analyze the effect that variations on the rates of these two processes have on the progression of the disease and on the number of fatalities. The population is split into two groups to highlight the overall effects on disease caused by different relationships between vaccine administration and various demographic structures. As a practical example, we study COVID-19 dynamics in various countries using real demographic data. The model can be easily applied to any other disease transmitted through direct interaction between infected and susceptible individuals, and any demographic structure, through a suitable estimation of parameter values. Two main conclusions stand out. First, the higher the fraction of reinfected individuals, the higher the likelihood that the disease becomes quasi-endemic. Second, optimal vaccine roll-out depends on demographic structure and disease fatality, so there is no unique vaccination protocol, valid for all countries, that minimizes the effects of a specific disease. Simulations of the general model can be carried out at this interactive webpage Atienza (2021 S2iyrd model simulator). © 2023 The Author(s). Published by IOP Publishing Ltd.
ABSTRACT
Mark–recapture sampling schemes are conventional approaches for population size (N) estimation. In this paper, we mainly focus on providing fixed-length confidence interval estimation methodologies for N under a mark–recapture–mark sampling scheme, where, during the resampling phase, non-marked items are marked before they are released back in the population. Using a Monte Carlo method, the interval estimates for N are obtained through a purely sequential procedure with an adaptive stopping rule. Such an adaptive decision criterion enables the user to "learn” with the subsequent marked and newly tagged items. The method is then compared with a recently developed accelerated sequential procedure in terms of coverage probability and expected number of captured items during the resampling stage. To illustrate, we explain how the proposed procedure could be applied to estimate the number of infected COVID-19 individuals in a near-closed population. In addition, we present a numeric application inspired on the problem of estimating the population size of endangered monkeys of the Atlantic forest in Brazil.
ABSTRACT
The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient. © 2023 IEEE.
ABSTRACT
Introduction and objective: Undoubtedly, employees face a variety of organizational problems and issues during the service period. Employees' decisions and actions in facing these problems and issues will be based on their knowledge and mental patterns. This study aimed to document and record the cultural and spiritual experience in the face of the COVID-19 crisis. Method: This research was an applied, inductive and exploratory-descriptive case study, which was conducted as a one-time crosssectional survey in a qualitative manner. Data was collected using documentary reviews and questionnaires. The statistical population of this study included managers, experts, and administrators of Baqiyatallah University located in Tehran, Iran. According to the qualitative approach of the research, purposeful sampling was used and data collection was continued until the required data was collected. Data analysis was performed in MaxQDA 2020 and Excel 2019 software. Results: The results showed a total of 282 open source codes. Code frequencies were as follows: 25 codes for event dimension, 54 for issue dimension, 61 for measures and decisions dimension, 71 for output-outcome dimension, 35 for suggestions dimension, 17 for scenario planning and modeling dimension, and 19 for lessons learned dimension. Conclusion: The results of this study can be used as a basis for managers to plan and implement experience documentation in cultural and spiritual areas in the face of the COVID-19 crisis. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)