Search

The COVID-19 epidemic has required countries to implement different containment strategies to limit its spread, like strict or weakened national lockdown rules and the application of age-stratified vaccine prioritization strategies. These interventions have in turn modified the age-dependent patterns of social contacts. In our recent paper, starting from the available age-structured real data at the national level, we identified, for the Italian case, specific virulence parameters for a two-age-structured COVID-19 epidemic compartmental model (under 60, and 60 years and over) in six different diseases transmission scenarios under concurrently adopted feedback interventions. An interpretation of how each external scenario modifies the age-dependent patterns of social contacts and the spread of COVID-19 disease has been accordingly provided. In this paper, which can be viewed as a sequel to the previous one, we mainly apply the same general methodology therein (involving the same dynamic model) to new data covering the three subsequent additional scenarios: (i) a mitigated coordinated intermittent regional action in conjunction with the II vaccination phase; (ii) a super-attenuated coordinated intermittent regional action in conjunction with the II vaccination phase; and (iii) a last step towards normality in conjunction with the start of the III vaccination phase. As a new contribution, we show how meaningful updated information can be drawn out, once the identification of virulence parameters, characterizing the two age groups within the latest three different phases, is successfully carried out. Nevertheless, differently from our previous paper, the global optimization procedure is carried out here with the number of susceptible individuals in each scenario being left free to change, to account for reinfection and immunity due to vaccination. Not only do the slightly different estimates we obtain for the previous scenarios not impact any of the previous considerations (and thus illustrate the robustness of the procedure), but also, and mainly, the new results provide a meaningful picture of the evolution of social behaviors, along with the goodness of strategic interventions. [ABSTRACT FROM AUTHOR]

The COVID-19 lockdown had a positive control effect on urban air quality. However, this effect remains uncertain after the epidemic enters regular management, and furthermore, only limited data are available regarding urban PM2.5 (aerodynamic diameter ≤ 2.5μm) under the impact of the epidemic. We used daily ambient PM2.5 concentration data in Beijing to compare and analyze the changes in urban PM2.5 concentrations before and after the COVID-19 epidemic and to estimate the healthy effects and economic burden associated with PM2.5 before and after the epidemic. The study found that COVID-19 has a significant impact on the urban environmental PM2.5 concentration, which is manifested by the decrease in the PM2.5 concentration in Beijing during the epidemic by 27.8%. Exposure-response models estimated 56.443 (95% CI: 43.084–69.893) thousand people die prematurely in Beijing during the COVID-19 epidemic attributed to long-term PM2.5 exposure, with a 13.3% decrease in the number of premature deaths year-on-year. The total healthy economic losses attributable to PM2.5 in Beijing during the COVID-19 epidemic were 35.76 (95% CI: 28.41–42.44) billion yuan, with a per capita loss of 816.8 yuan. Strict control measures throughout the COVID-19 epidemic had a positive impact on air quality in Beijing, with a decrease in both premature deaths and economic healthy losses attributable to fine particles. This paper helps to enrich and expand the research on the impact of COVID-19 on the urban environment and provides a basis for formulating policies related to air quality improvement in the post-epidemic era. [ABSTRACT FROM AUTHOR]

Purpose: The coronavirus disease 2019 (COVID-19) epidemic has caused stress for everyone and impacted the lives of people globally. Such stress increases troubles for the employees. Therefore, the aim of the paper is to identify symptoms of health problems that employees face during the COVID-19 epidemic. Also, the aim of the paper is to examine if there are statistically significant differences in the physical, emotional and behavioral symptoms of health problems among employees before the COVID-19 epidemic and employees during the COVID-19 epidemic. Design/methodology/approach: The research is based on a survey of 950 employees in Slovenian companies. The factor analysis and the t-test for two independent samples were used to test the hypotheses of the research. Findings: The results show that physical, emotional and behavioral symptoms of health problems of employees during the COVID-19 epidemic are intensified as compared to the before COVID-19 epidemic era. Originality/value: The results highlight the measures with which companies can reduce the problem of different symptoms of employees during the COVID-19 epidemic. The results can be useful for employers and for managers who want to create an adequate working environment for employees during the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Since 2020, COVID-19 has repeatedly arisen around the world, which has had a significant impact on the global economy and culture. The prediction of the COVID-19 epidemic will help to deal with the current epidemic and similar risks that may arise in the future. So, this paper proposes a hybrid prediction model based on particle swarm optimization variational mode decomposition (PSO-VMD), Long Short-Term Memory Network (LSTM) and AdaBoost algorithm. To address the issue of determining the optimal number of modes K and the penalty factor (α) in the variational mode decomposition (VMD), an adaptive value for particle swarm optimization (PSO) is proposed. Specifically, the weighted average sample entropy of the relevant coefficients is utilized to determine the adaptive value. First, the epidemic data are decomposed into multiple modal components, known as intrinsic mode functions (IMFs), using PSO-VMD. These components, along with policy-based factors, are integrated to form a multivariate forecast dataset. Next, each IMF is predicted using AdaBoost-LSTM. Finally, the prediction results of all the IMF components are reconstructed to obtain the final prediction result. Our proposed method is validated by the cumulative confirmed data of Hubei and Hebei provinces. Specifically, in the case of cumulative confirmation data, the coefficient of determination ( R 2 ) of the mixed model is increased compared to the control model, and the average mean absolute error (MAE) and root mean square error (RMSE) decreased. The experimental results demonstrate that the VMD–AdaBoost–LSTM model achieves the highest prediction accuracy, thereby offering a new approach to COVID-19 epidemic prediction. [ABSTRACT FROM AUTHOR]

Bulgaria has the lowest COVID-19 vaccination rate in the European Union and the second-highest COVID-19 mortality rate in the world. That is why we think it is important better to understand the reason for this situation and to analyse the development of the disease over time. In this paper, an extended time-dependent SEIRS model SEIRS-VB is used to investigate the long-term behaviour of the COVID-19 epidemic. This model includes vaccination and vital dynamics. To apply the SEIRS-VB model some numerical simulation tools have been developed and for this reason a family of time-discrete variants are introduced. Suitable inverse problems for the identification of parameters in discrete models are solved. A methodology is proposed for selecting a discrete model from the constructed family, which has the closest parameter values to these in the differential SEIRS-VB model. To validate the studied models, Bulgarian COVID-19 data are used. To obtain all these results for the discrete models a mathematical analysis is carried out to illustrate some biological properties of the differential model SEIRS-VB, such as the non-negativity, boundedness, existence, and uniqueness. Using the next-generation method, the basic reproduction number associated with the model in the autonomous case is defined. The local stability of the disease-free equilibrium point is studied. Finally, a sensitivity analysis of the basic reproduction number is performed. [ABSTRACT FROM AUTHOR]

Novel coronavirus (COVID‐19), a global threat whose source is not correctly yet known, was firstly recognised in the city of Wuhan, China, in December 2019. Now, this disease has been spread out to many countries in all over the world. In this paper, we solved a time delay fractional COVID‐19 SEIR epidemic model via Caputo fractional derivatives using a predictor–corrector method. We provided numerical simulations to show the nature of the diseases for different classes. We derived existence of unique global solutions to the given time delay fractional differential equations (DFDEs) under a mild Lipschitz condition using properties of a weighted norm, Mittag–Leffler functions and the Banach fixed point theorem. For the graphical simulations, we used real numerical data based on a case study of Wuhan, China, to show the nature of the projected model with respect to time variable. We performed various plots for different values of time delay and fractional order. We observed that the proposed scheme is highly emphatic and easy to implementation for the system of DFDEs. [ABSTRACT FROM AUTHOR]

In order to strengthen the management and control of personnel flow and the timeliness of information dissemination during the COVID-19 epidemic, it is necessary to improve the reliability of authority and content management. The extensive application of authority information management can closely connect the professional system with the production activities of enterprises. At present, the traditional authority management level is complex, and the management of complex data is chaotic. This paper is based on ASP.NET. This paper proposes a data authority management method. The main function of this method is to establish a standard three-tier structure of rights management level by using the open-source string template engine technology, tag analysis, SEO search engine optimization technology and information collection technology. This method provides a general process oriented and automatic management platform for users through the hierarchical management mechanism. This technology can meet people's rights and content requirements for creating, publishing and updating websites without understanding the technology itself. The experimental results show that the rapid management mechanism based on authority management can accurately handle and manage authority and data which can improve the accuracy and timeliness of personnel flow control under the impact of the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Research background: On 11 March 2020, the Covid-19 epidemic was identified by the World Health Organization (WHO) as a global pandemic. The rapid increase in the scale of the epidemic has led to the introduction of non-pharmaceutical countermeasures. Forecast of the Covid-19 prevalence is an essential element in the actions undertaken by authorities. Purpose of the article: The article aims to assess the usefulness of the Auto-regressive Integrated Moving Average (ARIMA) model for predicting the dynamics of Covid-19 incidence at different stages of the epidemic, from the first phase of growth, to the maximum daily incidence, until the phase of the epidemic's extinction. Methods: ARIMA(p,d,q) models are used to predict the dynamics of virus distribution in many diseases. Model estimates, forecasts, and the accuracy of forecasts are presented in this paper. Findings & Value added: Using the ARIMA(1,2,0) model for forecasting the dynamics of Covid-19 cases in each stage of the epidemic is a way of evaluating the implemented nonpharmaceutical countermeasures on the dynamics of the epidemic. [ABSTRACT FROM AUTHOR]

The complex and changeable external social and economic environment has a significant impact on the sustainable development of the closed-loop supply chain. In particular, the occurrence of uncertain emergencies increases the risk of interruption of the closed-loop supply chain, making it insufficient to analyze its complex changes from the perspective of material flow alone. Based on this analysis, the paper constructs a closed-loop supply chain material flow and capital flow coupling system composed of manufacturers, sellers and recyclers to explore the impact of material flow sudden interruption on the closed-loop supply chain system when an uncertain emergency occurs. In this paper, based on the closed-loop supply chain system coupled with logistics and capital flow, a system dynamics simulation model was established by using Vensim simulation software to analyze the impact of COVID-19 epidemic on manufacturers, sellers and recyclers under five scenarios. The results show that when COVID-19 outbreaks occur, the material flow of each main enterprise in the closed-loop supply chain is more easily influenced than the capital flow. At the same time, it can be found that the recyclers in the main enterprises of the closed-loop supply chain are more easily influenced by the material flow. The model constructed in this paper has applicability and can be used for related studies of closed-loop supply chain under other emergencies, but the scene design should be carried out according to the characteristics of emergencies themselves. [ABSTRACT FROM AUTHOR]

Copyright of Communication & Culture Online / Komunikacija i Kultura Online is the property of FOKUS: Forum za Interkulturnu Komunikaciju 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Copyright of Družboslovne Razprave is the property of Druzboslovne Razprave 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Previous studies have investigated the effects of the pandemic on quality of life; both economical and psychosocial effects were explored. Some studies also mentioned mediating factors as mechanisms in this relationship; however, the mediating role of anxiety has not been examined. The present study examined the mediating effects of anxiety on the relationship between socioeconomic impact of COVID-19 and quality of life. An online survey of 280 Vietnamese residents was conducted in the context of the pandemic outbreak. Anxiety was found to be a completely mediating variable in the association between the socioeconomic impact of the pandemic and quality of life during the lockdown period. This finding contributes to a better understanding of how the pandemic affects people's quality of life and serves as the foundation for lessening the negative effects of the epidemic on people's lives. [ABSTRACT FROM AUTHOR]

The outbreak of Covid-19 epidemic has a prolonged impact on global economic activities. In recent years, many scholars have been motivated to estimate the effects of Covid-19 shock on global foreign direct investment (FDI). However, existing studies have not paid enough attention to the spillover effects caused by the epidemic. Although few academic works have explored the geographic-neighboring spillover effects of epidemic shock on global investment, we further extent the understanding of the spillover effects in an economic network. On the basis of country-month greenfield FDI panels, we construct a spatial Durbin model, and figure out that Covid-19 shock may have positive FDI spillover effects in an economic network via global FDI transfers. Furthermore, we find that such spillovers are greatly conditioned by country-level network position and institutional ties among nations. Our research suggests that global FDI transfers may partly offset economic-adverse effects of the Covid-19 shock. While global countries, especially those in the Global South, should be more closely embedded in the global investment network in such an uncertain environment. • This paper investigates the spillover effects of Covid-19 epidemic on global greenfield investment network. • Covid-19 would shift much more overseas greenfield investment projects to the victim's neighbor on the economic network. • Such potential positive spillovers are conditioned by country-level network position and institutional ties among nations. • The research suggests that the Global South countries should be closely embedded in the global investment network in an uncertain environment. [ABSTRACT FROM AUTHOR]

This paper looks into the impact of the recent COVID-19 epidemic on the daily mobility of people. Existing research into the epidemic travel patterns points at transport as a channel for disease spreading with especially long-distance travel in the centre of interest. We adopt a different approach looking into the effects that epidemic has on the transport system and specifically in relation to short-distance daily mobility activities. We go beyond simple travel avoidance behaviours and look into factors influencing change in travel times and in modal split under epidemic. This leads to the research problems we posit in this paper. We look into the overall reduction of daily travel and into the factors impacting peoples' decisions to refrain from daily traveling. This paper focuses on modes affected and explores differences between various societal groups. We use a CATI survey with a representative sample size of 1069 respondents from Poland. The survey was carried out between March, 24th and April, 6th2020, with a start date one week after the Polish government introduced administrative measures aimed at slowing down the COVID-19 epidemic. For data analysis, we propose using the GLM (general linear model), allowing us to include all the qualitative and quantitative variables which depict our sample. We observe significant drops in travel times under epidemic conditions. Those drops are similar regardless of the age group and gender. The time decrease depended on the purpose of travels, means of transport, traveller's household size, fear of coronavirus, main occupation, and change in it caused by the epidemic. The more the respondent was afraid of coronavirus, the more she or he shortened the travel time. • CATI survey reveals significant drops in daily travel times during epidemic. • The research confirms the effectiveness of mobility restrictions. • Travel time drops are similar regardless of the age group and gender. • Main occupation is the key factor affecting travel time change. • The more one was afraid of coronavirus, the bigger the travel time reduction was. [ABSTRACT FROM AUTHOR]

This paper develops the exponentiated Mfamily of continuous distributions, aiming to provide new statistical models for data fitting purposes. It stands out from the other families, as it depends on two baseline distributions, with the use of ratio and power transforms in the definition of the main cumulative distribution function. Thanks to the joint action of the possibly different baseline distributions, flexible statistical models can be created, motivating a complete study in this regard. Thus, we discuss the theoretical properties of the new family, with emphasis on those of potential interest to the overall probability and statistics. Then, a new three-parameter lifetime distribution is derived, with the choices of the inverse exponential and exponential distributions as baselines. After pointing out the great flexibility of the related model, we apply it to analyze an actual dataset of current interest: the daily COVID-19 cases observed in Pakistan from 21 March to 29 May 2020 (inclusive). As notable results, we demonstrate that the proposed model is the best among the 15 top ranked models in the literature, including the inverse exponential and exponential models, several modern extensions of them depending on more parameters, and the "unexponentiated" version of the proposed model as well. As future perspectives, the proposed model can be of interest to analyze data on COVID-19 cases in other countries, for possible comparison studies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO 2 emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO 2 emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO 2 emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO 2 emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO 2 emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO 2 emissions declines into firm climate action. • COVID-19 has negative effects on economy, but positive effects on CO 2 emissions. • A new model is used to measure economic and CO 2 emission effects during COVID-19. • Hubei had the biggest economic and CO 2 emission effects on China due to COVID-19. • Economic and CO 2 emission effects of COVID-19 are combined to select key sectors. [ABSTRACT FROM AUTHOR]

With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m–2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m–1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54% under this epidemic. [ABSTRACT FROM AUTHOR]

Copyright of Karbala Journal of Pharmaceutical Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Coronavirus disease (COVID-19) spread across the globe through the human transmission. The World Health Organization suggested social distancing to curb the community spread. After national social lockdown started in India, air quality improved drastically. This further hypothesized to influence the environment and human health, and this study is positively the first to weigh it using multiple indices. The calculated environmental indices are photochemical ozone creation potential (POCP), acidification potential (AP), and eutrophication potential (EP). The cancer risk, chronic health index (CHI), and acute health index (AHI) were considered to calculate the health risk. The spatial trend change in the air pollution reflecting on these indices are calculated for four Indian megacities Delhi, Bangalore, Hyderabad, and Kolkata. Temporal variation was accounted for monthly (2019 vs 2020), one-week and two-weeks period during the social lockdown. The results showed a significant decrease in environmental and health risk during the lockdown due to a corresponding decrement in air pollution. The decrease in the particulate matter was found to play a vital role in altering the air pollution mediated risks of interest. Delhi showed a maximum difference in POCP and Acute HI by recording a dip of 70.79% and 43.53% respectively in 2020 during lockdown. The maximum reduction in health risk indices was 41%, 31%, 17%, 19% for Delhi, Bangalore, Hyderabad, and Kolkata. Bangalore recorded the maximum decline in EP, Cancer risk, Chronic HI by 66.66%, 58.62%, and 58.76% in 2020 compared to 2019. A maximum fall in AP was seen in Kolkata by 57.23% in 2020 among all cities. The connection between these drop-in indices and the cause of air pollutants were well discussed. This present paper gives more in-depth insights into air pollution's effect on environmental and health parameters by connecting and converging various air pollution aspects into a single scale. This study also enlightens the importance of controlling air pollution to have a better environment and healthy life to attain sustainable development. Conceptual illustration of impact of COVID-19 enabled social lockdown on environmental and health risk indices in India. [Display omitted] • Detailed study on COVID-19 lockdown vs environmental and health indices in India. • Included three time periods & four megacities Delhi, Mumbai, Bangalore & Kolkata. • Noticeable reduction in particulate matter and indices during lockdown. • Maximum health risk reduction in Delhi (41%) and Bangalore (31%). • Bangalore recorded maximum reduction in EP, Cancer risk, Chronic HI. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected all sectors across the globe. The education sector is one of the critical sectors that have been affected on all continents. Educational institutions, including universities, are struggling as they had no time to manage their learning system to encounter the sudden shock of COVID-19. The purpose of this study is to explore and examine the readiness of higher education institutes for e-learning. A case study of Saudi Arabia universities during the COVID-19 pandemic is considered. Our analysis focused on measuring the level of readiness between public and private universities. The results show that universities adopting blended learning systems among both public and private universities managed to reduce the negative impact of the epidemic much better than traditional universities. [ABSTRACT FROM AUTHOR]

With the outbreak of COVID-19 (Corona Virus Disease, 2019), China adopted traffic restrictions to reduce the spread of COVID-19. Using daily data before and after the outbreak of COVID-19, an exogenous shock, this paper analyzes the effects of private vehicle restriction policies on air pollution. We find that the private vehicle restriction policies reduce the degree of air pollution to a certain extent. However, their effect varies with other policies implemented in the same period and the economic development of the city itself. Through the analysis of different categories of restrictions, we find that restriction policy for local fuel vehicles and the restriction policy based on the last digit of license plate numbers have the best effect in reducing air pollution. Under the background of COVID-19 epidemic and the implementation of private vehicle restriction policies and other traffic control policies during this period, we have also obtained other enlightenment on air pollution control. • The causal relationship between air pollution and restriction is analyzed. • An exogenous shock from COVID-19 is used to identify this relationship. • The private vehicle restriction reduces the degree of air pollution. • The restriction policies have heterogeneous effects. • The effects of restriction are affected by other policies against COVID-19. [ABSTRACT FROM AUTHOR]

COVID-2019 is a global threat, for this reason around the world, researches have been focused on topics such as to detect it, prevent it, cure it, and predict it. Different analyses propose models to predict the evolution of this epidemic. These analyses propose models for specific geographical areas, specific countries, or create a global model. The models give us the possibility to predict the virus behavior, it could be used to make future response plans. This work presents an analysis of COVID-19 spread that shows a different angle for the whole world, through 6 geographic regions (continents). We propose to create a relationship between the countries, which are in the same geographical area to predict the advance of the virus. The countries in the same geographic region have variables with similar values (quantifiable and non-quantifiable), which affect the spread of the virus. We propose an algorithm to performed and evaluated the ARIMA model for 145 countries, which are distributed into 6 regions. Then, we construct a model for these regions using the ARIMA parameters, the population per 1M people, the number of cases, and polynomial functions. The proposal is able to predict the COVID-19 cases with a RMSE average of 144.81. The main outcome of this paper is showing a relation between COVID-19 behavior and population in a region, these results show us the opportunity to create more models to predict the COVID-19 behavior using variables as humidity, climate, culture, among others. [ABSTRACT FROM AUTHOR]

An analysis is presented of universal stages in the development of the COVID-19 virus infection epidemic. It is assumed that during a pandemic the growth rate in the number of infections in a country occurs similarly to the process of virion replication in an infected organism. The exponent of the growth rate of the number of infection cases reflects not only biomedical parameters of a virus infection but also distinctive features of the social behavior of the population of a country. The dynamics of the change in the growth rate exponents is simulated by a system of relaxation-type ordinary differential equations. In applied mathematics, based on the imbedding method, limit values are forecast for exponents of the growth rate of the number of cases using the currently available experimental data. These values "forecast ahead" are subsequently approached by the actual exponents of growth in the number of infectees. The search for unknown parameters is carried out by minimizing a specially constructed quadratic functional accounting for all confirmed cases of COVID-19 infection. The functional minimum is found via iterations by solving a system of ordinary differential equations. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) emerged in December 2019 and posed numerous challenges to China's health system. Almost 4 million primary care practitioners (PCPs) participated in controlling the outbreak. However, PCPs' barriers to and experience of the epidemic control remain unknown and are essential for improving countermeasures. Objective: To better understand the barriers PCPs faced in COVID-19 epidemic control and their psychological and occupational impacts, and explore potential solutions. Design: This qualitative study was conducted through semi-structured, in-depth interviews from February 12, to March 10, 2020. Participants: A purposive sample of frontline PCPs affiliated with either community health centers or township health centers in four provinces of China were recruited. Approach: Interviews were conducted by telephone, and then recorded, transcribed, and content analyzed. Themes surrounding PCPs' barriers to COVID-19 epidemic control, their experience, and potential solutions were iteratively identified using the constant comparative method. Key Results: Of the 21 PCPs interviewed, 10 (48%) were women and 5 (24%) worked in rural areas. Barriers to epidemic control in primary care included inappropriate PCP scheduling and role ambiguity, difficult tasks and inadequate capacities, and inexperienced community workers and insufficient cooperation. Some PCPs perceived respect and a sense of accomplishment and were preoccupied with the outbreak, while others were frustrated by fatigue and psychological distress. PCPs reported potential solutions for improving countermeasures, such as improving management, optimizing workflows, providing additional support, facilitating cooperation, and strengthening the primary care system. Conclusions: Due to their roles in controlling the COVID-19 epidemic, PCPs in China faced a series of barriers that affected them physically and mentally. Support for PCPs should help them to overcome these barriers and work efficiently. The current findings provide insight into the challenges and potential solutions for strengthening the preparedness and response of China's primary care system in future disease outbreaks. [ABSTRACT FROM AUTHOR]

The effectiveness of the national/regional healthcare system is one of the keys to prevent the spread of COVID-19. In the face of this unknown pandemic, where the healthcare system should continue to be promoted and improved are crucial decision issues. In the past, most studies have used the subjective opinions of experts for analysis and decision-making processes when investigating complicated decision-making problems. However, such decision-making processes are easily influenced by experts' preferences. Therefore, this research proposes a soft computing technology that integrates CRiteria Importance Through Intercriteria Correlation (CRITIC) with the modified VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, meaning multicriteria optimization and compromise solution (modified VIKOR) technique to reduce the impact of expert preference. In order to cope with the fact that COVID-19 has spread globally and to discover problems quickly and effectively, this study uses the global health security (GHS) index as the evaluation framework and conducts overall discussions in 195 countries/regions around the world. It is verified that the technology of soft computing can be used for continuous promotion and improvement of the national/regional healthcare system. This technology facilitates decision makers to know the gap of performance between the current healthcare system and the aspiration level. Finally, based on these gaps, we provide management advice to help improve these systems. [ABSTRACT FROM AUTHOR]

Background: Since the first outbreak of SARS-CoV-2, the clinical characteristics of the Coronavirus Disease 2019 (COVID-19) have been progressively changed. Data reporting a viral intra-host and inter-host evolution favouring the appearance of mild SARS-CoV-2 strains are since being accumulating. To better understand the evolution of SARS-CoV-2 pathogenicity and its adaptation to the host, it is therefore crucial to investigate the genetic and phenotypic characteristics of SARS-CoV-2 strains circulating lately in the epidemic.Methods: Nasopharyngeal swabs have been analyzed for viral load in the early (March 2020) and late (May 2020) phases of epidemic in Brescia, Italy. Isolation of SARS-CoV-2 from 2 high viral load specimens identified on March 9 (AP66) and on May 8 (GZ69) was performed on Vero E6 cells. Amount of virus released was assessed by quantitative PCR. Genotypic characterization of AP66 and GZ69 was performed by next generation sequencing followed by an in-depth in silico analysis of nucleotide mutations.Results: The SARS-CoV-2 GZ69 strain, isolated in May from an asymptomatic healthcare worker, showed an unprecedented capability of replication in Vero E6 cells in the absence of any evident cytopathic effect. Vero E6 subculturing, up to passage 4, showed that SARS-CoV-2 GZ69 infection was as productive as the one sustained by the cytopathic strain AP66. Whole genome sequencing of the persistently replicating SARS-CoV-2 GZ69 has shown that this strain differs from the early AP66 variant in 9 nucleotide positions (C2939T; C3828T; G21784T; T21846C; T24631C; G28881A; G28882A; G28883C; G29810T) which lead to 6 non-synonymous substitutions spanning on ORF1ab (P892S; S1188L), S (K74N; I95T) and N (R203K, G204R) proteins.Conclusions: Identification of the peculiar SARS-CoV-2 GZ69 strain in the late Italian epidemic highlights the need to better characterize viral variants circulating among asymptomatic or paucisymptomatic individuals. The current approach could unravel the ways for future studies aimed at analyzing the selection process which favours viral mutations in the human host. [ABSTRACT FROM AUTHOR]