Current perspective of COVID-19 spread across South Korea: exploratory data analysis and containment of the pandemic

Embitterment in Vietnam War Veterans Predicted by Symptoms of Posttraumatic Stress Disorder

OBJECTIVE

Research on the association between posttraumatic embitterment disorder (PTED) and other psychopathologies in veterans and adults aged ≥65 years is lacking. This study aimed to assess embitterment among elderly war veterans and its association with major psychopathological factors.

METHODS

Participants included Vietnam War veterans who visited a psychiatric clinic. Based on the Posttraumatic Embitterment Disorder Self-Rating Scale (PTEDS) score, the participants were divided into the embitterment (PTED(+), mean score of PTEDS items [mPTEDS] ≥1.6) and non-embitterment (PTED(-), mPTEDS <1.6) groups. Demographic characteristics, combat exposure severity, depression, anxiety, sleep, and alcohol use disorder symptom scores of the participants were collected and compared between the PTED(+) and PTED(-) groups. A correlation analysis between symptom measure scores and the mPTEDS was conducted. The influence of psychopathology on embitterment was investigated using stepwise multiple linear regression analysis.

RESULTS

In total, 60 participants (28 in PTED(+) and 32 in PTED(-)) were included. Among those in PTED(+), 21 (35.0%) showed mild embitterment symptoms (1.6≤ mPTEDS <2.5) and 7 (11.7%) reported moderate or severe embitterment symptoms (mPTEDS ≥2.5). The mean scores of posttraumatic stress disorder (PTSD), depression, and anxiety were significantly higher in the PTED(+) than in the PTED(-) group. The mPTEDS were significantly correlated with PTSD, depression, anxiety, and sleep disorder scores. The PTSD symptoms significantly explained the higher mPTEDS score in a regression model.

CONCLUSION

Embitterment symptoms were associated with PTSD, depression, anxiety, and insomnia symptoms in elderly veterans, similar to the results of prior studies involving only the general population.

Informed Random Forest to Model Associations of Epidemiological Priors, Government Policies, and Public Mobility

Background. Infectious diseases constitute a significant concern worldwide due to their increasing prevalence, associated health risks, and the socioeconomic costs. Machine learning (ML) models and epidemic models formulated using deterministic differential equations are the most dominant tools for analyzing and modeling the transmission of infectious diseases. However, ML models can be inconsistent in extracting the dynamics of a disease in the presence of data drifts. Likewise, the capability of epidemic models is constrained to parameter dimensions and estimation. We aimed at creating a framework of informed ML that integrates a random forest (RF) with an adapted susceptible infectious recovered (SIR) model to account for accuracy and consistency in stochasticity within the dynamics of coronavirus disease 2019 (COVID-19). Methods. An adapted SIR model was used to inform a default RF on predicting new COVID-19 cases (NCCs) at given intervals. We validated the performance of the informed RF (IRF) using real data. We used Botswana's pharmaceutical interventions (PIs) and non-PIs (NPIs) adopted between February 2020 and August 2022. The discrepancy between predictions and observations is modeled using loss functions, which are minimized, interpreted, and used to assess the IRF. Results. The findings on the real data have revealed the effectiveness of the default RF in modeling and predicting NCCs. The use of the effective reproductive rate to inform the RF yielded an excellent predictive power (84%) compared with 75% by the default RF. Conclusion. This research has potential to inform policy and decision makers in developing systems to evaluate interventions for infectious diseases.

Highlights

This framework is initiated by incorporating model outputs from an epidemic model to a machine learning model.An informed random forest (RF) is instantiated to model government and public responses to the COVID-19 pandemic.This framework does not require data transformations, and the epidemic model is shown to boost the RF's performance.This is a baseline knowledge-informed learning framework for assessing public health interventions in Botswana.

Pollution free UV-C radiation to mitigate COVID-19 transmission

The high rate of transmission of the COVID-19 virus has brought various types of disinfection techniques, for instance, hydrogen peroxide vaporization, microwave generating steam, UV radiation, and dry heating, etc. to prevent the further transmission of the virus. The chemical-based techniques are predominantly used for sanitization of hands, buildings, hospitals, etc. However, these chemicals may affect the health of humans and the environment in unexplored aspects. Furthermore, the UV lamp-based radiation sanitization technique had been applied but has not gained larger acceptability owing to its limitation to penetrate different materials. Therefore, the optical properties of materials are especially important for the utilization of UV light on such disinfection applications. The germicidal or microorganism inactivation application of UV-C has only been in-use in a closed chamber, due to its harmful effect on human skin and the eye. However, it is essential to optimize UV for its use in an open environment for a larger benefit to mitigate the virus spread. In view of this, far UV-C (222 nm) based technology has emerged as a potential option for the sanitization in open areas and degradation of microorganisms present in aerosol during the working conditions. Hence, in the present review article, efforts have been made to evaluate the technical aspects of UV (under the different spectrum and wavelength ranges) and the control of COVID 19 virus spread in the atmosphere including the possibilities of the human body sanitization in working condition.

Understanding small-scale COVID-19 transmission dynamics with the Granger causality test

Mobility patterns have been broadly studied and deeply altered due to the coronavirus disease (COVID-19). In this paper, we study small-scale COVID-19 transmission dynamics in the city of Valencia and the potential role of subway stations and healthcare facilities in this transmission. A total of 2,398 adult patients were included in the analysis. We study the temporal evolution of the pandemic during the first six months at a small-area level. Two Voronoi segmentations of the city (based on the location of subway stations and healthcare facilities) have been considered, and we have applied the Granger causality test at the Voronoi cell level, considering both divisions of the study area. Considering the output of this approach, the so-called 'donor stations' are subway stations that have sent more connections than they have received and are mainly located in interchanger stations. The transmission in primary healthcare facilities showed a heterogeneous pattern. Given that subway interchange stations receive many cases from other regions of the city, implementing isolation measures in these areas might be beneficial for the reduction of transmission.

Adapting and Validating the COVID-19 Vaccine Hesitancy and Vaccine Conspiracy Beliefs Scales in Korea

The coronavirus (COVID-19) pandemic has emphasized a need to assess the cause of vaccine hesitancy. This study verified the reliability and validity of the Korean versions of the COVID-19 vaccine hesitancy scale and vaccine conspiracy belief scale and the correlation between them. The COVID-19 vaccine hesitancy scale, Korean COVID-19 vaccine hesitancy scale, vaccine conspiracy beliefs scale, and self-efficacy scale were the study tools. Following translation into Korean, back translation into English, content validity verification, and preliminary survey, valid samples were obtained from 400 adults aged >20 years. Exploratory factor analysis revealed that “belief” and “lack of trust” accounted for 62.4% of the total variance. The model fit index of the vaccine conspiracy beliefs scale revealed that all values were in a good range. The Korean version of the COVID-19 vaccine hesitancy scale showed a positive correlation with vaccine conspiracy beliefs (r = 0.74, p < 0.001) and a significant negative correlation with self-efficacy (r = −0.17, p < 0.001). The validity and reliability of the COVID-19 vaccine hesitancy scale and vaccine conspiracy beliefs scale were verified. The Korean versions of the two scales can contribute to programs that measure and mediate various factors influencing vaccination during the COVID-19 pandemic.

An International Comparative Study on the Resilience of Urban Communities after COVID-19 Pandemic: A One-Year Case Study between Lanzhou, China and Sarajevo, Bosnia and Herzegovina

After the prevailing of the COVID-19 pandemic, urban communities around the world took initiatives to bring their cities back to life. In this research, 45 indicators and 55 elements were selected to make comparisons between urban communities in Lanzhou, China and Sarajevo, Bosnia and Herzegovina from five dimensions of social resilience, economic resilience, institutional resilience, infrastructural resilience, and community capital resilience. At the same time, the ArcGIS platform tool was used for spatial interpolation analysis. In this paper, the inverse distance weighting (IDW) method was used to carry out the spatial analysis of the perceived resilience of the two cities. Due to the heterogeneity of the neighborhood physical environment, operation and management mode, individual attribute characteristics, and internal relations, the resilience of the two urban communities showed disparity in different dimensions. Overall, the communities with good urban property management services, high-income owners, and the convenient transportation have stronger resilience in the face of pandemic. On the contrary, scattered communities, which are scattered in the inner cities, lack effective management, and based on unstable employment, people become the most affected by the epidemic with the lowest resilience power. The importance of social capital, represented by community understanding, identity, and mutual help and cooperation between neighbors, is highlighted in the resilience assessment of the two cities, respectively, in the East and West, indicating that to build more resilient cities, in addition to improving government management and increasing investment in urban infrastructure, building the residents' sense of belonging, identity, and enduring community culture is even more important in the construction of resilient cities.

Air pollution in five Indian megacities during the Christmas and New Year celebration amidst COVID-19 pandemic

Urban air quality and COVID-19 have been considered significant issues worldwide in the last few years. The current study highlighted the variation in air pollutants (i.e., PM2.5, PM10, NO2, and SO2) profile between Christmas and new year celebrations in 2019, 2020, and 2021. It can be seen that the concentration of selected air pollutants shows a substantially higher concentration in celebration periods in all reported years. The results indicate that air pollutants values are always higher than permissible limits. This observation indicates that people gather and reunite during Christmas and new year celebrations than the preceding years (2020 and 2021) amidst the pandemic. In the pandemic year, a higher margin enhanced the transportation and firework-induced air pollutant load in urban city Jodhpur, Rajasthan. In all states, a significant tendency was observed to retain the concentration profile of air pollutants in baseline concentration for almost more than one week after the celebration. This study addresses the pandemic situation, but it also dealt with the air pollutant parameter that brings down the sustainable quality of the environment due to the high usage of private vehicles, and crackers. In addition, a study on COVID-19 (cases and death rate) indicates a clear picture of the increasing trend after the event in probably all states. Thus, this approach suggested that stringent law enforcement is needed to ameliorate gatherings/reunions and pollution levels due to such events.

Analyzing and visualizing global research trends on COVID-19 linked to sustainable development goals

This study is intended to afford a comprehensive overview of the implications of COVID-19 on progress toward achieving the sustainable development goals (SDGs) set out in the United Nations (UN) 2030 Agenda and the state of related research activities on COVID-19 linked to the SDGs. Bibliometric techniques and visual mapping are proposed as methodological tools to better approach the objectives of the present work. This includes: retrieving related publications from Scopus database, investigating the trends and growth trajectories of research works, and analyzing the scenarios post-COVID-19 either optimistic or pessimistic outlooks. The national and international contributions and collaboration toward this theme of research are further analyzed at countries, institutions, and sources levels. This analysis indicates that research works conducted on the impacts of COVID-19 on the achievement of the SDGs are still in the immaturity level. The global research productivity on this topic was just 160 documents (0.19% of total global research productivity in all fields of science with relevance to COVID-19). The implications of COVID-19 on good health and well-being, SDG-3, have attracted considerable attention. It is followed by SDG-13 that concerned with climate changes. The post-COVID-19 scenarios showed deep and justified worries in relation to achieving the SDGs by 2030. This study figures the major issues debated in the literature with respect to COVID-19 and its implications on the SDGs. The study, furthermore, attempts to assess the required actions to advance the SDGs post-COVID-19.

Organizational resilience and social-economic sustainability: COVID-19 perspective

COVID-19 has affected the global economy like no other crisis in the history of mankind. It forced worldwide lockdown and economic shutdown to the point from where the recovery process has been very difficult. It has affected demand, supply, production and consumption in such a way that the entire economic development cycle has gone to its lowest levels. COVID-19 has also affected the social and economic sustainability structure which has led from one crisis to another and the developing countries have been the worst hit. Economic crisis resulted in unemployment leading to labour migrations, inevitable casualties and rising poverty etc. However, at a certain level, a few industries and organizations have shown resilience with better anticipation and survivability which may lead them to a quicker recovery. The current study aims at presenting a holistic view of organizational resilience which leads to the overall sustainable development. The study considers three aspects of organizational resilience as crisis anticipation, organizational robustness and recoverability. It assesses the impact of the aspects of resilience on social sustainability and economic sustainability. The study uses empirical analysis of primary data which is analysed to verify the hypothesized relationships by using a structural equation modelling approach. The study finds out that predicting the crisis and disruptions, building robustness and recoverability have a positive effect on both the social and economic aspects of sustainability. Findings of the study have their practical implications for industry, researchers and society.

Statistical interpretation of environmental influencing parameters on COVID-19 during the lockdown in Delhi, India

The novel coronavirus disease is known as COVID-19, which is declared as a pandemic by the World Health Organization during March 2020. In this study, the COVID-19 connection with various weather parameters like temperature, wind speed, and relative humidity is investigated and the future scenario of COVID-19 is predicted based on the Gaussian model (GM). This study is conducted in Delhi, the capital city of India, during the lowest mobility rate due to strict lockdown nationwide for about two months from March 15 to May 17, 2020. Spearman correlation is applied to obtain the interconnection of COVID-19 cases with weather parameters. Based on statistical analysis, this has been observed that the temperature parameter shows a significant positive trend during the period of study. The number of confirmed cases of COVID-19 is fitted with respect to the number of days by using the Gaussian curve and it is estimated on the basis of the model that maximum cases will go up to 123,886 in number. The maximum number of cases will be observed during the range of 166 ± 36 days. It is also estimated by using the width of the fitted GM that it will take minimum of 10 months for the complete recovery from COVID-19. Additionally, the linear regression technique is used to find the trend of COVID-19 cases with temperature and it is estimated that with an increase in temperature by 1 °C, 30 new COVID-19 cases on daily basis will be expected to observe. This study is believed to be a preliminary study and to better understand the concrete relationship of coronavirus, at least one complete cycle is essential to investigate. The laboratory-based study is essential to be done to support the present field-based study. Henceforth, based on preliminary studies, significant inputs are put forth to the research community and government to formulate thoughtful strategies like medical facilities such as ventilators, beds, testing centers, quarantine centers, etc., to curb the effects of COVID-19.

Strong link between coronavirus count and bad air: a case study of India

The present study aims to highlight the contrast relationship between COVID-19 (Coronavirus Disease-2019) infections and air pollutants for the Indian region. The COVID-19 data (cumulative, confirmed cases and deaths), air pollutants (PM₁₀, PM_2.5, NO₂ and SO₂) and meteorological data (temperature and relative humidity) were collected from January 2020 to August 2020 for all 28 states and the union territory of India during the pandemic. Now, to understand the relationship between air pollutant concentration, meteorological factor, and COVID-19 cases, the nonparametric Spearman's and Kendall's rank correlation were used. The COVID-19 shows a favourable temperature (0.55-0.79) and humidity (0.14-0.52) over the Indian region. The PM_2.5 and PM₁₀ gave a strong and negative correlation with COVID-19 cases in the range of 0.64-0.98. Similarly, the NO₂ shows a strong and negative correlation in the range of 0.64-0.98. Before the lockdown, the concentration of pollution parameters is high due to the shallow boundary layer height. But after lockdown, the overall reduction was reported up to 33.67% in air quality index (AQI). The background metrological parameters showed a crucial role in the variation of pollutant parameters (SO₂, NO₂, PM₁₀ and PM_2.5) and the COVID-19 infection with the economic aspects. The European Centre for Medium-Range Weather Forecasts derived monthly average wind speed was also plotted. It can see that January and February of 2020 show the least variation of air mass in the range of 1-2 m/s. The highest wind speed was reported during July and August 2020. India's western and southern parts experienced an air mass in the range of 4-8 m/s. The precipitation/wet deposition of atmospheric aerosols further improves the AQI over India. According to a study, the impact of relative humidity among all other metrological parameters is positively correlated with Cases and death. Outcomes of the proposed work had the aim of supporting national and state governance for healthcare policymakers.

Coronavirus disease-19 in environmental fields: a bibliometric and visualization mapping analysis

A coronavirus disease-19 (COVID-19) caused by a novel coronavirus has appeared in China by the end of 2019 and later recognized as a global pandemic. This pandemic has evolved as a global public health menace. It has affected every aspect of human life. In line with these concerns, governments and the scientific community react promptly to the outbreak of this pandemic. These efforts are devoted to develop vaccine and curative medicines. Further efforts are dedicated to assessing the impacts of the pandemic in relation to socioeconomic, psychological, and environmental dimensions. In this regard, it is important to follow up developments and research activities on this global issue. The present work intended to tracking the current hotspots and research trends on COVID-19 in environmental fields. Bibliometric analysis and visualization mapping were utilized with the objective of revealing and evaluating the developments in knowledge on COVID-2019 and its impacts based on a collection of environmental sources. A sum of 729 documents were collected from Scopus database limiting to environmental sources only. Of all these publications, 563 (77.2%) were articles, 56 (7.7%) were reviews, and 110 (15.0%) were others. China has the highest share of publications (163; 22.4%). It is followed by the USA (139; 19.15), and Italy (110; 15.1%). Most publications on COVID-19 were in prestigious journals. The most productive institution at global level was Chinese Academy of Sciences, China (24 documents; 3.3%). The most prevalent topics are in relation to impacts of the pandemic on air quality, mental health, psychological, and economic aspects. The development of these topics is based on cross-sectional studies, evidence-based tools, remote sensing, satellite mapping, geographic information systems, market analysis and sampling. The progress of environmental research on COVID-19 will guide the development of global environmental strategies to control future global environmental risks.

Largest democracy in the world crippled by COVID-19: current perspective and experience from India

The outbreak of novel and recent coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has made an emergency throughout the world. In India, the outspread of the pandemic was observed on 3 March 2020, and after that exponential growth in the cases was observed in the country. Owing to the widespread transmission, high population density, high testing capacity and ineffective treatment, a continuous rise in cases was observed due to the pandemic in India. In this paper, we have discussed the trend and spread of COVID-19 spread in India with time, history of initial confirmed cases, the impact of phased manner lockdown, age- and gender-wise trend of cases and comparison of cases with the other most affected countries. The study uses exploratory data analysis to describe the current situation of COVID-19 cases in India till 16 August 2020, with the help of data from the Ministry of Health and Family Welfare, Government of India (GOI) and the World Health Organization (WHO). As of August 16, the total number of confirmed cases in India crossed 2.5 million marks with over 50,000 causalities. With more patients recovering and being discharged from hospitals and home isolation (in case of mild and moderate cases), the total recoveries have crossed the 1.8 million mark with a recovery rate of more than 70% and case fatality rate of 1.94% which is maintained below the global average and is on a continuous positive slide. The study also enlightens the preventive and stringent measures taken by India to combat the COVID-19 situation along with the future prospects. The GOI is following its proactive and preemptive approach for management, prevention and containment of COVID-19 in collaboration with the WHO.