A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable k-model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity. Crown

Characteristics and Outcomes of Covid-19 in an Early-Treated Hiv Cohort in Thailand

Background: Emerging data indicate that people with HIV (PWH) are at risk of more severe outcomes from COVID-19. We described the clinical course and laboratory parameters pre-and post-COVID-19 in an early-treated HIV cohort in Thailand. Method(s): RV254 cohort participants were enrolled during Fiebig I-V acute HIV and initiated antiretroviral therapy (ART) within days. They underwent regular blood tests (CD4+ & CD8+ T-cell counts, HIV RNA), neuropsychiatric (NP) assessment (Color Trails 1 & 2, non-dominant hand Grooved Pegboard, Trails Making A), and mood questionnaires (Patient Health Questionnaire-9, Distress Thermometer) post-enrollment longitudinally. Their assessment outcomes pre-and post-COVID-19 were compared using Generalized Estimating Equations (GEE) with a normal distribution and identity link (CD4+, CD8+ T-cell counts, NP parameters) or binomial distribution with log link (HIV RNA), and autoregressive correlation structure. Result(s): Between 4/2021 and 9/2022, 295 participants on ART (98% male, median age 32 [IQR 28-37] were diagnosed with COVID-19. Of these, 16(5%), 38(13%) and 241(82%) were infected with alpha, delta and o variants, determined by the predominant strain circulating in Thailand at the time of infection;238(81%) received >=2 doses of COVID-19 vaccines prior to diagnosis;121(41%) received favipiravir. While 106 (36%) were managed in hospital or 'hospitel', including one intensive care unit admission, only 4(1.4%) received supplemental oxygen and none required mechanical ventilation (mean length of stay: 12 days). The participants were followed a median of 8 [IQR 5-15] weeks post-COVID. Comparing the outcomes pre-and post-COVID, plasma HIV suppression rate remained stable (98% vs. 96%, p=0.212). CD4+ (782 [IQR 708-856] vs. 823 [IQR 748-899], p=0.018) and CD8+ (622 [IQR 563-681] vs. 667 [IQR 605-728], p=0.023) T-cell counts were higher at follow-up after adjusting for age, sex, and duration between COVID-19 diagnosis and follow-up. The increasing trends of CD4+ and CD8+ T-cell were sustained on subsequent visits. Mood scores and NP performance (n=217) were stable at follow-up. Conclusion(s): In this cohort of young PWH on stable ART, we did not observe major clinical adverse events after COVID-19. Increases of CD4+ and CD8+ T-cell counts were observed while mood and NP parameters remained stable. More extensive NP assessment with incorporation of multimodal imaging outcomes and longer follow-up are needed to determine the long-term sequelae of COVID-19 in PWH.

Asymptotic inference for moderate deviations from a unit root of nearly unstable INAR(1) processes

The INAR(1) processes with coefficients , where c>0 is a fixed constant and is a deterministic sequence growing to infinity at a slower rate than n, which are often referred to as nearly unstable INAR processes with moderate deviations from a unit root. We consider some basic properties of the processes and obtain the conditional least squares estimation of the coefficient , which converges to a normal distribution at speed . The simulation study provides numerical support for the theoretical results. The practical utility is illustrated in the data sets about liquor offences, claims of short-term disability and COVID-19, respectively.

Cross-Sectional Survey of Views on COVID-19 and Its Vaccines among Pregnant Women

Objective The objective of this study is to evaluate the acceptance of pregnant women with regards to coronavirus disease 2019 (COVID-19) vaccination during pregnancy and to identify any significant changes in their anxiety and knowledge on COVID-19 compared to our previous study. Methods This cross-sectional survey was performed in the antenatal clinics of United Christian Hospital and Tseung Kwan O Hospital of Hong Kong, China. Questionnaires were distributed to pregnant women for self-completion when attending follow-up from August to October 2021. Apart from basic demographic data, the questionnaire comprised of questions including knowledge on COVID-19 and its vaccines in pregnancy as well as attitudes and behaviors of pregnant women and their partners toward COVID-19. Continuous variables were analyzed by Student's test and Levene's test was used to confirm normal distribution and homogeneity of variance for continuous variables, whereas categorical variables were analyzed by the Chi-squared test or Fisher's exact test as appropriate. A P value of <0.05 was considered to be statistically significant. Results A total of 816 completed questionnaires were included for analysis. Pregnant women were less worried about COVID-19 in the current survey as compared to the last survey (393/816, 48.2% vs. 518/623, 83.1%, P?<?0.001). Fewer pregnant women believed that pregnancy were more susceptible to contract SARS-CoV-2 as compared to the last survey (265/816, 32.5% vs. 261/623, 41.9%, P?<?0.001). They have significant knowledge gap and concerns about COVID-19 vaccines. Nearly half of the participants believed that pregnant women cannot have COVID-19 vaccination (402/816, 49.3%) and it is unsafe to fetus (365/816, 44.7%). Around a third of women perceived that they were more prone to the side effects and complications of COVID-19 vaccines than the general population (312/816, 38.2%) and did not recognize that maternal COVID-19 vaccination could effect transferral of antibodies to the fetus to promote postnatal passive immunity (295/816, 36.2%). Most of them had not been vaccinated (715/816, 87.6%) and only (12/715) 1.7% of them would consider vaccination during pregnancy. Conclusion Despite the local and international recommendations for pregnant women to be vaccinated, the uptake of COVID-19 vaccines during pregnancy remained extremely low. Efforts should be made to effectively provide information about the safety and benefits of COVID-19 vaccines during pregnancy. There is an urgent need to booster vaccination rates in pregnant women to avoid excessive adverse pregnancy outcomes related to COVID-19.Copyright © the Author(s). Published by Wolters Kluwer Health, Inc.

A New Normal Regression with Medical Applications

We define the generalized odd log-logistic normal regression with a dispersion systematic component. We obtain a linear representation, some of its properties, and maximum likelihood estimates. Furthermore, we carry out several simulations for different schemes to evaluate the accuracy of the estimators. The robustness of the new regression model is proved by modeling COVID-19 data. The proposed model explains COVID-19 ICU survival times of the patients in a Brazilian hospital. © 2023.

Anxiety and Coping Strategies among Nursing Students During the Covid-19 Pandemic: An Example From Turkey

Study Objective: The aim of this study is to examine the anxiety levels and coping strategies of nursing students during the covid-19 pandemic. Materials and Methodology: The data were collected as online and The study was completed with 645 students who voluntarily and completely filled in the data collection forms. Results: The average age of the participants was 21.45±1.34, and 79.7% of them were females. The most frequently used coping attitude included "turning to religion" (13.51 ±2.75) indicating an increase in religious activities in case of difficulties;the least utilized coping attitude was found "use of alcohol-drug" (4.82±2.15), indicating the use of substances effect to relieve the tension experienced. Of all the participating students, 55% reported mild, moderate, and severe anxiety levels. Conclusions: Nursing students were found to have decreased generalized anxiety scores when they used problemfocused and emotion-focused coping methods, and they were found to have increased anxiety scores when they used dysfunctional coping methods.

Frequency of SARS-CoV- 2 infection in patients diagnosed with allergic rhinitis between 5-45 years old

Background: Unlike other chronic diseases, allergic diseases such as allergic rhinitis (AR), are not considered risk factors for Covid-19. However, there is a limited number of studies and current data on this subject. In our study, we aimed to conduct a retrospective study on the frequency and severity of Covid-19 disease in patients followed up with the diagnosis of AR. Method(s): In our study, 1915 patients diagnosed with AR who applied to Allergy and Immunology clinic between March 2020 and December 2021 were included. When the RT-PCR for Covid-19 tests of these patients were screened, 102 of them were found to be positive. IBM SPSS Statistics v25 package program was used in the analysis. Result(s): In this study, AR patients are less frequently diagnosed with Covid-19 disease (5.3%). 53.2% of the participants were male (n:1019), 46.8% were female (n:896), and they showed a normal distribution. The average age of all participants was determined as 11 years. In patients with AR, the mean age of patients of Covid-19 positive patients was 14.01+/-4.52. Neutrophil/lymphocyte ratio (NLR) was evaluated in 40 patients and was determined as 2.78+2.79 (normal: 1.6-1.9). C-reactive protein was evaluated in 33 patients and was determined as 4.47+/-4.09 (normal: <3.3). When correlation analysis is applied;a moderately strong correlation was found between patient age and ferritin values (r: 0.413;p: 0.026). A moderately strong correlation was determined between C-reactive protein and ferritin values (r: 0.581;p: 0.004). It has been determined that there is a very strong correlation between the D-dimer (n: 20;324.15+/-168.21;normal: < 250 ng/ml) and ferritin (n: 29;52.26+/-49.48;normal: 21-274 mcg/L) values (r:0.637;p:0.03). The recurrence frequency was found to be 0.2% (n:4) in our AR patients. Conclusion(s): The SARS-CoV- 2 prevalence is lower when compared to 13%, according to the literature. In the follow-up of our AR patients having SARS-CoV- 2, less oxygen support was needed. The recurrence frequency was determined less (0.2%), as compared to 7-21% reported in the literature. Our data suggest that the prevalence, recurrence frequency, and severity of the disease are less than other chronic diseases.

Sars-Cov-2 Infection Does Not Worsen the Course of Inflammatory Bowel Disease in the Long Term

Background and aim: The long-term outcome of inflammatory bowel disease (IBD) patients after SARS-CoV-2 infection is under investigation. In a prospective, single-center study, we aimed to assess whether a recent SARS-CoV-2 infection increases the risk of IBD relapse within 12 months. Material(s) and Method(s): From March to April 2021, all IBD patients with recent (<2 months) SARS-CoV-2 infection (Cases) were enrolled. For each enrolled Case, 4 IBD Controls with no history of infection were considered. Clinical course of IBD was recorded for 12 months. Inclusion criteria: a) well-defined diagnosis of IBD;b) age >=18 and <=85 years;c) 12-months follow-up;d) consent. Exclusion criteria: a) incomplete data;b) SARS-CoV-2 infection after enrollment. Additional inclusion criteria: a) recent SARS-CoV-2 infection for Cases;b) no history of SARS-CoV-2 infection for Controls. Data were expressed as median [range]. Normal distribution of continuous variables was assessed through the Kolmogonov-Smirnov test. Statistical analysis included Student-t Test, Mann-Whitney u-test, 2 test, multivariate logistic regression model (OR [95% CI]), Kaplan- Meier curves, as appropriate. Result(s): During the study period, 143 IBD patients were enrolled. The analysis included 118 patients (22 met the exclusion criteria, 3 lost at follow-up): 29 (24.6%) Cases, 89 (75.4%) Controls. Demographic and clinical characteristics were comparable between groups. During the 12-months study, the frequency of IBD relapse was comparable between Cases and Controls (8 [27%] vs 19 [21%];p=0.65). At univariate analysis, SARS-CoV-2 infection was not a risk factor for IBD relapse within 12-months (1.5 [0.6-3.9];p=0.34). At multivariate analysis, IBD activity at baseline was the only risk factor for relapse (3.2 [1.1-9.1];p=0.03). Kaplan-Meier curves showed that survival from IBD relapse was comparable between Cases and Controls (p=0.33). Conclusion(s): In a prospective 12-months study, a recent SARSCoV- 2 infection did not increase the risk of clinical relapse of IBD in the long term.Copyright © 2023. Editrice Gastroenterologica Italiana S.r.l.

Prediction model of intention to use digital fitness services for health promotion during the COVID-19 pandemic: a gender-based multi-group analysis

As the number of people infected with COVID-19 in Korea is increasing, several measures have been implemented to gradually restrict outdoor activities and indoor gatherings while promoting a non-face-to-face social culture. In this study, we performed a gender-based multi-group analysis using a technology acceptance model (TAM) as an external variable for COVID-19 risk perception to verify the model's predictive ability to increase participation behavior toward digital fitness services. We analyzed the data of 433 Koreans using an online survey consisting of 23 items. A structural equation model was used to verify the perceived ease of use (PEOU), perceived usefulness (PU), intention to use and exercise participation behavior of the TAM with COVID-19 risk perception as an external variable. First, our results showed that COVID-19 risk perception had a statistically higher significant and positive effect on PEOU (beta = 0.170, t = 3.296, p < 0.001) than on PU (beta = 0.130, t = 2.848, p = 0.004) of digital fitness services. Second, the PEOU of the digital fitness service was found to have a statistically higher significant positive effect on PU (beta = 0.512, t = 9.728, p < 0.001) than on intention to use (beta = 0.130, t = -2.774, p = 0.006). Third, the PU of digital fitness services was found to have a statistically significant positive effect on the intention to use (beta = 0.684, t = 12.909, p < 0.001). Fourth, the intention to use the digital fitness service was found to have a statistically significant positive effect on exercise participation behavior (beta = 0.796, t = 16.248, p < 0.001). Lastly, we observed a significant difference between men and women in COVID-19 risk perception and PEOU among the six paths established. Digital environments that encourage participation in exercises could promote health during a pandemic. This study highlighted the need to consider digital environments that encourage exercise participation in creating physical exercise contents as there was no significant difference in the intention to use digital fitness services between men and women.Copyright © 2023 The Author(s).

Risk assessment using information entropy

Risk assessment, particularly when using simulations, requires that the analyst develops estimates of expected, low, and high values for inputs. Mean and standard deviation are often used to assess the variability of metrics, assuming that the underlying distribution is normal. However, it is increasingly realized that non-normal distributions are common and important. If data are available, it is simple and straightforward to check this assumption by computing higher order moments.Claude Shannon [1], [2] proposed that the information entropy for a set of N discrete events can be measured by (Formula Presented) E. T. Jaynes [3] proposed that, if data is available, information entropy can be maximized using Lagrangian multipliers and that the resulting probability distribution maximizes the uncertainty of that distribution given the data.In order to use entropy maximization, it is required to define constraints such that Σpi = 1, plus constraints on the mean, variance, skew, kurtosis, and other moments. This problem does not have a closed form solution but can be solved iteratively in a spreadsheet.The problem can be set up as follows for mean bar x and variance s2: (Formula Presented) This basic formulation models the normal distribution. The importance of non-normality can be estimated by adding higher order moments as desired. For n ≥ 3, constraints can be added using: (Formula Presented) where Mn is the computed nth moment of the data set.Differentiating ∂H/∂pi = 0 maximizes information entropy, and the resulting probability distribution has the most uncertainty given the observed data.This suggests that it is possible to develop an estimate of the distribution where some values are underrepresented in the sample. It further suggests that unusual or atypical results can be better estimated.This paper uses the method of maximizing entropy to model observed data and will study two time series applications. One problem of interest is sequential acquisition of data. For example, time to failure for a device may be a metric of concern. A maximum entropy model provides an empirical estimate of the distribution of this metric. A second problem of interest is forecasting the distribution of a metric at some point in the future. This applies to supply chain management. Project sponsors prepare cost and schedule estimates well in advance of placing the orders for the materials used in those projects. Management reserves for cost and schedule are typically set by subject matter experts, and recent experience (e.g., supply chain disruptions due to the COVID19 pandemic) may overemphasize current data when developing risk assessments. This approach offers a datadriven way to empirically develop risk assessments. © 2023 IEEE.

Households Income in 2021: Influence of the COVID-19 Pandemic

The aim of the study is to assess the consequences of the COVID-19 pandemic on the incomes of households located in various national economies in 2021. The survey of representatives of the economically active adult population (18-64 years old) was conducted in 47 countries in Europe, Asia, Africa, Latin America, and North America within the framework of the Global Entrepreneurship Monitoring Project. The development of mathematical models included the construction of normal distribution density functions in accordance with the authors' methodology. It was proved that almost half of the households (46.6%) had a certain decrease in household income due to the pandemic. Slightly less (45.6%) was the proportion of households in which the income remained stable. An absolute minority (7.8%) of households experienced income growth. © 2022 Universidad Católica de Colombia.

Strategic Planning and Economic Recovery in the Post-Pandemic of the Automotive Sector

This research analyzes from three indicators the impact suffered by the automotive sector in Ecuador after the pandemic and proposes a generic model for the design and creation of strategic planning that helps companies in the sector to adapt to the new context and reactivate their activities in the face of new consumption habits,o maintain its validity in the market. The methodology used was quantitative, the method was deductive of explanatory scope with a non-experimental cross-sectional design, since historical documentary sources published by the Association of Automotive Companies of Ecuador (AEADE) were used. According to the results obtained with this study, it was determined that in 2022, after the pandemic, there was an increase in the number of imported vehicles, a decrease in the number of vehicles sold that were assembled in the country, and there is no significant difference in the number of imported vehicles marketed in the country. In any of these cases, the implementation or updating of appropriate strategic planning in organizations that develop activities in the automotive field will allow the best performance of these and their greater validity in the market. Given these effects, the Government needs to support policies for the preservation of capacities and resources, as well as their subsequent strengthening to promote post-pandemic recovery. The model proposed as a product of the study is composed of 9 phases that allow to develop and apply strategic planning in companies in the automotive sector, this model arises from the review and adoption of the best practices found in four of the most used modelsto the global novel.Copyright © 2023, Anka Publishers. All rights reserved.

N-Step-Ahead Optimal Control of a Compartmental Model of COVID-19

This paper uses a compartmental model that accounts for some of the main features of the COVID-19 pandemic. Assuming a control that represents the aggregated intensity of non pharmaceutical interventions, such as lockdown in varying degrees and the use of masks and social distancing, this text proposes an N-step-ahead optimal control (NSAOC) method that is easy to calculate and provides a guideline for implementation. The compartmental model is extended to account for vaccination, and the N-step-ahead optimal control is calculated for this case as well. The proposed control is robust to parameter variation in all model parameters, when they are assumed to be normally distributed about nominal values. In addition, the proposed NSAOC is shown to compare favorably with a recently proposed PID-like controller. © 2023, Brazilian Society for Automatics--SBA.

The Multivariate Skewed Log-Birnbaum–Saunders Distribution and Its Associated Regression Model

In this article, a multivariate extension of the unit-sinh-normal (USHN) distribution is presented. The new distribution, which is obtained from the conditionally specified distributions methodology, is absolutely continuous, and its marginal distributions are univariate USHN. The properties of the multivariate USHN distribution are studied in detail, and statistical inference is carried out from a classical approach using the maximum likelihood method. The new multivariate USHN distribution is suitable for modeling bounded data, especially in the (0,1)p region. In addition, the proposed distribution is extended to the case of the regression model and, for the latter, the Fisher information matrix is derived. The numerical results of a small simulation study and two applications with real data sets allow us to conclude that the proposed distribution, as well as its extension to regression models, are potentially useful to analyze the data of proportions, rates, or indices when modeling them jointly considering different degrees of correlation that may exist in the study variables is of interest.

COVID-19 multi-state epidemic forecast in India

Clinical importance: Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of novel coronavirus waves, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between infection rate and mortality. Objective: To determine extreme novel coronavirus death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. Design: Apply modern novel statistical methods directly to raw clinical data. Setting: Multicenter, population-based, medical survey data based bio statistical approach. Main outcome and measure: Due to the non-stationarity and complicated nature of novel coronavirus, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Conclusions and relevance: The suggested methodology can be used in various public health applications, based on their clinical survey data. © 2023, Indian National Science Academy.

The Impact of Diabetes Mellitus on the Course of COVID-19 Pneumonia

For this purpose, we compared the demographic characteristics, symptoms, and signs of the disease, laboratory parameters, computerized tomography(CT) findings, and some data about the clinical course of the disease between two patient groups. Of 125 study participants, while 100 constituted the non-diabetic control group, 25 were grouped as the case or patient group diagnosed with DM. [...]in order to rule out the effects of other comorbidities, those with additional comorbidities were not included in both groups. The inclusion criteria were composed of the following: 1- Being over 18 years of age, 2- The determination of SARS-CoV-2 through the reverse transcription-polymerase chain reaction (RT-PCR) test taken from the respiratory tract swabs based on the guidelines of The Turkish Ministry of Health meeting the definition of probable cases, 3- The existence of significant involvement in chest tomography suggesting Covid-19 pneumonia, 4- No additional diseases other than DM in the case group with DM, 5- No additional diseases in the control group without DM, For patients' respiratory tract samples, the oropharyngeal swab samples were first taken and then the nasal samples were obtained using the same swab under the guidelines by The Turkish Ministry of Health. Among the laboratory parameters examined in the study, there was a statistically significant difference between both groups in terms of the values of gamma-glutamyl transferase (GGT), Creactive protein (CRP), white blood cell (WBC) count, neutrophil count, mean platelet volume (MPV), D-dimer and albumin.

Predictive role of some biomarkers in the course of COVID 19 infection

Introduction: Increasing amount of data in the literature proves the predictive role of some biomarkers for severity and the outcome of the COVID 19 infection. Aim(s): To make assessment of the relationship between some laboratory parameters and in hospital outcomes in patients with COVID-19. Method(s): Data from 123 hospital patients for a 3-month period were analyzed, 71 men and 52 women, average age 68.1y +/-12.4. They were divided into two groups regarding the outcome - deceased (82, 66.7%) and discharged (41, 33.3%) and compared by demographic, hematological and biochemical indicators. In the analysis of the results the parametric tests were applied in: normal distribution for hypothesis testing: Student's t-test;Analysis of Variance (ANOVA) - LSD, Tukey HSD, Duncan, Scheffe, Bonferon, Student - Newman - Keuls. Non-parametric tests with a different than normal distribution to test hypotheses: chi2 Pearson test;Kruscal-Wallis test;Man Witney (Wilcoxon) W test. Result(s): There were no significant differences in terms of gender and age in the two groups p>0,05). We found significantly higher values of lactate dehydrogenase (LDH), p=0.0379, fibrinogen, p= 0.0209 and international normalized ratio (INR), p= 0.0151 in the group of the dead. The values of total protein and albumins in the same group were much lower - p=0.0203, p=0.0018 respectively. There was no significant difference between both groups regarding D-dimer, C-reactive protein, leucocytes, lymphocytes and thrombocytes. Conclusion(s): Deviations found in basic laboratory parameters are useful for assessing the risk of the outcome of COVID -19 infection in hospitalized patients.

PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer

With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis model based on PVTv2 and the multiple voting mechanism. To accommodate the different dimensions of the image input, we classified the images using the Transformer model, sampled the images in the dataset according to the normal distribution, and fed the sampling results into the PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method. Our method won the sixth place in the (2nd) COVID19 Detection Challenge of ECCV 2022 Workshop: AI-enabled Medical Image Analysis - Digital Pathology & Radiology/COVID19. Our code is publicly available at https://github.com/MenSan233/Team-Dslab-Solution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on Chinese Stock Market during COVID-19—Based on Random Matrix Theory

This paper focuses on the three industries that are greatly impacted by COVID-19, including the consumption industry, the pharmaceutical industry, and the financial industry. The daily returns of 98 stocks in the consumption industry, the pharmaceutical industry, and the financial industry in the 100 trading days from January 2, 2020, to June 3, 2020, are selected. Based on the random matrix theory, it first analyzes whether the stock market conforms to the efficient market hypothesis during the epidemic period, and second it further studies the linkage between the three industries. The results show that (1) the correlation coefficient is approximately a normal distribution, but the mean value is greater than 0, which is greater than that of the more mature markets such as the United States. (2) There are three eigenvalues greater than the prediction value, of which the maximum eigenvalue is about 11.18 times larger than the largest eigenvalue of the RMT. (3) There is a significant positive relationship between the maximum eigenvalue and the correlation coefficient. The specific market performance is that the stock price fluctuations show a high degree of consistency. (4) In the sample interval, the financial industry has a restraining effect on the consumption industry in the short term, and the pharmaceutical industry has a promoting and then restraining effect on the consumption industry in the short term. The consumption industry has a promoting effect on the financial industry in the short term, and the pharmaceutical industry has a promoting and then restraining effect on the financial industry in the short term. The consumption industry has a promoting and then restraining effect on the pharmaceutical industry in the short term, and the financial industry has a promoting and then restraining effect on the pharmaceutical industry in the short term. (5) In the sample interval, the consumption industry is mainly affected by itself, while the role of the pharmaceutical industry and the financial industry is very small. The pharmaceutical industry is mainly affected by itself and the consumption industry, while the role of the financial industry is very small. The financial industry is mainly affected by itself and the consumption industry, while the role of the pharmaceutical industry is very small. This situation has consequences for individual investors and institutional investors, since some stock returns can be expected, creating opportunities for arbitrage and for abnormal returns, contrary to the assumptions of random walk and information efficiency. The research on the correlation between asset returns will help to accurately price assets and avoid losses caused by price fluctuations during the epidemic.

Tolerance limits for mixture-of-normal distributions with application to COVID-19 data

Tolerance intervals (TIs) are widely used in various applications including manufacturing engineers, clinical research, and pharmaceutical industries. TIs can be used to construct limits of control charts for monitoring quality characteristics. For manufacturing processes where multiple factors may contribute to defects or multiple-stream processes, a mixture distribution of several suitable probabilistic models may be a better choice than a simple distribution for modeling the data. TIs for the normal mixture distribution have been studied in the literature. This article reviews the TIs of the normal mixture distribution, the applications of the mixture distribution, and the control charts of the mixture distribution. A rule for constructing modified two-sided TIs of the normal mixture distribution is summarized, and this rule may be extended to construct modified two-sided TIs for general mixture distributions. The feasibility of using TIs to build control charts for mixture distributions is also discussed. A real data example of coronavirus disease 2019 is used to illustrate the method by linking the TI to control charts. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification. © 2023 Wiley Periodicals LLC.