Coronavirus Cases Research Articles

Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model

The traditional Back-propagation algorithm has several weaknesses, including long training times and significant iterations to achieve convergence. This study aims to optimize traditional Back-propagation using the cyclical rule method to cover these weaknesses. Optimization is done by changing the training function and standard Back-propagation parameters using the training function and cyclical rule parameters. After that, a comparison of the two results will be carried out. This study uses quantitative method of time-series data on coronavirus cases sourced from the Worldometer website, then analyzed using three forecasting models with five input layers, one hidden layer (5, 10, and 15 neurons) and one output layer. The results showed that the 5-10-1 model with the training function and cyclical rule parameters and the tansig and purelin activation functions could perform well in optimization, including faster training time and smaller iterations (epochs), MSE training performance, and better tests. Low and high accuracy (92%) with an error rate of 0.01. So it was concluded that the training function and cyclical rule parameters with the tansig and purelin activation functions were able to optimize the traditional Back-propagation method, and the 5-10-1 model could be used for forecasting active cases of the coronavirus in Asia

COVID-19 Pandemic and the Performance of Listed Securities: Evidence from the Nairobi Securities Exchange in Kenya

The Novel Covid-19 disease led to panic that resulted to volatility of stock prices across the securities exchange in the world. This saw foreign investors liquidating their positions for fear of a market crash in Nairobi Securities exchange (NSE). The specific objective of this research was to find out whether Covid-19 pandemic had an effect on the performance of listed securities at the NSE in Kenya. Daily data for 5 months, 154 days since the period 13th March 2020 to 14 August 2020 was utilized. The study applied linear regression analysis model to find out the effect of Covid-19 daily cases on the performance of listed securities at the NSE in Kenya. The study findings revealed that Covid-19 daily cases and weather changes had a negative and statistically significant effect on the performance of listed securities at the NSE. The results inferred that Covid-19 daily cases affected the performance of listed stocks at the NSE since the announcement of the first Coronavirus cases in Kenya. The findings from this study are useful for policy implications as it provides guidance to policy makers to institute applicable policy reactions in line with the course of current coronavirus pandemic or during future pandemics of similar nature.

The Conundrum of Corruption During a Coronavirus Lockdown in Zimbabwe: Lessons for Social Work

ABSTRACT Faced with the serious threat of the deadly coronavirus, governments of different nations were swift to respond to the pandemic by declaring national lockdowns. Having confirmed fewer than ten cases of coronavirus that had tested positive, Zimbabwe called for a national lockdown which initially lasted three weeks before declaring it ‘indefinite’. Despite the fact that the lockdown declared in Zimbabwe was in the interest of public health, anecdotal evidence indicates that there has been an inextricable nexus between the lockdown and increased corrupt tendencies. This paper, which is a product of qualitative research, utilised a narrative research approach and documentary analysis to explicate the relationship between Zimbabwe’s coronavirus lockdown and corruption. The paper looks at the different forms of corruption that occurred during the coronavirus lockdown in Zimbabwe and the implication of these on the well-being and welfare of economically vulnerable groups. In conclusion, the paper indicates the role that can be played by pro-human rights professionals such as social workers in the fight against corruption during emergency times to ensure transparency and accountability.

An Overview of Repurposed Drugs for Potential COVID-19 Treatment.

The COVID-19 pandemic caused by SARS-CoV-2 has placed severe constraints on healthcare systems around the globe. The SARS-CoV-2 virus has caused upheaval in the healthcare and economic sectors worldwide. On the 20th of May 2020, the World Health Organisation declared COVID-19 a global pandemic due to the unprecedented number of cases reported around the globe. As of the 4th of November 2022, there were 637,117,429 coronavirus cases reported globally by Worldometer stats, with 6,602,572 related deaths. In South Africa, there were approximately 4,029,496 coronavirus cases and 102,311 associated deaths. As such, there is a need for efficacious therapeutic regimes. There has been a paucity of knowledge encompassing the use of effective and specific antiviral drug therapies for treating COVID-19 since the outbreak. In this review, we provide valuable insights into the repurposing of current drugs for COVID-19. Drug repurposing provides a suitable option for the discovery of efficacious drugs for COVID-19, thereby decreasing the costs and turnaround times of drug development strategies. This review provides an overview of ten drugs, including antimalarial, antiparasitic, anti-inflammatory, nucleoside analogue, monoclonal-antibody drugs, that were repurposed for the potential treatment of COVID-19.

A Mathematical Model of Vaccinations Using New Fractional Order Derivative.

Purpose: This paper studies a simple SVIR (susceptible, vaccinated, infected, recovered) type of model to investigate the coronavirus’s dynamics in Saudi Arabia with the recent cases of the coronavirus. Our purpose is to investigate coronavirus cases in Saudi Arabia and to predict the early eliminations as well as future case predictions. The impact of vaccinations on COVID-19 is also analyzed. Methods: We consider the recently introduced fractional derivative known as the generalized Hattaf fractional derivative to extend our COVID-19 model. To obtain the fitted and estimated values of the parameters, we consider the nonlinear least square fitting method. We present the numerical scheme using the newly introduced fractional operator for the graphical solution of the generalized fractional differential equation in the sense of the Hattaf fractional derivative. Mathematical as well as numerical aspects of the model are investigated. Results: The local stability of the model at disease-free equilibrium is shown. Further, we consider real cases from Saudi Arabia since 1 May−4 August 2022, to parameterize the model and obtain the basic reproduction number R0v≈2.92. Further, we find the equilibrium point of the endemic state and observe the possibility of the backward bifurcation for the model and present their results. We present the global stability of the model at the endemic case, which we found to be globally asymptotically stable when R0v>1. Conclusion: The simulation results using the recently introduced scheme are obtained and discussed in detail. We present graphical results with different fractional orders and found that when the order is decreased, the number of cases decreases. The sensitive parameters indicate that future infected cases decrease faster if face masks, social distancing, vaccination, etc., are effective.

Sistem Informasi Covid-19 Berbasis Mobile Dengan Framework Flutter dan Application Programming Interface (API)

The corona virus (COVID-19) has been happen for long time on the world, recently there is a new variant, namely omicron, which is rapidly spreading throughout the world, especially in Indonesia. The lack and difficulty to creating of spreading data of corona virus, it makes difficulty to know the information of corona virus cases for people especially for people who want doing kinds of trip. This research uses Information system of Covid-19 to plan the flutter framework and uses APIs to be able to solve these problems. The implementation of Flutter and API can easily collaborate to exchange the data in JSON format, so that data of covid-19 cases and their problems can be handled in the information system.

#FleeingWuhan: Legitimation and Delegitimation Strategies in Hostile Online Discourse

AbstractFollowing the first coronavirus case reported to the World Health Organization in Wuhan in 2019 and the ensuing city-wide lockdown that was imposed, many people attempted to leave the city, culminating in a vigorous discourse on the dominant Chinese microblogging site, Weibo. This study seeks to examine how online participants discursively delegitimated and legitimated people who left Wuhan before the lockdown. Weibo posts with the hashtag #逃离武汉 (‘Fleeing Wuhan’) were collected, and delegitimation and legitimation strategies deployed by users were identified. My findings reveal that the delegitimators exploited moral evaluation and impersonal authority to highlight the construed unethicality and shamelessness of people who left Wuhan, whereas the legitimators used an array of strategies, including explanation and definition, to normalize their intentions and counter linguistic hostility. These findings also provide implications vis-à-vis the clustering of delegitimation strategies as well as their linkages with emotional appeals in online discourse.

Impact of cross-border-associated cases on the SARS-CoV-2 epidemic in Switzerland during summer 2020 and 2021

During the summers of 2020 and 2021, the number of confirmed cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Switzerland remained at relatively low levels, but grew steadily over time. It remains unclear to what extent epidemic growth during these periods was a result of the relaxation of local control measures or increased traveling and subsequent importation of cases. A better understanding of the role of cross-border-associated cases (imports) on the local epidemic dynamics will help to inform future surveillance strategies.We analyzed routine surveillance data of confirmed cases of SARS-CoV-2 in Switzerland from 1 June to 30 September 2020 and 2021. We used a stochastic branching process model that accounts for superspreading of SARS-CoV-2 to simulate epidemic trajectories in absence and in presence of imports during summer 2020 and 2021.The Swiss Federal Office of Public Health reported 22,919 and 145,840 confirmed cases of SARS-CoV-2 from 1 June to 30 September 2020 and 2021, respectively. Among cases with known place of exposure, 27% (3,276 of 12,088) and 25% (1,110 of 4,368) reported an exposure abroad in 2020 and 2021, respectively. Without considering the impact of imported cases, the steady growth of confirmed cases during summer periods would be consistent with a value of Re that is significantly above the critical threshold of 1. In contrast, we estimated Re at 0.84 (95% credible interval, CrI: 0.78–0.90) in 2020 and 0.82 (95% CrI: 0.74–0.90) in 2021 when imported cases were taken into account, indicating that the local Re was below the critical threshold of 1 during summer.In Switzerland, cross-border-associated SARS-CoV-2 cases had a considerable impact on the local transmission dynamics and can explain the steady growth of the epidemic during the summers of 2020 and 2021.

Plasma exchange and COVID 19

Plasma exchange and COVID 19

Life cycle of COVID-19 Progression in a Given Region

The study focuses on revealing the typical patterns of COVID-19 spread. The identification of regularities is based on conceptual processing data, in which a sample is approximated by model functions expressing a specific phenomenological concept of the process in question. The reconstruction of desired parameters is hindered by insufficient information on the growth features, the limited volume of the required experimental data, and incomplete elucidation of the phenomenological properties of the objects required to formulate complex models. The investigation addresses these limitations using the Verhulst equation, which expresses the general factors of object growth and adequately describes the growth dynamics in the context of significant theoretical uncertainties regarding an object under study. The need for identifiability and numerical stability of the model parameter estimates is also addressed, and a special regularization approach is applied to obtain an adequate and stable picture of the virus’s evolutionary dynamics in the context of significant uncertainties. The results demonstrate that the growth of the viral population can be integrally described by two variable parameters of the Verhulst equation. Interpreting the reconstructed functions indicates the existence of the typical pattern of seven stages of COVID-19 spread. The possibility of radically managing the number of infected persons is established. The proposed processing of coronavirus cases is of practical value for the preclinical diagnosis and control of virus spread in any geographic area, such as a district, city, local region, or country.

Covid-19 and Serbian Stock Market Response

In this paper, the authors attempted to explore the relationship between the Covid-19 Coronavirus pandemic and the stock market in the Republic of Serbia. The main research variables on the stock market are the daily values of the Belex 15 and Belex Line indices and trading volume. For the pandemic variables, official daily data on the number of new Covid-19 cases in Serbia, Europe, and the world were taken. By applying the panel regression analysis for the period from 03/06/2020 to 12/30/2021, the empirical results show a positive and significant influence of the number of daily infected in Serbia and Europe on the stock market index value, At the same time, the influence of daily infected ones on trading volume is negative but statistically significant only when it comes to the new cases of coronavirus per day at the level of Europe. The presented results indicate the resilience of the Serbian capital market to internal and external shocks.

Analysis of Convolution neural network algorithms for Classification of Covid-19 Computed Tomography Images

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec.2019. TheCOVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affectedevery aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deathsstill increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from atotal number of around 13.1 million positive cases, 571, 527 deaths were reported in the world.Motivatedby recent advances and applications of artificial intelligence (AI) and big data in various areas, this paperaims at emphasizing their importance in responding to the COVID-19 outbreak and preventingthe severeeffects of the COVID-19 pandemic. We firstly present an overview of AI and bigdata and then identify theapplications aimed at fighting against COVID-19, next highlight challenges and issuesassociated withstate-of-the-art solutions, and finally come up with recommendations for the communications to effectivelycontrol the COVID-19 situation. It is expected that this paper provides researchers and communities withnew insights into the ways AI and big data improve the COVID-19 situation, and drives further studies instopping the COVID-19 outbreak.

Knowledge about COVID-19 and Associated Factors Early in the Outbreak among the Brazilian Population.

(1) Background: In Brazil, the first case of the novel coronavirus occurred on the 25 February 2020, and since then, it has spread rapidly over the entire country. During a pandemic, knowledge, attitudes, and practices are expected to largely influence the adherence to non-pharmacological interventions (NPIs). We evaluated the knowledge about COVID-19 and associated factors early in the outbreak among the Brazilian population. (2) Methods: A Brazilian cross-sectional study was carried out using an online questionnaire. The questionnaire consisted of the following topics: isolation, caring for someone sick at home, cleaning habits, disinfecting habits, and true and fake news. Logistic regression was conducted using sociodemographic and associated factors as the independent variables and a knowledge score as the dependent variable to estimate factors associated with knowledge about COVID-19. Crude, sex-, and age-adjusted odds ratios (OR) were calculated. (3) Results: Participants with a better educational status had higher odds of having a higher knowledge score (OR = 2.49, 95% CI = 1.15-5.37). Similarly, healthcare providers (health students and professionals) had higher odds of having higher scores regarding knowledge about COVID-19 (OR = 1.62, 95% CI = 1.05-2.48) than other counterparts. Of the wrong answers, the most frequent was the isolation period, followed by household recommendations to prevent COVID-19 and cleaning habits. (4) Conclusions: In conclusion, our study suggests that a higher educational status and being a healthcare provider are conditions associated with superior knowledge about COVID-19. In addition, inadequate knowledge related to isolation, COVID-19 prevention, and cleaning habits were found in our study. We believe that improving awareness to address these specific COVID-19 issues through a health education campaign is a significant approach for public health policymakers to fight against COVID-19 in Brazil.

Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread

Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible–Infectious–Recovered (SIR) models are traditionally used to forecast the infection rates but do not provide insights into the causal effects of interventions. We propose a spatiotemporal model that estimates the causal effect of changes in community mobility (intervention) on infection rates. Using an approximation to the SIR model and incorporating spatiotemporal dependence, the proposed model estimates a direct and indirect (spillover) effect of intervention. Under an interference and treatment ignorability assumption, this model is able to estimate causal intervention effects, and additionally allows for spatial interference between locations. Reductions in community mobility were measured by cell phone movement data. The results suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks after the intervention.

Government strategies in confronting COVID-19 in the republic of Cuba

In Cuba, the first cases of coronavirus 2019 (COVID-19) were confirmed on March 11, 2020, when the World Health Organization (WHO) officially declared the pandemic and the Ministry of Public Health of Cuba (MINSAP) began to execute the COVID-19 Prevention and Control Plan. This plan was prepared two months earlier by MINSAP working together with the National Civil Defense and the government approved it at the end of January. The aim of this research is to assess the effectiveness of the government strategies to deal with COVID-19, by analyzing the role of the different agencies involved in the pandemic management. A bibliographical review of the following documents was conducted: information issued by MINSAP and other ministries, archives of the Pedro Kouri Institute (IPK) and Cuban journals regarding the high impact in the field of medicine. The data were processed with different tools (diagrams, bar graphs, analysis and synthesis, etc.) that allowed measuring the effectiveness of the strategies implemented. The government's strategies focused on: the integration of all state agencies and some private institutions to confront COVID-19; the collaboration between MINSAP specialists, country's research centers and universities for the creation of vaccines to contain the pandemic; the production of medical equipment and instruments; the design of the organization processes of the services, such as planning techniques and distribution of ambulances, allocation of hospitals and isolation centers for sufferers and direct contacts respectively. The analysis carried out showed that the interrelations between the different organizations involved had positive influences on the treatment of the pandemic.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19.The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Incidencia del COVID-19 y la vulnerabilidad social de los migrantes en Chile

The global social and health crisis as a result of COVID-19 exposes the social vulnerability of the migrant population. The purpose is to carry out an analysis of the confirmed coronavirus cases between March to July 2020 in Chile, and the incidence of these among the migrant population, considering social and health conditions and multidimensional poverty rates. The data analyzed not only reflects vulnerability to the pandemic but also shows the hidden realities of migration processes and new challenges to this population. The information has been collected through the Transparency Act and the Sub-Secretariat of Health. Since this information is not public and disaggregated, it represents a limitation when making political, social, and health decisions for the benefit of the migrant population.

Predictors of persistent depressive and anxiety symptoms among older adults during the COVID-19 pandemic in Canada.

The COVID-19 pandemic has increased the risk of psychological distress for older adults during a period of reduced access to mental health support. We identified predictors of persistent depressive and anxiety symptoms among older adults throughout the initial 9 months of the pandemic. This was a longitudinal study using web-based surveys sent to a convenience sample of Canadians aged ≥55 years. A baseline survey was sent in May 2020, with eight surveys sent monthly between June 2020 and January 2021. Data were separated into three study periods, corresponding to coronavirus case rates. Respondents who met predetermined thresholds for elevated depressive or anxiety symptoms using standardized mental health scales during all three periods were defined as having persistent symptoms. Multivariable Poisson regression was used to compare the risk of persistent symptoms across respondent characteristics selected a priori. There were 835 respondents who answered questions about depressive symptoms and 865 who answered questions about anxiety symptoms across all three study periods. Among those with complete follow-up data, 19.2% (n= 159/827) reported persistent depressive symptoms and 16.4% (n= 140/856) reported persistent anxiety symptoms. Respondents with persistent depressive symptoms were more likely to be women (adjusted relative risk [aRR] 1.71; 95% CI 1.19-2.46), and report multimorbidity (aRR 1.45; 95% CI 1.07-1.97), pre-existing depression (aRR 2.00; 95% CI 1.45-2.77) and pre-existing anxiety (aRR 1.50; 95% CI 1.10-2.06). The same respondent characteristics were correlates of persistently elevated anxiety symptoms. Older women, older adults with pre-existing depression and anxiety, and those with multimorbidity were more likely to report persistent mental health symptoms during the first 9 months of the COVID-19 pandemic. These individuals were known to have elevated risk prior to the pandemic, and expanded interventions targeted to these groups are necessary to adequately address their mental health needs.

Impact of the COVID-19 pandemic on non-COVID-19 hospital mortality in patients with schizophrenia: a nationwide population-based cohort study

It remains unknown to what degree resource prioritization toward SARS-CoV-2 (2019-nCoV) coronavirus (COVID-19) cases had disrupted usual acute care for non-COVID-19 patients, especially in the most vulnerable populations such as patients with schizophrenia. The objective was to establish whether the impact of the COVID-19 pandemic on non-COVID-19 hospital mortality and access to hospital care differed between patients with schizophrenia versus without severe mental disorder. We conducted a nationwide population-based cohort study of all non-COVID-19 acute hospitalizations in the pre-COVID-19 (March 1, 2019 through December 31, 2019) and COVID-19 (March 1, 2020 through December 31, 2020) periods in France. We divided the population into patients with schizophrenia and age/sex-matched patients without severe mental disorder (1:10). Using a difference-in-differences approach, we performed multivariate patient-level logistic regression models (adjusted odds ratio, aOR) with adjustment for complementary health insurance, smoking, alcohol and substance addiction, Charlson comorbidity score, origin of the patient, category of care, intensive care unit (ICU) care, major diagnosis groups and hospital characteristics. A total of 198,186 patients with schizophrenia were matched with 1,981,860 controls. The 90-day hospital mortality in patients with schizophrenia increased significantly more versus controls (aOR = 1.18; p < 0.001). This increased mortality was found for poisoning and injury (aOR = 1.26; p = 0.033), respiratory diseases (aOR = 1.19; p = 0.008) and for both surgery (aOR = 1.26; p = 0.008) and medical care settings (aOR = 1.16; p = 0.001). Significant changes in the case mix were noted with reduced admission in the ICU and for several somatic diseases including cancer, circulatory and digestive diseases and stroke for patients with schizophrenia compared to controls. These results suggest a greater deterioration in access to, effectiveness and safety of non-COVID-19 acute care in patients with schizophrenia compared to patients without severe mental disorders. These findings question hospitals’ resilience pertaining to patient safety and underline the importance of developing specific strategies for vulnerable patients in anticipation of future public health emergencies.

SARS-CoV-2 reinfections with BA.1 (Omicron) variant among fully vaccinated individuals in northeastern Brazil.

BackgroundThe first case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in Rio Grande do Norte, northeastern Brazil, was diagnosed on March 12, 2020; thereafter, multiple surges of infection occurred, similar to what was seen elsewhere. These surges were mostly due to SARS-CoV-2 mutations leading to emergence of variants of concern (VoC). The introduction of new VoCs in a population previously exposed to SARS-CoV-2 or after vaccination has been a challenge to understanding the kinetics of the protective immune response against this virus. The aim of this study was to investigate the outbreak of SARS-CoV-2 reinfections observed in mid-January 2022 in Rio Grande do Norte state, Brazil. It describes the clinical and genomic characteristics of nine cases of reinfection that occurred coincident with the introduction of the omicron variant.Methodology/Principal findingsOf a total of 172,965 individuals with upper respiratory symptoms tested for SARS-CoV-2, between March 2020 through mid-February 2022, 58,097 tested positive. Of those, 444 had documented a second SARS-CoV-2 infection and nine reinfection cases were selected for sequencing. Genomic analysis revealed that virus lineages diverged between primary infections and the reinfections, with the latter caused by the Omicron (BA.1) variant among individuals fully vaccinated against SARS-CoV-2.Conclusions/SignificanceOur findings suggest that the Omicron variant is able to evade both natural and vaccine-induced immunity, since all nine cases had prior natural infection and, in addition, were fully vaccinated, emphasizing the need to develop effective blocking vaccines.