COVID-19 Lockdown Effects on a Highly Contaminated Coastal Site: The Mar Piccolo Basin of Taranto

The COVID-19 pandemic has had a dramatic socio-economic impact on mankind;however, the COVID-19 lockdown brought a drastic reduction of anthropic impacts on the environment worldwide, including the marine–coastal system. This study is concentrated on the Mar Piccolo basin of Taranto, a complex marine ecosystem model that is important in terms of ecological, social, and economic activities. Although many numerical studies have been conducted to investigate the features of the water fluxes in the Mar Piccolo basin, this is the first study conducted in order to link meteo-oceanographic conditions, water quality, and potential reduction of anthropic inputs. In particular, we used the model results in order to study the response of the Mar Piccolo basin to a drastic reduction in the leakage of heavy metal IPAs from industrial discharges during the two months of the mandated nationwide lockdown. The results show the different behavior of the two sub-basins of Mar Piccolo, showing the different times necessary for a reduction in the concentrations of heavy metals even after a total stop in the leakage of heavy metal IPAs. The results highlight the high sensitivity of the basin to environmental problems and the different times necessary for the renewal of the water in both sub-basins. © 2023 by the authors.

Socioeconomic Impacts of Households' Vulnerability during COVID-19 Pandemic in South Africa: Application of Tobit and Probit Models

Coronavirus is a public health issue with socioeconomic and livelihood dimensions. The World Health Organization declared the current novel coronavirus disease (COVID-19) epidemic a public health emergency of international concern on January 30, 2020, and a global pandemic on March 11, 2020. The South African government has implemented different strategies, ranging from total lockdown in certain locations and provision of palliatives in some provinces across the country. This study, therefore, investigated the correlates of vulnerability and responsiveness to the adverse impacts of COVID-19 in South Africa. The study utilized primary data collected among 477 respondents. Descriptive statistical tools, Tobit and Probit regression models, were used to analyze the data. The study found different levels of vulnerability (low, medium, and high) and responsiveness among households, including stocking up of food items, remote working, reliance on palliatives, and social grant provision, among others. Some of the correlates of responsiveness to the COVID-19 pandemic include being employed, the type of community, and the income of respondents. The study, therefore, recommends increased investments in welfare programmes (safety nets, palliative measures and economic stimulus packages) as well as capacity building of households through education to reduce vulnerability. © Authors retain all copyrights.

Status of Industrial Complex Activity Explained by Air Quality: Central Thailand

Resilience in business continuity of an entire industrial complex has direct local socioeconomic impact;however, there are few methods available for objective assessment of its status. This study investigated whether change in air quality could explain the state of economic activity in an industrial complex. Concentrations of PM2.5 and NO2 above several industrial complexes in central Thailand were extracted using the Google Earth Engine™ and analyzed to examine their temporal characteristics in relation to decline in business activity caused by the COVID-19 pandemic. Results confirmed that industrial complexes whose activities were diminished by the pandemic showed concurrent trends of reduction in each pollutant, proving that the concentration of airborne substances has potential to reveal the level of activity of industrial complexes. To enhance the application potential of the proposed method, further study should investigate specific causal inferences by extracting the characteristics of other airborne substances, and consider industrial complexes that include a greater number of companies and major industries. © 2023 IEEE.

Immediate impacts of COVID-19 lockdown on personal mobility and consumer behaviour of households in Sri Lanka

Lockdown measures adopted to contain the Coronavirus (COVID-19) pandemic resulted in severe disruptions to mobility, both in demand and supply of passenger and goods transport and supply chain activities globally. This research was designed to understand the immediate reactions of households and society during the curfew imposed in Sri Lanka, a developing country, to curb the spread of the pandemic. This paper investigates psychometric perceptions across different socio-economic characteristics of households using statistical techniques to explore the association between the sample and population parameters. Results prominently indicate that these immediate impacts of health advisories and lockdown on personal mobility and consumption patterns were short-term in nature and unlikely to continue beyond the curfew. These results would be useful in understanding how society would deal with a similar unforeseen event in the future if it were to arise. © 2023 The Authors

Artificial intelligence framework for threat assessment and containment for covid-19 and future epidemics while mitigating the socioeconomic impact to women, children, and underprivileged groups

With the emergency situation that arises with COVID-19, the intense containment strategies adopted by many countries had little or no consideration towards socio-economic ramifications or the impact on women, children, socio­economically underprivileged groups. The existence of many adverse impacts raises questions on the approaches taken and demands proper analysis, scrutiny and review of the policies. Therefore, a framework was developed using the artificial intelligence (Al) techniques to detect, model, and predict the behaviour of the COVID-19 pandemic containment strategies, understanding the socio-economic impact of these strategies on identified diverse vulnerable groups, and the development of AI-based solutions, to predict and manage a future spread of COVID or similar infectious disease outbreaks while mitigating the social and economic toil. Based on generated behaviour and movements, Al tools were developed to conduct contact tracing and socio-economic impact mitigation actions in a more informed, socially conscious and responsible manner in the case of the next wave of COVID-19 infections or a different future infectious disease. © 2022, National Science Foundation. All rights reserved.

A mathematical analysis of the socio-economic impacts of a patent waiver on COVID-19 vaccines

We perform a calibrated mathematical analysis of the potential impacts of a patent waiver on COVID-19 vaccines. In the model, we schematically divide nations into high- and low-income, the latter accounting for 80% of the world population but currently using only 60% of the vaccine production. We show that a significant increase in vaccine production combined with a more equitable distribution - made possible by an intellectual property (IP) waiver - would have stopped the pandemic in 18 months of vaccination and saved more than ten million people, mostly in poor countries, compared with five years of the current scenario in which the virus becomes endemic. We hypothesize the peak rollout capacity shown by high-income countries at the beginning of the vaccination campaign and half of that capacity for low-income ones. We even show that the money saved on vaccines globally in the hypothetical IP-waiver scenario overcomes the actual value of the 5-yr profits of the big pharma in the current situation. This profit loss could be immediately covered (mostly by the expected saving of high-income countries) in exchange for the waiver. © 2022 IEEE.

EPROACH: A Population Vaccination Game for Strategic Information Design to Enable Responsible COVID Reopening

The COVID-19 lockdowns have created a significant socioeconomic impact on our society. In this paper, we propose a population vaccination game framework, called EPROACH, to design policies for reopenings that guarantee post-opening public health safety. In our framework, a population of players decides whether to vaccinate based on the public and private information they receive. The reopening is captured by the switching of the game state. The insights obtained from our framework include the appropriate vaccination coverage threshold for safe-reopening and information-based methods to incentivize individual vaccination decisions. In particular, our framework bridges the modeling of the strategic behaviors of the populations and the spreading of infectious diseases. This integration enables finding the threshold which guarantees a disease-free epidemic steady state under the population's Nash equilibrium vaccination decisions. The equilibrium vaccination decisions depend on the information received by the agents. It makes the steady-state epidemic severity controllable through information. We find that the externalities created by reopening lead to the coordination of the players in the population and result in a unique Nash equilibrium. We use numerical experiments to corroborate the results and illustrate the design of public information for responsible reopening. © 2022 American Automatic Control Council.

Analysis and Prognosis of Covid-19 using Machine Learning Algorithms and Visualization using Tableau

This paper uses prognosticative machine learning models that predict corona positives and deaths as a result of the crisis, and the recovery rate from the pandemic. This method aids in diagnosing the contours of an individual's presumption in data transmission based on medical knowledge and calculates the unfolding virus's socioeconomic impact. It examines the Covid-19's spread technique with the help of machine learning models. It also identifies the approaching prophecy and recessive presumption of the crisis at the same time, and as a result, this applicable analysis aids similar countries in making decisions. This paper also considers the global prevalence of the plague. Within the first phase of the irruption, eight supervised classification epidemiologic models are used to estimate the day-to-day and monomer incidents of coronavirus throughout the world, as well as the vital replica variety, growth rate, and increasing time. Calculations are also made for the more intricate efficacious replica variety, which reveals that since the predominant cases are confirmed to the specific countries, the severity has decreased. Machine learning models' prognosticative capabilities are found to provide an additional satisfactory match, and simple estimates of daily incidents around the world. © 2022 IEEE.

Leveraging Data Mining Strategies for Predictive Modeling of COVID-19 Outbreak and its Socio-economic Impacts: A Simulation for Public Health in Iran (Regular Paper)

We present a thorough analysis of socio-economic impacts of COVID-19 on public health through data mining strategies including correlation index matrix, auto-regressive integrated moving average, decision trees, heatmaps and statistical performance evaluation. We acquired and filtered data for mortality and outbreak prediction through key features such as total cases, daily new cases, active cases, total deaths, daily new deaths, newly recovered, death rate and recovery rate for 54 days. The socio-economic impacts of the pandemic through quantitative analysis of stock market index, currency inflation,gasoline prices, interest rate, consumer price index and crude oil prices were also investigated. With correlation index matrix and heatmaps, we discovered the nature and intensity of interdependency of these features and developed the regressive estimation model to forecast the values of inter-related features for 10 days. We observed a highest correlation of +0.95 between recovery rate and total infected cases. We also observed aninverse correlation of -0.81 between daily new cases and recovery rate due to unexpected rise in outbreak. Also, the mild but positive index for economic impacts, such as currency inflation,depict the virus' adverse impact on the fiscal situation. The statistical representation of the developed prediction models through bar charts show outstanding performance when evaluated on the benchmarking merits of mean absolute error, root mean square error, relative error and percentage accuracy.

Literature Review of Risk Communication on Public Perceptions in Responding to Covid-19 with a System Dynamic Approach

Gap of information regarding the risks of Covid-19 conveyed by the government to the public will result in various problems such as socio-economic impacts, loss of trust in the government, and even loss of life. The problem of communicating the risk of Covid-19 is complex because the problems that occur will be interrelated and affect each other. Different perceptions of society and the government in viewing the risk of Covid-19 can hinder the completion of Covid-19 cases. This problem should be seen as a system. Systems dynamics is a discipline that focuses on the research and analysis of information feedback systems. By modeling a risk communication strategy, this study aims to mapping research opportunity of Covid-19 risk communication problems related to soft variables and developing modes for their resolution by using system dynamic. © 2021 IEEE.

Self-Supervised Learning with Heterogeneous Graph Neural Network for COVID-19 Drug Recommendation

The emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have created an enormous socioeconomic impact. Although there are several promising drug candidates in clinical trials, none of them are approved yet. Thus, the drug repositioning approach may help to overcome the current pandemic. However, the sparse dataset of COVID-19 limits the accuracy of existing drug repositioning. To overcome this problem, we propose a novel drug repositioning framework (named Drug2Cov). Drug2Cov can learn an effective representation via integrating self-supervised learning with sparse data. Meanwhile, Drug2Cov uses a heterogeneous graph neural network to capture the complex interaction between viruses, targets, and drugs that enhance the accuracy of drug repositioning. The experimental results demonstrate the effectiveness and feasibility of our proposed Drug2Cov framework. Source code and dataset are freely available at https://github.com/lhf3291109/Drug2Cov. © 2021 IEEE.

Correlations of Shocks and Stresses with Distribution Network Outages

Distribution network outages have significant socioeconomic impacts, and potentially pose a threat to life when critical infrastructures are affected. Shocks and stresses, such as climate change, extreme weather or intentional attacks, in combination with an expected rise in electricity demand, pose an increasing risk to power network reliability. Detailed data on distribution network outages can be used for further research in prevention and mitigation of such outages. This paper presents and describes a unique and comprehensive dataset of UK distribution network outages. The dataset for 2020 is analyzed to identify correlations with shocks and stresses, such as demand, extreme weather, and COVID-19 lockdown. The results justify further research in prevention and mitigation of distribution network outages, support the industry in future planning of distribution networks, and can feed into models for operational planning in face of upcoming shocks and stresses. © 2021 IEEE.

Anxiety, Anger and Depression Amongst Low-Income Earners in Southwestern Uganda During the COVID-19 Total Lockdown.

Background: Low-income earners are particularly vulnerable to mental health, consequence of the coronavirus disease 2019 (COVID-19) lockdown restrictions, due to a temporary or permanent loss of income and livelihood, coupled with government-enforced measures of social distancing. This study evaluates the mental health status among low-income earners in southwestern Uganda during the first total COVID-19 lockdown in Uganda. Methods: A cross-sectional descriptive study was undertaken amongst earners whose income falls below the poverty threshold. Two hundred and fifty-three (n = 253) male and female low-income earners between the ages of 18 and 60 years of age were recruited to the study. Modified generalized anxiety disorder (GAD-7), Spielberger's State-Trait Anger Expression Inventory-2 (STAXI-2), and Beck Depression Inventory (BDI) tools as appropriate were used to assess anxiety, anger, and depression respectively among our respondents. Results: Severe anxiety (68.8%) followed by moderate depression (60.5%) and moderate anger (56.9%) were the most common mental health challenges experienced by low-income earners in Bushenyi district. Awareness of mental healthcare increased with the age of respondents in both males and females. A linear relationship was observed with age and depression (r = 0.154, P = 0.014) while positive correlations were observed between anxiety and anger (r = 0.254, P < 0.001); anxiety and depression (r = 0.153, P = 0.015) and anger and depression (r = 0.153, P = 0.015). Conclusion: The study shows the importance of mental health awareness in low resource settings during the current COVID-19 pandemic. Females were identified as persons at risk to mental depression, while anger was highest amongst young males.

Mass spectrometry analysis of human tear fluid biomarkers specific for ocular and systemic diseases in the context of 3P medicine.

Over the last two decades, a large number of non-communicable/chronic disorders reached an epidemic level on a global scale such as diabetes mellitus type 2, cardio-vascular disease, several types of malignancies, neurological and eye pathologies-all exerted system's enormous socio-economic burden to primary, secondary, and tertiary healthcare. The paradigm change from reactive to predictive, preventive, and personalized medicine (3PM/PPPM) has been declared as an essential transformation of the overall healthcare approach to benefit the patient and society at large. To this end, specific biomarker panels are instrumental for a cost-effective predictive approach of individualized prevention and treatments tailored to the person. The source of biomarkers is crucial for specificity and reliability of diagnostic tests and treatment targets. Furthermore, any diagnostic approach preferentially should be noninvasive to increase availability of the biomaterial, and to decrease risks of potential complications as well as concomitant costs. These requirements are clearly fulfilled by tear fluid, which represents a precious source of biomarker panels. The well-justified principle of a "sick eye in a sick body" makes comprehensive tear fluid biomarker profiling highly relevant not only for diagnostics of eye pathologies but also for prediction, prognosis, and treatment monitoring of systemic diseases. One prominent example is the Sicca syndrome linked to a cascade of severe complications that include dry eye, neurologic, and oncologic diseases. In this review, protein profiles in tear fluid are highlighted and corresponding biomarkers are exemplified for several relevant pathologies, including dry eye disease, diabetic retinopathy, cancers, and neurological disorders. Corresponding analytical approaches such as sample pre-processing, differential proteomics, electrophoretic techniques, high-performance liquid chromatography (HPLC), enzyme-linked immuno-sorbent assay (ELISA), microarrays, and mass spectrometry (MS) methodology are detailed. Consequently, we proposed the overall strategies based on the tear fluid biomarkers application for 3P medicine practice. In the context of 3P medicine, tear fluid analytical pathways are considered to predict disease development, to target preventive measures, and to create treatment algorithms tailored to individual patient profiles.

Knowledge, practice, and economic impacts of COVID-19 on small-scale coastal fishing communities in Bangladesh: Policy recommendations for improved livelihoods.

COVID-19 has profoundly impacted global livelihoods and disrupted the food supply chain, including the aquaculture and fisheries industries. Little is known about the response to COVID-19 and the impact it has on incomes, livelihoods and knowledge and practice in the coastal artisanal fishers communities of Bangladesh. Therefore, the aim of this study was to determine the socio-demographics of selected coastal fishers, their knowledge about COVID-19 and the preventive practice taken to reduce it. The impact on their fishing habits and income was also examined to determine potential policy areas. Data were collected via a structured questionnaire from 250 respondents from three coastal districts, Cox's Bazar, Patuakhali and Barguna, Bangladesh during April-June 2020. The research shows that the fishers' knowledge about COVID-19 and measures taken to reduce it were significantly higher in Patuakhali and Barguna than in Cox's Bazar. The pandemic caused lower consumer demand, reduced fish prices and created fish transportation issues due to movement restrictions enforced during the lockdown. Irrespective of geographical location, fishing trips were reduced by frequency and duration compared with the pre-COVID-19 period, consequently lowering the income of fishers. Fishers have received little or no support from private, non-governmental or governmental sources. Considering the evidence in this paper of economic hardship, this paper recommends artisanal fishers in Bangladesh should be provided with support to improve their health education, access to professional health facilities and financial services. This will contribute to improved food security and sustainable livelihoods that can better withstand local and/or global crises.

A detailed review of the outbreak of COVID-19.

The disease COVID-19 caused by SARS-CoV-2 is the third highly infectious human Coronavirus epidemic in the 21s⁢t century due to its high transmission rate and quick evolution of its pathogenicity. Genomic studies indicate that it is zoonotic from bats. The COVID-19 has led to significant loss of lives and a tremendous economic decline in the world. Generally, the population at risk of a fatal outcome are the elderly and those who are debilitated or are immune compromised. The fatality rate is high, but now is reduced after the development of preventive vaccine although an effective treatment by drug against the virus is yet to be developed. The treatment is narrowed to the use of several anti-viral drugs, or other re-purposed drugs. Social distancing, therefore, has emerged as a putative method to decrease the rate of infection. In this review, we summarize the aspects of the disease that is so far have come to light and review the impact of the infection on our society, healthcare, economy, education, and environment.

The socio-economic impacts of Covid-19 restrictions: Data from the coastal city of Mombasa, Kenya.

The novel corona virus disease (Covid-19) outbreak has caused great uncertainty in all spheres of human life. The experience has been incredibly humbling given that no country or section of society, regardless of its wealth or status, has been spared. The pandemic is not only a health crisis, but is also having serious damaging effects on societies, economies and vulnerable groups. Timely response is necessary in order to alleviate human suffering and to prevent irreversible destruction of livelihoods. This paper provides preliminary data on the socio-economic impacts of Covid-19 in the coastal city of Mombasa, Kenya, at the time of government-imposed curfews and cessation of movement. We conducted online surveys for two weeks during the restrictions period. The data was collected using online questionnaires directed at the city residents. The data highlights the mobile gender gap resulting from gender inequalities, residents' reliance on the government for Covid-19 information but lack of trust for government interventions, inadequate provisions of essential services, and the residents' lack of preparedness to tackle similar challenges in the future.