Understanding epidemic data and statistics: A case study of COVID-19

Hospitalization Endpoint in Clinical Trials of Outpatient Settings: using Anti-SARS-COV-2 Therapy as an Example

Purpose

In response to the COVID-19 pandemic, the World Health Organization (WHO) developed a set of outcome measures for trials primarily aimed at hospitalised patients. However, a gap exists in defining outcome standards for non-hospitalised patients. Therefore, this study aims to discuss hospitalisation as a primary outcome in outpatient trials and its potential pitfalls, specifically focusing on trials related to anti-SARS-COV-2 therapy.

Methods

In this narrative review, researchers thoroughly searched MEDLINE and ClinicalTrials.gov from January 2020 to December 2022, targeting Phase III randomized controlled trials involving outpatients with mild-to-moderate COVID-19. The trials were specifically related to anti-SARS-COV-2 monoclonal antibodies or antiviral agents. The study collected essential data, including the type of intervention, comparator, primary objective, primary endpoint, and the use of estimands in the trial.

Results

The search identified 12 trials that evaluated the efficacy of anti-SARS COV-2 therapies in a predefined population. Three studies used hospitalisation and death as primary endpoints in high-risk patients receiving monoclonal antibodies. Nine studies assessed the efficacy of several antiviral agents: four trials used hospitalisation and death as the main endpoints, while others used different measures such as virologic measures using the Reverse Transcription-Polymerase Chain Reaction test (RT-PCR), the eight-point WHO ordinal scale, symptom alleviation by Day 7 and time to clinical response.

Conclusion

Choosing hospitalization as an endpoint may provide meaningful data such as the cost-effectiveness ratio of a drug. However, different hospital utilisation patterns and investigator decisions could bias clinical outcomes if no specific criteria are considered. Therefore, investigators should have clear criteria for determining variables that influence this measure.

Google Trends as a predictive tool in the era of COVID-19: a scoping review

Google Trends has been extensively used in different sectors from finance to tourism, the economy, fashion, the fun industry, the oil trade, and healthcare. This scoping review aims to summarize the role of Google Trends as a monitoring and a predicting tool in the COVID-19 pandemic. Inclusion criteria for this scoping review were original English-language peer-reviewed research articles on the COVID-19 pandemic conducted in 2020 using Google Trends as a search tool. Articles that were in a language other than English, were only in abstract form, or did not discuss the role of Google Trends during the COVID-19 pandemic were excluded. According to these criteria, a total of 81 studies were included to cover the period of the first year after the emergence of the crisis. Google Trends can potentially help health authorities to plan and control pandemics earlier and to decrease the risk of infection among people.

Neurological Involvements in COVID-19: A hospital-based study

Objectives

The SARS-CoV-2 pandemic is the most challenging crisis in the contemporary world. Besides severe pulmonary involvement, the disease also has several extrapulmonary manifestations, and new signs and symptoms are associated with it every dayThe present study aimed to inquire about the frequency of neurological manifestations and risk factors of COVID-19.

Materials & Methods

This retrospective, descriptive study included patients with neurological involvement admitted to the Alborz University of Medical Sciences academic hospitals from March 2020 to July 2020 with confirmed COVID-19 infection. The data included in the analysis were the patient's demographic information, underlying diseases, neurological manifestations, and laboratory findings.

Results

The study included ninety-five patients with a mean age of fifty-nine. Neurological symptoms and signs were observed in 91.6% and 10.5% of the patients, respectively. The most frequently associated neurological symptoms of COVID-19 were fatigue (49.5%), headache (47.4%), and dizziness (45.3%). Furthermore, the most common neurological involvements included gait disorders (6.3%), cerebellar dysfunction (4.2%), and cerebrovascular accidents (3.15%). Positive troponin was shown to be the strongest predictor of neurological signs (OR=21, P=0.017), followed by WBC≥15,000 (OR = 20.75, P=0.018) and a history of respiratory disease (OR=7.42, P=0.007).

Conclusion

Neurological symptoms were observed in more than 91% of the patients, while neurological signs were present in 10.5% of the COVID-19 patients. Additionally, positive troponin, WBC≥15,000, and a history of respiratory disease were the strongest predictors of neurological signs.

The Effect of Physical Exercise during COVID-19 Lockdown

The COVID-19 pandemic has exerted an effect on the general population that went over every expectation. To explore the effect of physical exercise (PE) during the national lockdown in Italy, a survey was drawn up and administered to a sample composed of 208 subjects. The questionnaire comprised 81 multiple-choice items, including sociodemographic data, health-related questions, and assessment of physical exercise, satisfaction with life, depression, and personality. The purpose of this study is to explore the role of physical exercise during the outbreak following the present hypothesis: first, if a link exists between the time spent on physical exercise during the lockdown and the perceived health condition, depressive and somatic symptomatology, and life satisfaction; second, to find associations among the SF-12 component summaries and the other psychological outcomes; and finally, to explore how physical and psychological variables are predictive of PCS-12 and MCS-12. The results showed that both vigorous and moderate physical exercise was strongly correlated with psychological variables, with statistically significant negative correlations found between age and physical exercise. Additionally, significant positive correlations were observed between physical exercise and mental health indices, such as MCS-12 and SWLS, whereas negative correlations were found with BDI, PCS-12, and SOM-H. The correlation analysis also revealed that physical and individual mental health summaries were associated with psychological outcomes, with statistically significant negative correlations found between PCS-12 and MCS, PCS-12 and SOM-H, and MCS-12 and BDI scores. Regression analysis showed that physical activities and psychological status both had a direct influence on perceived mental and physical well-being during the lockdown, accounting for 56.7% and 35.5% of the variance, respectively. The p-values for the significant correlations ranged from <0.05 to <0.01. Overall, these findings highlight the importance of physical exercise and psychological well-being in maintaining good health during the pandemic.

Metal and Metal Oxides Nanoparticles and Nanosystems in Anticancer and Antiviral Theragnostic Agents

The development of antiviral treatment and anticancer theragnostic agents in recent decades has been associated with nanotechnologies, and primarily with inorganic nanoparticles (INPs) of metal and metal oxides. The large specific surface area and its high activity make it easy to functionalize INPs with various coatings (to increase their stability and reduce toxicity), specific agents (allowing retention of INPs in the affected organ or tissue), and drug molecules (for antitumor and antiviral therapy). The ability of magnetic nanoparticles (MNPs) of iron oxides and ferrites to enhance proton relaxation in specific tissues and serve as magnetic resonance imaging contrast agents is one of the most promising applications of nanomedicine. Activation of MNPs during hyperthermia by an external alternating magnetic field is a promising method for targeted cancer therapy. As therapeutic tools, INPs are promising carriers for targeted delivery of pharmaceuticals (either anticancer or antiviral) via magnetic drug targeting (in case of MNPs), passive or active (by attaching high affinity ligands) targeting. The plasmonic properties of Au nanoparticles (NPs) and their application for plasmonic photothermal and photodynamic therapies have been extensively explored recently in tumor treatment. The Ag NPs alone and in combination with antiviral medicines reveal new possibilities in antiviral therapy. The prospects and possibilities of INPs in relation to magnetic hyperthermia, plasmonic photothermal and photodynamic therapies, magnetic resonance imaging, targeted delivery in the framework of antitumor theragnostic and antiviral therapy are presented in this review.

Research on Korean Translation in the Context of Epidemic Prevention and Control

An emergency like COVID-19 requires a theoretical framework for policy implementation that involves public and private sector collaborations. After policy failures, new institutions have formed that trigger PPP's later, allowing the incumbent administration to continue in office longer. It focuses on novel approaches to dealing with pandemics. The present administration put these rules in place to keep COVID-19 under control. When it comes to Real Time - polymerase chain reaction (RT-PCR) testing, South Korea's government and corporations partnered to swiftly raise the quantity of testing in the country. Models of policy change are shown to be dynamic, cyclical, and recursive. During the COVID-19 outbreak in South Korea, an empirical content research was conducted. Even though South Korea's leader was at risk of losing public support to the point where impeachment was mentioned as a possible option, he dramatically reversed public mood to win general elections by a wide margin in April 2020, while the pandemic scenario persisted. To win reelection, democratic administrations are under more pressure to effectively perform crisis management when faced with a crisis. As a result, they are under even more pressure to immediately mobilize public and private resources. The emergency use authorization (EUA) protocol for test kits is an example of "leapfrogging actors" – up-and-coming innovators – who helped turn a pandemic tragedy into a possibility for sustained leadership and for them. The results based on infected premises culling rate ratio is 82.3%, number of measles cases report is 86.4%, spread and epidemic ratio is 84.2%, important of epidemiology is 89.35%, transmission potential of COVID-19 is 91.24% and illustration of epidemic control is 92.45. The results based on infected premises culling rate ratio is 82.3%, number of measles cases report is 86.4%, spread and epidemic ratio is 84.2%, important of epidemiology is 89.35%, transmission potential of COVID-19 is 91.24% and illustration of epidemic control is 92.45.

Evaluating COVID-19 risk under the estimation of population mean using two attributes

The virus of COVID-19 has affected humans physically, mentally, and economically all over the world. Each country's development level, resources, and immunization to have coping capacity against this covid19 differ country-wise. Thus, understanding socioeconomic vulnerability and coping capacity among countries under different health systems can be crucial. Contrasting most articles on COVID-19, this article focuses on evaluating and estimating the COVID-19 risk and lack of coping capacity in 190 countries. This present study suggests an exponential estimator using two auxiliary attributes. Theoretically, the mean square error expressions are derived and compared with some existing estimators. These findings were supported by using the real data of INFORM COVID-19 risk.

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O'Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40-60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40-60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.

COVID-19: A Comparative Study of Contagions Peaks in Cities from Europe and the Americas

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a group of viruses that provoke illnesses ranging from the common cold to more serious illnesses such as pneumonia. COVID-19 started in China and spread rapidly from a single city to an entire country in just 30 days and to the rest of the world in no more than 3 months. Several studies have tried to model the behavior of COVID-19 in diverse regions, based on differential equations of the SIR and stochastic SIR type, and their extensions. In this article, a statistical analysis of daily confirmed COVID-19 cases reported in eleven different cities in Europe and America is conducted. Log-linear models are proposed to model the rise or drop in the number of positive cases reported daily. A classification analysis of the estimated slopes is performed, allowing a comparison of the eleven cities at different epidemic peaks. By rescaling the curves, similar behaviors among rises and drops in different cities are found, independent of socioeconomic conditions, type of quarantine measures taken, whether more or less restrictive. The log-linear model appears to be suitable for modeling the incidence of COVID-19 both in rises and drops.

Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela

We propose two different mathematical models to study the effect of immigration on the COVID-19 pandemic. The first model does not consider immigration, whereas the second one does. Both mathematical models consider five different subpopulations: susceptible, exposed, infected, asymptomatic carriers, and recovered. We find the basic reproduction number R₀ using the next-generation matrix method for the mathematical model without immigration. This threshold parameter is paramount because it allows us to characterize the evolution of the disease and identify what parameters substantially affect the COVID-19 pandemic outcome. We focus on the Venezuelan scenario, where immigration and emigration have been important over recent years, particularly during the pandemic. We show that the estimation of the transmission rates of the SARS-CoV-2 are affected when the immigration of infected people is considered. This has an important consequence from a public health perspective because if the basic reproduction number is less than unity, we can expect that the SARS-CoV-2 would disappear. Thus, if the basic reproduction number is slightly above one, we can predict that some mild non-pharmaceutical interventions would be enough to decrease the number of infected people. The results show that the dynamics of the spread of SARS-CoV-2 through the population must consider immigration to obtain better insight into the outcomes and create awareness in the population regarding the population flow.

Explaining trip generation during the COVID-19 pandemic: A psychological perspective

Introduction

The present study investigated the extent of reduction in the generation of non-essential trips (i.e., for shopping, personal, social, and entertainment reasons) due to the COVID-19 pandemic and the role of psychological factors (including deliberate planning and personal moral obligations) in explaining the change.

Method

We collected data through an internet survey conducted from April to June 2020. We recruited the respondents (N = 369) from a young segment of the population in Iran. The hypothesised model framework included the components of the theory of planned behaviour (including attitudes, subjective norms, perceived behavioural control, and intention to reduce non-essential trips) along with personal moral obligation. The framework also consisted of socio-demographic characteristics of age, gender, income, car ownership and trip distance. A structural equation model was developed to explain trip reduction at an aggregated level for four non-essential trip purposes (i.e., shopping, personal, social, and entertainment). In the aggregated model, trip-reducing behaviour represents the change in the number of trips for all non-essential purposes. We also tested the same framework, to explain trip reduction for each of the trip purposes, separately.

Results

On average, the study participants reduced their non-essential trips by 60% during the pandemic compared with in the pre-COVID-19 period. Men were less likely than women to reduce the rate of their trips during the pandemic. The SEM demonstrated that the theory of planned behaviour predicted trip-generation behaviour during the abnormal situation represented by the pandemic. Perceived behavioural control both directly and indirectly impacted trip reduction. However, personal moral obligations failed to provide a direct explanation for trip generation reduction. According to the hypothesised model, the results for different trip purposes were also mostly in line with those from the aggregated model.

Conclusions

The findings imply that participants in our sample made deliberate and planned psychological decisions (aligned with the theory of planned behaviour) regarding potential travel behavioural decisions during the COVID-19 pandemic. The findings also highlighted that perceived behavioural control grows in importance alongside growth in difficult circumstances.

An ethical visualization of the NorthCOVID-19 model

When modelling epidemics, the outputs and techniques used may be hard for the general public to understand. This can cause fear mongering and confusion on how to interpret the predictions provided by these models. This article proposes a solution for such a model that was created by a Canadian institute for COVID-19 in their region; namely, the NorthCOVID-19 model. In taking these ethical concerns into consideration, first the web interface of this model is analyzed to see how it may be difficult for a user without a strong mathematical background to understand how to use it. Second, a system is developed that takes this model's outputs as an input and produces a video summarization with an auto-generated audio to address the complexity of the interface, while ensuring that the end user is able to understand the important information produced by this model. A survey conducted on this proposed output asked participants, on a scale of 1 to 5, whether they strongly disagreed (1) or strongly agreed (5) with statements regarding the output of the proposed method. The results showed that the audio in the output was helpful in understanding the results (80% responded with 4 or 5) and that it helped improve overallcomprehension of the model (85% responded with 4 or 5). For the analysis of the NorthCOVID-19 interface, a System Usability Scale (SUS) survey was performed where itreceived a scoring of 70.94 which is slightly above the average of 68.

Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

Statistical analysis of the Hungarian COVID-19 victims

With the wide spread of Coronavirus, most people who infected with the COVID-19, will recover without requiring special treatment. Whereas, elders and those with underlying medical problems are more likely to have serious illnesses, even be threatened with death. Many more disciplines try to find solutions and drive master plan to this global trouble. Consequently, by taking one particular population, Hungary, this study aims to explore a pattern of COVID-19 victims, who suffered from some underlying conditions. Age, gender, and underlying medical problems form the structure of the clustering. K-Means and two step clustering methods were applied for age-based and age-independent analysis. Grouping of the deaths in the form of two different scenarios may highlight some concepts of this deadly disease for public health professionals. Our result for clustering can forecast similar cases which are assigned to any cluster that it will be a serious cautious for the population.

iBlock: An Intelligent Decentralised Blockchain-based Pandemic Detection and Assisting System

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML.

A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction

Currently, the novel COVID-19 coronavirus has been widely spread as a global pandemic. The COVID-19 pandemic has a major influence on human life, healthcare systems, and the economy. There are a large number of methods available for predicting the incidence of the virus. A complex and non-stationary problem such as the COVID-19 pandemic is characterized by high levels of uncertainty in its behavior during the pandemic time. The fuzzy logic, especially Type-2 Fuzzy Logic, is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. The main objective of the current study is to present a novel Deep Interval Type-2 Fuzzy LSTM (DIT2FLSTM) model for prediction of the COVID-19 incidence, including new cases, recovery cases, and mortality rate in both short and long time series. The proposed model was evaluated on real datasets produced by the world health organization (WHO) on top highly risked countries, including the USA, Brazil, Russia, India, Peru, Spain, Italy, Iran, Germany, and the U.K. The results confirm the superiority of the DIT2FLSTM model with an average area under the ROC curve (AUC) of 96% and a 95% confidence interval of [92–97] % in the short-term and long-term. The DIT2FLSTM was applied to a well-known standard benchmark, the Mackey-Glass time-series, to show the robustness and proficiency of the proposed model in uncertain and chaotic time series problems. The results were evaluated using a 10-fold cross-validation technique and statistically validated through the t-test method. The proposed DIT2FLSTM model is promising for the prediction of complex problems such as the COVID-19 pandemic and making strategic prevention decisions to save more lives.

Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach

The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small.

Nonlinear Dynamics of the Introduction of a New SARS-CoV-2 Variant with Different Infectiousness

Several variants of the SARS-CoV-2 virus have been detected during the COVID-19 pandemic. Some of these new variants have been of health public concern due to their higher infectiousness. We propose a theoretical mathematical model based on differential equations to study the effect of introducing a new, more transmissible SARS-CoV-2 variant in a population. The mathematical model is formulated in such a way that it takes into account the higher transmission rate of the new SARS-CoV-2 strain and the subpopulation of asymptomatic carriers. We find the basic reproduction number R0 using the method of the next generation matrix. This threshold parameter is crucial since it indicates what parameters play an important role in the outcome of the COVID-19 pandemic. We study the local stability of the infection-free and endemic equilibrium states, which are potential outcomes of a pandemic. Moreover, by using a suitable Lyapunov functional and the LaSalle invariant principle, it is proved that if the basic reproduction number is less than unity, the infection-free equilibrium is globally asymptotically stable. Our study shows that the new more transmissible SARS-CoV-2 variant will prevail and the prevalence of the preexistent variant would decrease and eventually disappear. We perform numerical simulations to support the analytic results and to show some effects of a new more transmissible SARS-CoV-2 variant in a population.

Visualizing and assessing US county-level COVID19 vulnerability

Background

Like most of the world, the United States’ public health and economy are impacted by the COVID19 pandemic. However, discrete pandemic effects may not be fully realized on the macro-scale. With this perspective, our goal is to visualize spread of the pandemic and measure county-level features which may portend vulnerability.

Methods

We accessed the New York Times GitHub repository COVID19 data and 2018 United States Census data for all United States Counties. The disparate datasets were merged and filtered to allow for visualization and assessments about case fatality rate (CFR%) and associated demographic, ethnic and economic features.

Results

Our results suggest that county-level COVID19 fatality rates are related to advanced population age (P < .001) and less diversity as evidenced by higher proportion of Caucasians in High CFR% counties (P < .001). Also, lower CFR% counties had a greater proportion of the population reporting has having 2 or more races (P < .001). We noted no significant differences between High and Low CFR% counties with respect to mean income or poverty rate.

Conclusions

Unique COVID19 impacts are realized at the county level. Use of public datasets, data science skills and information visualization can yield helpful insights to drive understanding about community-level vulnerability.

Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach

The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020. Currently, there are only a few approved vaccines, each with different efficacies and mechanisms of action. Moreover, vaccination programs in different regions may vary due to differences in implementation, for instance, simply the availability of the vaccine. In this article, we study the impact of the pace of vaccination and the intrinsic efficacy of the vaccine on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. Then we study different potential scenarios regarding the burden of the COVID-19 pandemic in the near future. We construct a compartmental mathematical model and use computational methodologies to study these different scenarios. Thus, we are able to identify some key factors to reach the aims of the vaccination programs. We use some metrics related to the outcomes of the COVID-19 pandemic in order to assess the impact of the efficacy of the vaccine and the pace of the vaccine inoculation. We found that both factors have a high impact on the outcomes. However, the rate of vaccine administration has a higher impact in reducing the burden of the COVID-19 pandemic. This result shows that health institutions need to focus on increasing the vaccine inoculation pace and create awareness in the population about the importance of COVID-19 vaccines.

Estimation of novel coronavirus (COVID-19) reproduction number and case fatality rate: A systematic review and meta-analysis

BACKGROUND AND AIMS

Realizing the transmission potential and the magnitude of the coronavirus disease 2019 (COVID-19) aids public health monitoring, strategies, and preparation. Two fundamental parameters, the basic reproduction number (R 0) and case fatality rate (CFR) of COVID-19, help in this understanding process. The objective of this study was to estimate the R 0 and CFR of COVID-19 and assess whether the parameters vary in different regions of the world.

METHODS

We carried out a systematic review to find the reported estimates of the R 0 and the CFR in articles from international databases between January 1 and August 31, 2020. Random-effect models and Forest plots were implemented to evaluate the mean effect size of R 0 and the CFR. Furthermore, R 0 and CFR of the studies were quantified based on geographic location, the tests/thousand population, and the median population age of the countries where the studies were conducted. To assess statistical heterogeneity among the selected articles, the I 2 statistic and the Cochran's Q test were used.

RESULTS

Forty-five studies involving R 0 and 34 studies involving CFR were included. The pooled estimation of R 0 was 2.69 (95% CI: 2.40, 2.98), and that of the CFR was 2.67 (2.25, 3.13). The CFR in different regions of the world varied significantly, from 2.49 (2.08, 2.94) in Asia to 3.40 (2.81, 4.04) in North America. We observed higher mean CFR values for the countries with lower tests (3.15 vs 2.16) and greater median population age (3.13 vs 2.27). However, R 0 did not vary significantly in different regions of the world.

CONCLUSIONS

An R 0 of 2.69 and a CFR of 2.67 indicate the severity of the COVID-19. Although R 0 and CFR may vary over time, space, and demographics, we recommend considering these figures in control and prevention measures.

Public health awareness: knowledge, attitude and behaviors of the public on health risks during COVID-19 pandemic in sultanate of Oman

Purpose

This study aims to measure the level of public awareness about the threat of the emerging coronavirus (COVID-19) pandemic among the Omani population. It also aims to investigate the mediating effect of the Omanis’ attitudes and behaviors with underlying conditions of COVID-19.

Design/methodology/approach

A cross-sectional study was conducted to collect data via an online survey of Omani citizens and residents from various geographic areas in Oman, 305 responses were received. SPSS and partial least square-structural equation modeling were used for data analysis.

Findings

The study revealed that public awareness regarding the COVID-19 pandemic was significantly influenced by people’s perceived risk, information source and health-related knowledge. Further, preventive behavior during the disease spread has a significant direct and indirect impact on their awareness. However, an insignificant mediation effect of public attitude was found between the source of information and public awareness.

Research limitations/implications

This study is limited by the scarcity of related literature in the Omani context. It is recommended that future research complete an in-depth study of public awareness regarding COVID-19, using other constructs and/or other data collection techniques.

Practical implications

This research will provide governmental health authorities and policymakers with a guideline to establish more efficient pandemic containment strategies to control public behavior toward the COVID-19 pandemic and curb viral prevalence.

Social implications

This research will help in improving prevention measures against COVID-19 are recommended to be more educated through a more effective mechanism to raise public attitude regarding pandemic prevalence positively.

Originality/value

The originality of this research can be drawn from key findings that indicate that people overall gained knowledge about how to deal with the COVID-19 pandemic and the accuracy of information significantly impacts public awareness.

Impact of a New SARS-CoV-2 Variant on the Population: A Mathematical Modeling Approach

Several SARS-CoV-2 variants have emerged around the world, and the appearance of other variants depends on many factors. These new variants might have different characteristics that can affect the transmissibility and death rate. The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020 and in some countries the vaccines will not soon be widely available. For this article, we studied the impact of a new more transmissible SARS-CoV-2 strain on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. We studied different scenarios regarding the transmissibility in order to provide a scientific support for public health policies and bring awareness of potential future situations related to the COVID-19 pandemic. We constructed a compartmental mathematical model based on differential equations to study these different scenarios. In this way, we are able to understand how a new, more infectious strain of the virus can impact the dynamics of the COVID-19 pandemic. We studied several metrics related to the possible outcomes of the COVID-19 pandemic in order to assess the impact of a higher transmissibility of a new SARS-CoV-2 strain on these metrics. We found that, even if the new variant has the same death rate, its high transmissibility can increase the number of infected people, those hospitalized, and deaths. The simulation results show that health institutions need to focus on increasing non-pharmaceutical interventions and the pace of vaccine inoculation since a new variant with higher transmissibility, such as, for example, VOC-202012/01 of lineage B.1.1.7, may cause more devastating outcomes in the population.

SARS-CoV-2 Mediated Endothelial Dysfunction: The Potential Role of Chronic Oxidative Stress

Endothelial cells have emerged as key players in SARS-CoV-2 infection and COVID-19 inflammatory pathologies. Dysfunctional endothelial cells can promote chronic inflammation and disease processes like thrombosis, atherosclerosis, and lung injury. In endothelial cells, mitochondria regulate these inflammatory pathways via redox signaling, which is primarily achieved through mitochondrial reactive oxygen species (mtROS). Excess mtROS causes oxidative stress that can initiate and exacerbate senescence, a state that promotes inflammation and chronic endothelial dysfunction. Oxidative stress can also activate feedback loops that perpetuate mitochondrial dysfunction, mtROS overproduction, and inflammation. In this review, we provide an overview of phenotypes mediated by mtROS in endothelial cells - such as mitochondrial dysfunction, inflammation, and senescence - as well as how these chronic states may be initiated by SARS-CoV-2 infection of endothelial cells. We also propose that SARS-CoV-2 activates mtROS-mediated feedback loops that cause long-term changes in host redox status and endothelial function, promoting cardiovascular disease and lung injury after recovery from COVID-19. Finally, we discuss the implications of these proposed pathways on long-term vascular health and potential treatments to address these chronic conditions.

Determinants of COVID-19 pandemic in India: an exploratory study of Indian states and districts

The countries across the globe are facing one of the worst infectious diseases in modern times in the form of COVID-19 pandemic. Different measures have been taken to control and manage the outbreak of COVID-19 in these countries. There are two propositions in context of effective control and management of a pandemic like COVID-19. First, a strong and effective public health care system is essential for managing the public health crisis and the uneven responses to COVID-19 are mainly because of inadequate health infrastructure. Second, the spread of COVID-19 depends on the interplay of other social determinants at local level, and therefore, addressing the gaps in social determinants of COVID-19 at local level is critical to control and manage this pandemic. The present paper attempts to examine these two propositions in Indian context at states and districts level, respectively. Using the cross-sectional data and constructing composite indices of COVID-19 intensity and level of health infrastructure at state level, the results show that there is no robust relationship between level of health infrastructure and management of COVID-19 at state level as the states with better health infrastructure are also struggling to combat against COVID-19. The district-level analysis indicates a significant relationship between concentration of COVID-19 and social determinants as majority of the districts with higher concentration of COVID-19 are those which have social determinants below national average.

Understanding South Korea's Response to the COVID-19 Outbreak: A Real-Time Analysis

This case study focuses on the epidemiological situation of the COVID-19 outbreak, its impacts and the measures South Korea undertook during the first wave of the COVID-19 pandemic. Since the first case was confirmed on 20 January 2020, South Korea has been actively experiencing the COVID-19 outbreak. In the early stage of the pandemic, South Korea was one of the most-affected countries because of a large outbreak related to meetings of a religious movement, namely the Shincheonji Church of Jesus, in a city called Daegu and North Gyeongsang province. However, South Korea was held as a model for many other countries as it appeared to slow the spread of the outbreak with distinctive approaches and interventions. First of all, with drastic and early intervention strategies it conducted massive tracing and testing in a combination of case isolation. These measures were underpinned by transparent risk communication, civil society mobilization, improvement of accessibility and affordability of the treatment and test, the consistent public message on the potential benefit of wearing a mask, and innovation. Innovative measures include the mobile case-tracing application, mobile self-quarantine safety protection application, mobile self-diagnosis application, and drive-thru screening centres. Meanwhile, the epidemic has brought enormous impacts on society economically and socially. Given its relationship with China, where the outbreak originated, the economic impact in South Korea was predicted to be intense and it was already observed since February due to a decline in exports. The pandemic and measures undertaken by the government also have resulted in social conflicts and debates, human-right concerns, and political tension. Moreover, it was believed that the outbreak of COVID-19 and the governmental responses towards it has brought a huge impact on the general election in April. Despite of the large outbreak in late February, the Korean government has flattened the COVID-19 curve successfully and the downward trend in the number of new cases remained continuously as of 30 April. The most distinctive feature of South Korea’s responses is that South Korea conducted proactive case finding, contacts tracing, and isolations of cases instead of taking traditional measures of the containment of the epidemic such as boarder closures and lockdowns.

A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

BACKGROUND

COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.

METHODS

COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson's χ²-test or Fisher's exact test, and continuous variables were analyzed using Student's t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.

RESULTS

A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973).

CONCLUSIONS

A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

Balancing Personal Privacy and Public Safety During COVID-19: The Case of South Korea

There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Korea's approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our specific proposal discusses the COVID index, which considers collective infection, outbreak intensity, availability of medical infrastructure, and the death rate. Finally, we summarize the findings and lessons for future research and the policy implications.

The association between the incidence of COVID-19 and the distance from the virus epicenter in Iran

Since the first official report of the spread of SARS-CoV-2 infections in the city of Qom in mid-February, Iran has become the country most affected by the COVID-19 epidemic in the Middle East. All Iranian provinces are now affected, although to a different extent. The aim of the present study was to evaluate whether the distance from the epicenter of the infection (Qom) or demographic factors such as population density and the ratio of the elderly population are associated with the incidence of COVID-19 in different Iranian provinces. For the purpose of determining whether the distance from the virus epicenter could be associated with the spread of infection, linear regression analysis was performed using STATA 12.0 software. The association of the incidence of COVID-19 with the population density and the ratio of the population over 65 years old in 31 Iranian provinces was also evaluated. According to our results, a strong association was found between the incidence of COVID-19 in Iranian provinces and their respective distance from Qom (p < 0.001; C = -0.68). The incidence of COVID-19 in Iranian provinces was also positively associated with the ratio of the population over 65 years old (p = 0.002; C = 0.53), while no significant association with population density was found (p = 0.39; C = 0.16). These results suggest that the implementation of travel restrictions from highly affected areas to other provinces could considerably reduce the rate of transmission of the disease throughout the country. Also, provinces with a higher proportion of elderly people (over 65) were identified as particularly at risk for the spread of SARS-CoV-2 infections. These results will contribute to better management of the COVID-19 outbreak in Iran, taking into account demographic and geographic characteristics of different provinces.

COVID-19 paraclinical diagnostic tools: Updates and future trends

MOTIVATION

COVID-19 is one of the most widely affecting pandemics. As for many respiratory viruses-caused diseases, diagnosis of COVID-19 relies on two main compartments: clinical and paraclinical diagnostic criteria. Rapid and accurate diagnosis is vital in such a pandemic. On one side, rapidity may enhance management effectiveness, while on the other, coupling efficiency and less costly procedures may permit more effective community-scale management.

METHODOLOGY AND MAIN STRUCTURE

In this review, we shed light on the most used and the most validated diagnostic tools. Furthermore, we intend to include few under-development techniques that may be potentially useful in this context. The practical intent of our work is to provide clinicians with a realistic summarized review of the essential elements in the applied paraclinical diagnosis of COVID-19.

Taking the inner route: spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil

Even though the impact of COVID-19 in metropolitan areas has been extensively studied, the geographic spread to smaller cities is also of great concern. We conducted an ecological study aimed at identifying predictors of early introduction, incidence rates of COVID-19 and mortality (up to 8 May 2020) among 604 municipalities in inner São Paulo State, Brazil. Socio-demographic indexes, road distance to the state capital and a classification of regional relevance were included in predictive models for time to COVID-19 introduction (Cox regression), incidence and mortality rates (zero-inflated binomial negative regression). In multivariable analyses, greater demographic density and higher classification of regional relevance were associated with both early introduction and increased rates of COVID-19 incidence and mortality. Other predictive factors varied, but distance from the State Capital (São Paulo City) was negatively associated with time-to-introduction and with incidence rates of COVID-19. Our results reinforce the hypothesis of two patterns of geographical spread of SARS-Cov-2 infection: one that is spatial (from the metropolitan area into the inner state) and another which is hierarchical (from urban centres of regional relevance to smaller and less connected municipalities). Those findings may apply to other settings, especially in developing and highly heterogeneous countries, and point to a potential benefit from strengthening non-pharmaceutical control strategies in areas of greater risk.

Neutrophilia and NETopathy as Key Pathologic Drivers of Progressive Lung Impairment in Patients With COVID-19

There is an urgent need for new therapeutic strategies to contain the spread of the novel coronavirus disease 2019 (COVID-19) and to curtail its most severe complications. Severely ill patients experience pathologic manifestations of acute respiratory distress syndrome (ARDS), and clinical reports demonstrate striking neutrophilia, elevated levels of multiple cytokines, and an exaggerated inflammatory response in fatal COVID-19. Mechanical respirator devices are the most widely applied therapy for ARDS in COVID-19, yet mechanical ventilation achieves strikingly poor survival. Many patients, who recover, experience impaired cognition or physical disability. In this review, we argue the need to develop therapies aimed at inhibiting neutrophil recruitment, activation, degranulation, and neutrophil extracellular trap (NET) release. Moreover, we suggest that currently available pharmacologic approaches should be tested as treatments for ARDS in COVID-19. In our view, targeting host-mediated immunopathology holds promise to alleviate progressive pathologic complications of ARDS and reduce morbidities and mortalities in severely ill patients with COVID-19.

Understanding epidemic data and statistics: A case study of COVID-19

The 2019 novel-coronavirus (COVID-19) has affected 181 countries with approximately 1197405 confirmed cases (by 5th April). Understanding the transmission dynamics of the infection in each country which got affected on a daily basis and evaluating the effectiveness of control policies are critical for our further actions. To date, the statistics of COVID-19 reported cases show that more than 80% of infected are mild cases of disease, around 14% of infected have severe complications, and about 5% are categorized as critical disease victims. Today's report (5th April 2020; daily updates in the prepared website) shows that the confirmed cases of COVID-19 in the United States, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092, respectively. Calculating the total case fatality rate (CFR) of Italy (4th April 2020), about 13.3% of confirmed cases have passed away. Compared with South Korea's rate of 1.8% (seven times lower than Italy) and China's 4% (69% lower than Italy), the CFR of Italy is too high. Some effective policies that yielded significant changes in the trend of cases were the lockdown policy in China, Italy, and Spain (the effect observed after some days), the shutdown of all nonessential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran.