COVID-19 created chaos across the globe: Three novel quarantine epidemic models

Challenges and solutions in clinical research during the COVID-19 pandemic: A narrative review

Background and Aims

The COVID-19 pandemic has presented significant challenges to clinical research, necessitating the adoption of innovative and remote methods to conduct studies. This study aimed to investigate these challenges and propose solutions for conducting clinical research during the pandemic.

Methods

A narrative review was conducted (approval ID: IR.AMS.REC.1401.029), utilizing keyword searches in PubMed and Web of Science (WOS) citation index expanded (SCI-EXPANDED) from January 2020 to January 2023. Keywords included COVID-19, clinical research, barriers, obstacles, facilitators and enablers.

Results

Out of 2508 records retrieved, 43 studies were reviewed, providing valuable insights into the challenges and corresponding solutions for conducting clinical research during the COVID-19 pandemic. The identified challenges were categorized into four main groups: issues related to researchers or investigators, issues related to participants and ethical concerns, administrative issues, and issues related to research implementation. To address these challenges, multiple strategies were proposed, including remote monitoring through phone or video visits, online data collection and interviews to minimize in-person contact, development of virtual platforms for participant interaction and questionnaire completion, consideration of financial incentives, adherence to essential criteria such as inclusion and exclusion parameters, participant compensation, and risk assessment for vulnerable patients.

Conclusion

The COVID-19 pandemic has significantly impacted clinical research, requiring the adaptation and enhancement of existing research structures. Although remote methods and electronic equipment have limitations, they hold promise as effective solutions during this challenging period.

Information-epidemic co-evolution propagation under policy intervention in multiplex networks

The emergence of epidemics has seriously threatened the running of human society, such as COVID-19. During the epidemics, some external factors usually have a non-negligible impact on the epidemic transmission. Therefore, we not only consider the interaction between epidemic-related information and infectious diseases, but also the influence of policy interventions on epidemic propagation in this work. We establish a novel model that includes two dynamic processes to explore the co-evolutionary spread of epidemic-related information and infectious diseases under policy intervention, one of which depicts information diffusion about infectious diseases and the other denotes the epidemic transmission. A weighted network is introduced into the epidemic spreading to characterize the impact of policy interventions on social distance between individuals. The dynamic equations are established to describe the proposed model according to the micro-Markov chain (MMC) method. The derived analytical expressions of the epidemic threshold indicate that the network topology, epidemic-related information diffusion and policy intervention all have a direct impact on the epidemic threshold. We use numerical simulation experiments to verify the dynamic equations and epidemic threshold, and further discuss the co-evolution dynamics of the proposed model. Our results show that strengthening epidemic-related information diffusion and policy intervention can significantly inhibit the outbreak and spread of infectious diseases. The current work can provide some valuable references for public health departments to formulate the epidemic prevention and control measures.

COVID-19 dynamics in Madrid (Spain): A new convolutional model to find out the missing information during the first three waves

This article presents a novel mathematical model to describe the spread of an infectious disease in the presence of social and health events: it uses 15 compartments, 7 convolution integrals and 4 types of infected individuals, asymptomatic, mild, moderate and severe. A unique feature of this work is that the convolutions and the compartments have been selected to maximize the number of independent input parameters, leading to a 56-parameter model where only one had to evolve over time. The results show that 1) the proposed mathematical model is flexible and robust enough to describe the complex dynamic of the pandemic during the first three waves of the COVID-19 spread in the region of Madrid (Spain) and 2) the proposed model allows us to calculate the number of asymptomatic individuals and the number of persons who presented antibodies during the first waves. The study shows that the following results are compatible with the reported data: close to 28% of the infected individuals were asymptomatic during the three waves, close to 29% of asymptomatic individuals were detected during the subsequent waves and close to 26% of the Madrid population had antibodies at the end of the third wave. This calculated number of persons with antibodies is in great agreement with four direct measurements obtained from an independent sero-epidemiological research. In addition, six calculated curves (total number of confirmed cases, asymptomatic who are confirmed as positive, hospital admissions and discharges and intensive care units admissions) show good agreement with data from an epidemiological surveillance database.

Response of vaccination on community transmission of COVID-19: a dynamical approach

Due to the severity of COVID-19, vaccination campaigns have been or are underway in most parts of the world. In the current circumstances, it is obligatory to examine the response of vaccination on transmission of the SARS-CoV-2 virus when there are many vaccines available. Considering the importance of vaccination, a dynamic model has been proposed to provide an insight in the same direction. A mathematical model has been developed where six population compartments viz. susceptible, infected, vaccinated, home-isolated, hospitalized and recovered population are considered. Moreover, two novel parameters are included in the model to ascertain the effectiveness and speed of the vaccination campaign. Reproduction number and local stability of both the disease-free and endemic equilibrium points are studied to examine the nature of population dynamics. Graphical results for the community stage of COVID-19 infection are simulated and compared with real data to ascertain the validity of our model. The data is then studied to understand the impact of vaccination. These numerical results evidently demonstrate that home isolation and hospitalization should continue for the infected people until the transmission of the virus from person to person reduces sufficiently after completely vaccinating every nation. This model also recommends that all type of prevention measures should still be taken to avoid any type of critical situation due to infection and also reduce the death rate.

Modeling Intention-Based Critical Determinants of E-Commerce Utilization: Emerging Business Models and Transformation in the Digital World

Companies in the world today understand that keeping users in touch is essential to enhancing their trust. The primary objective of this study was to determine the intention-based critical determinants of E-commerce utilization in China from the end users' perspective. We developed a framework that identifies the factors that influence E-commerce utilization in China. Besides, we introduced observational research (data analysis) conducted in a real-world E-commerce sense. Results are based on a sample of 400 respondents by employing a comprehensive questionnaire survey. The structural equation modeling (SEM) and the partial least squares (PLS) regression approach was used to analyze the data. Study results show that perceived usefulness, perceived ease of use, reputation, trust in vendors, and purchase frequency significantly influence consumers' intention to use E-commerce systems. Research outcomes emphasize transforming social norms, raising consumers' awareness, redesigning policy frameworks, and highlighting the paybacks that E-commerce offers through integrative and consistent efforts.

Optimal control design incorporating vaccination and treatment on six compartment pandemic dynamical system

In this paper, a mathematical model of the COVID-19 pandemic with lockdown that provides a more accurate representation of the infection rate has been analyzed. In this model, the total population is divided into six compartments: the susceptible class, lockdown class, exposed class, asymptomatic infected class, symptomatic infected class, and recovered class. The basic reproduction number (R0) is calculated using the next-generation matrix method and presented graphically based on different progression rates and effective contact rates of infective individuals. The COVID-19 epidemic model exhibits the disease-free equilibrium and endemic equilibrium. The local and global stability analysis has been done at the disease-free and endemic equilibrium based on R0. The stability analysis of the model shows that the disease-free equilibrium is both locally and globally stable when R0<1, and the endemic equilibrium is locally and globally stable when R0>1 under some conditions. A control strategy including vaccination and treatment has been studied on this pandemic model with an objective functional to minimize. Finally, numerical simulation of the COVID-19 outbreak in India is carried out using MATLAB, highlighting the usefulness of the COVID-19 pandemic model and its mathematical analysis.

How and to what extent does the anti-social behavior of violating self-quarantine measures increase the spread of disease?

COVID-19 has shown that quarantine (or self-isolation) may be the only available tool against an unknown infectious disease if neither an effective vaccine nor anti-viral medication is available. Motivated by the fact that a considerable number of people were not compliant with the request for self-quarantine made by public authorities, this study used a multi-agent simulation model, whose results were validated by theory work, which highlights how and to what extent such an anti-social behavior hampers the confinement of a disease. Our framework quantifies two important scenarios: in one scenario a certain number of individuals totally ignore quarantine, whereas in the second scenario a larger number of individuals partially ignore the imposed policy. Our results reveal that the latter scenario can be more hazardous even if the total amount of social deficit of activity-measured by the total number of severed links in a physical network-would be same as the former scenario has, of which quantitative extent is dependent on the fraction of asymptomatic infected cases and the level of quarantine intensity the government imposing. Our findings have significance not only to epidemiology but also to research in the broader field of network science.

PACS numbers

Theory and modeling; computer simulation, 87.15.Aa; Dynamics of evolution, 87.23.Kg.

Introduction to Group-Structured-Epidemic Model

The spread of an infectious disease in a population is a random process when considering a small group of individuals. However, to a great group of individuals, the use of deterministic behavior is better. Based on these facts, in the literature, there were proposed stochastic and deterministic epidemic models. This work proposes a mixed compartmental epidemic model that allows stratifying the population into groups, considers demographic and environmental variability, presents an approximation to stochastic effects, and contemplates the network effects. The proposed model has a compact form to assist in the synthesis of the control law and parameters estimation strategies. Its objective is to overcome the difficulties encountered when used purely deterministic or purely stochastic models. In the end, to detail and verify the functioning of the proposed model, we present a set of flowcharts and simulations.

Vaccination and social distance to prevent COVID-19

In order to analyze the effect of vaccination in a population with the presence of viruses, a variation of the SIR (Susceptible-Infected-Removed) model is proposed taking into account social distancing and the effect of the vaccine. The equilibrium points of the proposed model are calculated and the stability analysis of the system is carried out. For the proposed model, disease-free equilibrium point and endemic equilibrium point are found and the conditions of existence are discussed. For the disease-free equilibrium point the bifurcation conditions are derived and simulations show that reducing the vaccination effort can lead the disease-free equilibrium to the endemic equilibrium. From the theoretical analysis, a minimum value of effort is obtained to guarantee a disease-free equilibrium point. Simulations were carried out from the value obtained from Rv to validate the theoretical results.

Exploring the therapeutic potential of marine-derived bioactive compounds against COVID-19

The ocean is the most biodiverse habitat of various organisms. The organisms surviving in the harsh conditions of the ocean consist of several spectacular properties and produce bioactive compounds of pharmacological importance. These compounds are effective even in small quantities with various immunomodulatory qualities such as antioxidant and anti-inflammatory properties. Though the vaccines for COVID-19 are developed, and drug development is also in progress, but till now no effective drug is available for this deadly virus. Researchers are mining the huge data of bioactive compounds to develop the specific drug for COVID-19. The use of the repurposed drugs is challenging against the rapidly mutating virus with variable symptoms and mode of transmission. This review is an attempt to compile all the spattered data of marine-derived bioactive compounds with antiviral properties and to explore their therapeutic potential against COVID-19.

An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion

The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the data set of the number of deaths per day and the number of infected cases per day of the most affected countries, the USA, Brazil, Russia, India, and the UK, have been considered in the first and second waves. The correlation fractal dimension has been estimated for the prescribed data sets of COVID-19, and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance, has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to visualize the transmission rate in the both times.

Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network

Background

COVID-19 is a novel virus that affects the upper respiratory tract, as well as the lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are increasing regularly. Computed tomography (CT) scans can be used carefully to detect and analyze COVID-19 cases. In CT images/scans, ground-glass opacity (GGO) is found in the early stages of infection. While in later stages, there is a superimposed pulmonary consolidation.

Methods

This research investigates the quantum machine learning (QML) and classical machine learning (CML) approaches for the analysis of COVID-19 images. The recent developments in quantum computing have led researchers to explore new ideas and approaches using QML. The proposed approach consists of two phases: in phase I, synthetic CT images are generated through the conditional adversarial network (CGAN) to increase the size of the dataset for accurate training and testing. In phase II, the classification of COVID-19/healthy images is performed, in which two models are proposed: CML and QML.

Result

The proposed model achieved 0.94 precision (Pn), 0.94 accuracy (Ac), 0.94 recall (Rl), and 0.94 F1-score (Fe) on POF Hospital dataset while 0.96 Pn, 0.96 Ac, 0.95 Rl, and 0.96 Fe on UCSD-AI4H dataset.

Conclusion

The proposed method achieved better results when compared to the latest published work in this domain.

National Health Systems and COVID-19 Death Toll Doubling Time

Coronavirus disease 2019 (COVID-19) has placed stress on all National Health Systems (NHSs) worldwide. Recent studies on the disease have evaluated different variables, namely, quarantine models, mitigation efforts, damage to mental health, mortality of the population with chronic diseases, diagnosis, use of masks and social distancing, and mortality based on age. This study focused on the four NHSs recognized by the WHO. These systems are as follows: (1) The Beveridge model, (2) the Bismarck model, (3) the National Health Insurance (NHI) model, and (4) the "Out-of-Pocket" model. The study analyzes the response of the health systems to the pandemic by comparing the time in days required to double the number of disease-related deaths. The statistical analysis was limited to 56 countries representing 70% of the global population. Each country was grouped into the health system defined by the WHO. The study compared the median death toll DT, between health systems using Mood's median test method. The results show high variability of the temporal trends in each group; none of the health systems for the three analyzed periods maintain stable interquartile ranges (IQRs). Nevertheless, the results obtained show similar medians between the study groups. The COVID-19 pandemic saturates health systems regardless of their management structures, and the result measured with the time for doubling death rate variable is similar among the four NHSs.

Impacts of social distancing on the spread of infectious diseases with asymptomatic infection: A mathematical model

Social distancing can be divided into two categories: spontaneous social distancing adopted by the individuals themselves, and public social distancing promoted by the government. Both types of social distancing have been proved to suppress the spread of infectious disease effectively. While previous studies examined the impact of each social distancing separately, the simultaneous impacts of them are less studied. In this research, we develop a mathematical model to analyze how spontaneous social distancing and public social distancing simultaneously affect the outbreak threshold of an infectious disease with asymptomatic infection. A communication-contact two-layer network is constructed to consider the difference between spontaneous social distancing and public social distancing. Based on link overlap of the two layers, the two-layer network is divided into three subnetworks: communication-only network, contact-only network, and overlapped network. Our results show that public social distancing can significantly increase the outbreak threshold of an infectious disease. To achieve better control effect, the subnetwork of higher infection risk should be more targeted by public social distancing, but the subnetworks of lower infection risk shouldn't be overlooked. The impact of spontaneous social distancing is relatively weak. On the one hand, spontaneous social distancing in the communication-only network has no impact on the outbreak threshold of the infectious disease. On the other hand, the impact of spontaneous social distancing in the overlapped network is highly dependent on the detection of asymptomatic infection sources. Moreover, public social distancing collaborates with infection detection on controlling an infectious disease, but their impacts can't add up perfectly. Besides, public social distancing is slightly less effective than infection detection, because infection detection can also promote spontaneous social distancing.

Mathematical model, forecast and analysis on the spread of COVID-19

Pandemic COVID-19 which has infected more than 35,027,546 people and death more than 1,034,837 people in 235 countries as on October 05, 2020 has created a chaos across the globe. In this paper, we develop a compartmental epidemic model to understand the spreading behaviour of the disease in human population with a special case of Bhilwara, a desert town in India where successful control measures TTT (tracking, testing and treatment) was adopted to curb the disease in the very early phase of the spread of the disease in India. Local and global asymptotic stability is established for endemic equilibrium. Extensive numerical simulations with real parametric values are performed to validate the analytical results. Trend analysis of fatality rate, infection rate, and impact of lockdown is performed for USA, European countries, Russia, Iran, China, Japan, S. Korea with a comparative assessment by India. Kruskal - Wallis test is performed to test the null hypothesis for infected cases during the four lockdown phases in India. It has been observed that there is a significant difference at both 95% and 99% confidence interval in the infected cases, recovered cases and the case fatality rate during all the four phases of the lockdown.

The effect of acupoint herbal patching on the quality of life of patients recovering from COVID-19: A protocol for systematic review and meta analysis

BACKGROUND

There is a worldwide outbreak of COVID-19, and as the number of patients increases, an increasing number of patients are recovering. However, no relevant systematic review or meta-analysis has been designed to evaluate the effects of acupoint herbal patching on the life of patients recovering from COVID-19.

METHODS

The following electronic databases will be searched from the respective dates of database inception to April 20, 2021: The Cochrane Library, Web of Science, EMBASE, MEDLINE, China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM), Wanfang database, the Chinese Scientific Journal Database (VIP), and other sources. All published randomized controlled trials in English or Chinese related to acupoint herbal patching for COVID-19 will be included. The primary outcome was the timing of the influence of acupoint herbal patching on the quality of life of convalescent patients. Secondary outcomes were accompanying symptoms (such as myalgia, expectoration, stuffiness, runny nose, pharyngalgia, anhelation, chest distress, dyspnea, crackles, headache, nausea, vomiting, anorexia, diarrhea) disappearance rate, negative COVID-19 results rate on two consecutive occasions (not on the same day), average hospitalization time, clinical curative effect, and improved quality of life.

RESULTS

The main purpose of this systematic review protocol was to assess the effectiveness and safety of acupoint herbal patching therapy for treating patients recovering from COVID-19.

CONCLUSION

The conclusion of our study will provide evidence to judge whether acupoint herbal patching is an effective intervention for the quality of life in patients recovering.

PROSPERO REGISTRATION NUMBER

CRD42021246550.

DNA vaccines against COVID-19: Perspectives and challenges

The coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is associated with several fatal cases worldwide. The rapid spread of this pathogen and the increasing number of cases highlight the urgent development of vaccines. Among the technologies available for vaccine development, DNA vaccination is a promising alternative to conventional vaccines. Since its discovery in the 1990s, it has been of great interest because of its ability to elicit both humoral and cellular immune responses while showing relevant advantages regarding producibility, stability, and storage. This review aimed to summarize the current knowledge and advancements on DNA vaccines against COVID-19, particularly those in clinical trials.

Prediction and control of COVID-19 spreading based on a hybrid intelligent model

The coronavirus (COVID-19) is a highly infectious disease that emerged in the late December 2019 in Wuhan, China. It caused a worldwide outbreak and a major threat to global health. It is important to design prediction and control strategies to restrain its exploding. In this study, a hybrid intelligent model is proposed to simulate the spreading of COVID-19. First, considering the effect of control measures, such as government investment, media publicity, medical treatment, and law enforcement in epidemic spreading. Then, the infection rates are optimized by genetic algorithm (GA) and a modified susceptible-infected-quarantined-recovered (SIQR) epidemic spreading model is proposed. In addition, the long short-term memory (LSTM) is imbedded into the SIQR model to design the hybrid intelligent model to further optimize other parameters of the system model, which can obtain the optimal predictive model and control measures. Simulation results show that the proposed hybrid intelligence algorithm has good predictive ability. This study provide a reliable model to predict cases of infection and death, and reasonable suggestion to control COVID-19.

Analytical features of the SIR model and their applications to COVID-19

A classic two-parameter epidemiological SIR-model of the coronavirus propagation is considered. The first integrals of the system of non-linear equations are obtained. The Painlevé test shows that the system of equations is not integrable in the general case. However, the general solution is obtained in quadrature as an inverse time-function. Using the first integrals of the system of equations, analytical dependencies for the number of infected patients I(t) and that of recovered patients R(t) on the number of susceptible to infection S(t) are obtained. A particular attention is paid to interrelation of I(t) and R(t) both depending on α/β, where α is the contact rate in the community and β is the intensity of recovery/decease of patients. It is demonstrated that the data on particular morbidity waves in Hubei (China), Italy, Austria, South Korea, Moscow (Russia) as well some Australian territories are satisfactorily described by the expressions obtained for I(R). The variability of parameter N having been traditionally considered as a static population size is discussed.

Prediction of the peak Covid-19 pandemic in Indonesia using SIR model

This research implements the Susceptible, Infected, and Removed (SIR) model to predict the Covid-19 outbreak in Indonesia. The government official data, consisting of infected, dead, and recovered, are used as actual data to interpolate the model through matching data with minimum mean squared error (MSE). The study uses one of the Quasi-Newton search methods, the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) algorithm, to determine the interaction coefficient's optimal value in the model with the minimum MSE value. Based on data as of July 18, 2020, it predicts that the peak of the infected number will be in October 2020 with around 14 % of the total population infected, and the MSE of 18.42 is relative to the period of the actual data. Meanwhile, the basic reproduction rate is calculated to be 2.035 from the model, where it is underestimated about 29 % compared to the relative basic reproduction rate from the provided actual data.

SIRSi compartmental model for COVID-19 pandemic with immunity loss

The coronavirus disease 2019 (Covid-19) outbreak led the world to an unprecedented health and economic crisis. In an attempt to respond to this emergency, researchers worldwide are intensively studying the dynamics of the Covid-19 pandemic. In this study, a Susceptible - Infected - Removed - Sick (SIRSi) compartmental model is proposed, which is a modification of the classical Susceptible - Infected - Removed (SIR) model. The proposed model considers the possibility of unreported or asymptomatic cases, and differences in the immunity within a population, i.e., the possibility that the acquired immunity may be temporary, which occurs when adopting one of the parameters ( γ ) other than zero. Local asymptotic stability and endemic equilibrium conditions are proved for the proposed model. The model is adjusted to the data from three major cities of the state of São Paulo in Brazil, namely, São Paulo, Santos, and Campinas, providing estimations of duration and peaks related to the disease propagation. This study reveals that temporary immunity favors a second wave of infection and it depends on the time interval for a recovered person to be susceptible again. It also indicates the possibility that a greater number of patients would get infected with decreased time for reinfection.

An integrated view on society readiness and initial reaction to COVID-19: A study across European countries

With the wake of the COVID-19 pandemic, the question of society's capability to deal with an acute health crisis is, once again, brought to the forefront. In the core is the need to broaden the perspective on the determinants of a country's ability to cope with the spread of the virus. This paper is about bringing together diverse aspects of readiness and initial reaction to a COVID-19 outbreak. We proposed an integrated evaluation framework which encapsulates six dimensions of readiness and initial reaction. Using a specific multi-level outranking method, we analysed how these dimensions affect the relative positioning of European countries in the early stages of the COVID-19 outbreak. The results revealed that the order of countries based on our six-dimensional assessment framework is significantly reminiscent of the actual positioning of countries in terms of COVID-19 morbidity and mortality in the initial phase of the pandemic. Our findings confirm that only when a country's readiness is complemented by an appropriate societal reaction we can expect a less severe outcome. Moreover, our study revealed different patterns of performance between former communist Eastern European and Western European countries.