Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World

Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN

This study aims to quantify the effectiveness of lockdown as a non-pharmacological solution for managing the COVID-19 pandemic. Daily COVID-19 death counts were collected for four states: California, Georgia, New Jersey, and South Carolina. The effectiveness of the lockdown was studied and the number of people saved during 7 days was evaluated. Five neural network models (MLP, FFNN, CFNN, ENN, and NARX) were implemented, and the results indicate that FFNN is the best prediction model. Based on this model, the total number of survivors over a 7-day period is 211, 270, 989, and 60 in California, Georgia, New Jersey, and South Carolina, respectively. The coefficients and weights of the FFNN for each state differ due to various factors, including socio-demographic conditions and the behavior of citizens towards lockdown laws. New Jersey and South Carolina have the most lockdowns and the least.

Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data

The COVID-19 pandemic caused by the novel SARS-COV-2 virus poses a great risk to the world. During the COVID-19 pandemic, observing and forecasting several important indicators of the epidemic (like new confirmed cases, new cases in intensive care unit, and new deaths for each day) helped prepare the appropriate response (e.g., creating additional intensive care unit beds, and implementing strict interventions). Various predictive models and predictor variables have been used to forecast these indicators. However, the impact of prediction models and predictor variables on forecasting performance has not been systematically well analyzed. Here, we compared the forecasting performance using a linear mixed model in terms of prediction models (mathematical, statistical, and AI/machine learning models) and predictor variables (vaccination rate, stringency index, and Omicron variant rate) for seven selected countries with the highest vaccination rates. We decided on our best models based on the Bayesian Information Criterion (BIC) and analyzed the significance of each predictor. Simple models were preferred. The selection of the best prediction models and the use of Omicron variant rate were considered essential in improving prediction accuracies. For the test data period before Omicron variant emergence, the selection of the best models was the most significant factor in improving prediction accuracy. For the test period after Omicron emergence, Omicron variant rate use was considered essential in deciding forecasting accuracy. For prediction models, ARIMA, lightGBM, and TSGLM generally performed well in both test periods. Linear mixed models with country as a random effect has proven that the choice of prediction models and the use of Omicron data was significant in determining forecasting accuracies for the highly vaccinated countries. Relatively simple models, fit with either prediction model or Omicron data, produced best results in enhancing forecasting accuracies with test data.

Evaluation of Immune Response to Mucosal Immunization with an Oral Probiotic-Based Vaccine in Mice: Potential for Prime-Boost Immunization against SARS-CoV-2

Following the conclusion of the COVID-19 pandemic, the persistent genetic variability in the virus and its ongoing circulation within the global population necessitate the enhancement of existing preventive vaccines and the development of novel ones. A while back, we engineered an orally administered probiotic-based vaccine, L3-SARS, by integrating a gene fragment that encodes the spike protein S of the SARS-CoV-2 virus into the genome of the probiotic strain E. faecium L3, inducing the expression of viral antigen on the surface of bacteria. Previous studies demonstrated the efficacy of this vaccine candidate in providing protection against the virus in Syrian hamsters. In this present study, utilizing laboratory mice, we assess the immune response subsequent to immunization via the gastrointestinal mucosa and discuss its potential as an initial phase in a two-stage vaccination strategy. Our findings indicate that the oral administration of L3-SARS elicits an adaptive immune response in mice. Pre-immunization with L3-SARS enhances and prolongs the humoral immune response following a single subcutaneous immunization with a recombinant S-protein analogous to the S-insert of the coronavirus in Enterococcus faecium L3.

The stock market reaction to COVID-19 vaccination in ASEAN

Previous studies have shown that the confirmed cases drive investor sentiment, reflecting the stock's return. Based on this, the vaccination growth is also expected to drive the investor's sentiment, which can be reflected in the return of the stock market in ASEAN.  Therefore, this study explores the vaccination impact on stock returns in ASEAN countries. This study contributes to the gap of taking the COVID-19 vaccination impact to the stock return into account by using the panel regression model with HC and Driscoll and Kraay robust covariance matrix estimator, which addresses the cross-dependency and heterogeneity problems. This study is one of the early studies of the topic, especially in ASEAN. The panel regression model with HC and Driscoll and Kraay robust covariance matrix estimator uses three variables: the daily stocks return, vaccine growth, and cases growth. It is a balanced panel data that includes six countries and 117 daily series data, making 702 observations used in the study.  The results show conflicting results where daily vaccination growth negatively affects the stock return. This problem can arise for several reasons, such as the uncertainty in the financial market and cross-dependency and heterogeneity detected in the model. We can see that the investors still have a negative sentiment because COVID-19 has resulted in uncertainty on the financial market in ASEAN. This gives us practical implications that the ASEAN country members' government needs to push vaccination policy more aggressively.

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

BACKGROUND

The public health sectors can use the forecasting applications to determine vaccine stock requirements to avoid excess or shortage stock. This prediction will ensure that immunization protection for COVID- 19 is well-distributed among African citizens.

OBJECTIVE

The aim of this study is to forecast vaccination rate for COVID-19 in Africa.

METHODS

The method used to estimate predictions is the hybrid forecasting models which predicts the COVID-19 vaccination rate (CVR). HARIMA is a hybrid of ARIMA and the Linear Regression model and HGRNN is a hybrid of Generalized Regression Neural Network (GRNN) and the Gaussian Process Regression (GPR) model which are used to improve predictive accuracy.

RESULTS

In this study, standard and hybrid forecasting models are used to evaluate new COVID-19 vaccine cases daily in May and June 2021. To evaluate the effectiveness of the models, the COVID-19 vaccine dataset for Africa was used, which included new vaccine cases daily from 13 January 2021 to 16 May 2021. Root Mean Squared Error (RMSE) and Error Percentage (EP) are used as evaluation measures in this process. The results obtained showed that the hybrid GRNN model performed better than the hybrid ARIMA model.

CONCLUSION

HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock to avoid vaccine wastage and save many lives.

[Perinatal and neonatal management plan for prevention and control of SARS-CoV-2 infection (3rd Edition)]

Since the global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020, the virus has been evolving through mutations to acquire enhanced infectivity but reduced virulence. With a wide vaccination coverage among Chinese population, China is entering a new stage of SARS-CoV-2 infection control. The Working Group for the Prevention and Control of Neonatal SARS-CoV-2 Infection in the Perinatal Period of the Editorial Committee of Chinese Journal of Contemporary Pediatrics released the first and second editions of perinatal and neonatal management plan for prevention and control of SARS-CoV-2 infection in January and March 2020, respectively. In order to follow up new prevention and control needs, it is necessary to update the management plan to better guide clinical practice. Therefore, the Working Group formulated the 3rd-edition plan.

Global emerging Omicron variant of SARS-CoV-2: Impacts, challenges and strategies

Newly emerging variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are continuously posing high global public health concerns and panic resulting in waves of coronavirus disease 2019 (COVID-19) pandemic. Depending on the extent of genomic variations, mutations and adaptation, few of the variants gain the ability to spread quickly across many countries, acquire higher virulency and ability to cause severe disease, morbidity and mortality. These variants have been implicated in lessening the efficacy of the current COVID-19 vaccines and immunotherapies resulting in break-through viral infections in vaccinated individuals and recovered patients. Altogether, these could hinder the protective herd immunity to be achieved through the ongoing progressive COVID-19 vaccination. Currently, the only variant of interest of SARS-CoV-2 is Omicron that was first identified in South Africa. In this review, we present the overview on the emerging SARS-CoV-2 variants with a special focus on the Omicron variant, its lineages and hybrid variants. We discuss the hypotheses of the origin, genetic change and underlying molecular mechanism behind higher transmissibility and immune escape of Omicron variant. Major concerns related to Omicron including the efficacy of the current available immunotherapeutics and vaccines, transmissibility, disease severity, and mortality are discussed. In the last part, challenges and strategies to counter Omicron variant, its lineages and hybrid variants amid the ongoing COVID-19 pandemic are presented.

An optimization framework for COVID-19 vaccine allocation and inventory management: A case study

As the novel coronavirus pandemic wreaked havoc globally, governments have implemented massive vaccination programs to tackle it. However, since the pandemic's emergence moves beyond the second year, some issues have stymied vaccination programs, including vaccine hesitancy, vaccine distribution inequality, new strains of the virus, and a possibility that the virus enters a stage of a requirement for cyclical vaccination. These challenges highlight the need for an appropriate mass COVID-19 vaccination program. Therefore, we attempt to address this problem by developing a bi-objective integrated vaccine allocation and inventory management framework. The goal is to minimize the system costs while maximizing the vaccination service level. Several important factors, such as multiple types of vaccines, the vaccines' perishability concept, demand uncertainty, and motivational strategy, have been addressed using dynamic planning. Besides that, the model development mechanism is carried out to be compatible and applicable to the current general vaccination program policies, forcing few strategic changes. Then, a case study concerning the vaccination program of the city of Mashhad in Iran is applied to the model. The results demonstrated significant advantages in total cost, vaccine shortage, and wastage compared to the current policy. Finally, the Lagrangian relaxation method is implemented on the model to strengthen further its capacity to handle larger-scale problems.

Measuring efficiency of the global fight against the COVID-19 pandemic

Objectives

The ongoing COVID-19 pandemic has led to an unprecedented loss of life and a severe economic downturn across the globe. Countries have adopted various social distancing and vaccination policies to reduce the spread of the disease and lessen the impact on healthcare systems. The world should work together to confront the disaster and challenge of COVID-19.

Methods

This study uses stochastic frontier analysis to measure the efficiency and influencing factors of the global response to COVID-19 epidemics and to provide follow-up strategies and reference guidelines.

Results

The results of this study show that (1) the average efficiency of the global response to COVID-19 is not good, with significant space for improvement of up to 60%; (2) adequate medical supplies and equipment can reduce mortality; (3) the initial implementation of social distancing policies and wearing masks can effectively reduce the infection rate; and (4) as infection rates and vaccination rates increase so that most people have basic immunity to COVID-19, the epidemic will gradually be reduced.

Conclusions

As the world becomes more aware of the COVID-19 disease, humans will gradually return to normal social interaction and lifestyles. The results of this study are expected to provide a reference for the future direction of the global fight against epidemics and the improvement of public health policies.

Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models

Globally, since the outbreak of the Omicron variant in November 2021, the number of confirmed cases of COVID-19 has continued to increase, posing a tremendous challenge to the prevention and control of this infectious disease in many countries. The global daily confirmed cases of COVID-19 between November 1, 2021, and February 17, 2022, were used as a database for modeling, and the ARIMA, MLR, and Prophet models were developed and compared. The prediction performance was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The study showed that ARIMA (7, 1, 0) was the optimum model, and the MAE, MAPE, and RMSE values were lower than those of the MLR and Prophet models in terms of fitting performance and forecasting performance. The ARIMA model had superior prediction performance compared to the MLR and Prophet models. In real-world research, an appropriate prediction model should be selected based on the characteristics of the data and the sample size, which is essential for obtaining more accurate predictions of infectious disease incidence.

The impact of the COVID-19 pandemic on the residential real estate market on the example of Szczecin, Poland

The aim of this article is to assess the impact of the socio-economic situation caused by the COVID-19 pandemic on the residential property market. Research in this area has been conducted all over the world. The conclusions of these studies are inconclusive. In different countries and different cities, the changes in the property markets observed from 2020 onwards differ. These differences concern both prices and the number of transactions. In this situation, it is important to conduct research in as many markets as possible. Summaries of such research will allow certain patterns to be identified, which will provide a global perspective on how the COVID-19 pandemic has affected the property market. The paper describes the short-term changes that have occurred in the formation of residential property prices and the number of transactions concluded. Separate analyses were conducted for property sales and rental transactions. The research was carried out on the data of over 5000 transactions in one of the biggest Polish cities - Szczecin. The city is divided administratively into four districts, which was also taken into account in the study. This made it possible to assess whether, just as the impact of the pandemic varied between cities and countries, it also varied at district level within one city. Confirming the diversity of impact will allow conclusions to be drawn as to whether the pandemic affects each market equally or whether different property markets are affected differently by restrictions and changes in the decisions of property market participants.

Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: a computer-aided drug design approach

Small molecules such as 9,10-dihydrophenanthrene derivatives have remarkable activity toward inhibition of SARS-CoV-2 3CLpro and COVID-19 proliferation, which show a strong correlation between their structures and bioactivity. Therefore, these small compounds could be suitable for clinical pharmaceutical use against COVID-19. The objective of this study was to remodel the structures of 9,10-dihydrophenanthrene derivatives to achieve a powerful biological activity against 3CLpro and favorable pharmacokinetic properties for drug design and discovery. Therefore, by the use of bioinformatics techniques, we developed robust 3D-QSAR models that are capable of describing the structure-activity relationship for 46 molecules based on 9,10-dihydrophenanthrene derivatives using CoMFA/SE (R 2 = 0.97, Q 2 = 0.81, R 2 pred = 0.95, c R 2 p = 0.71) and CoMSIA/SEHDA (R 2 = 0.94, Q 2 = 0.76, R 2 pred = 0.91, c R 2 p = 0.65) techniques. Accordingly, 96 lead compounds were generated based on a template molecule that showed the highest observed activity in vitro (T40, pIC50 = 5.81) and predicted their activities and bioavailability in silico. The rational screening outputs of 3D-QSAR, Molecular docking, ADMET, and MM-GBSA led to the identification of 9 novel modeled molecules as potent noncovalent drugs against SARS-CoV-2-3CLpro. Finally, by molecular dynamics simulations, the stability and structural dynamics of 3CLpro free and complex (PDB code: 6LU7) were discussed in the presence of samples of 9,10-dihydrophenanthrene derivative in an aqueous environment. Overall, the retrosynthesis of the proposed drug compounds in this study and the evaluation of their bioactivity in vitro and in vivo may be interesting for designing and discovering a new drug effective against COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02004-z.

Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study

OBJECTIVE

The COVID-19 outbreak was first reported in Wuhan, China, and has been acknowledged as a pandemic due to its rapid spread worldwide. Predicting the trend of COVID-19 is of great significance for its prevention. A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA.

DESIGN

Time-series study.

SETTING

The USA was the setting for this study.

MAIN OUTCOME MEASURES

Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models.

RESULTS

In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model.

CONCLUSIONS

The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.

An index of access to essential infrastructure to identify where physical distancing is impossible

To identify areas at highest risk of infectious disease transmission in Africa, we develop a physical distancing index (PDI) based on the share of households without access to private toilets, water, space, transportation, and communication technology and weight it with population density. Our results highlight that in addition to improving health systems, countries across Africa, especially in the western part of Africa, need to address the lack of essential domestic infrastructure. Missing infrastructure prevents societies from limiting the spread of communicable diseases by undermining the effectiveness of governmental regulations on physical distancing. We also provide high-resolution risk maps that show which regions are most limited in protecting themselves. We find considerable spatial heterogeneity of the PDI within countries and show that it is highly correlated with detected COVID-19 cases. Governments could pay specific attention to these areas to target limited resources more precisely to prevent disease transmission.

Lack of private infrastructure remains a major challenge potentially hampering a societies’ ability to contain the transmission of communicable diseases. Areas at high risk in Africa are identified based on access to essential basic infrastructure.

A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods

The emergence of machine learning-based in silico tools has enabled rapid and high-quality predictions in the biomedical field. In the COVID-19 pandemic, machine learning methods have been used in many topics such as predicting the death of patients, modeling the spread of infection, determining future effects, diagnosis with medical image analysis, and forecasting the vaccination rate. However, there is a gap in the literature regarding identifying epitopes that can be used in fast, useful, and effective vaccine design using machine learning methods and bioinformatics tools. Machine learning methods can give medical biotechnologists an advantage in designing a faster and more successful vaccine. The motivation of this study is to propose a successful hybrid machine learning method for SARS-CoV-2 epitope prediction and to identify nonallergen, nontoxic, antigen peptides that can be used in vaccine design from the predicted epitopes with bioinformatics tools. The identified epitopes will be effective not only in the design of the COVID-19 vaccine but also against viruses from the SARS family that may be encountered in the future. For this purpose, epitope prediction performances of random forest, support vector machine, logistic regression, bagging with decision tree, k-nearest neighbor and decision tree methods were examined. In the SARS-CoV and B-cell datasets used for education in the study, epitope estimation was performed again after the datasets were balanced with the synthetic minority oversampling technique (SMOTE) method since the epitope class samples were in the minority compared to the nonepitope class. The experimental results obtained were compared and the most successful predictions were obtained with the random forest (RF) method. The epitope prediction performance in balanced datasets was found to be higher than that in the original datasets (94.0% AUC and 94.4% PRC for the SMOTE-SARS-CoV dataset; 95.6% AUC and 95.3% PRC for the SMOTE-B-cell dataset). In this study, 252 peptides out of 20312 peptides were determined to be epitopes with the SMOTE-RF-SVM hybrid method proposed for SARS-CoV-2 epitope prediction. Determined epitopes were analyzed with AllerTOP 2.0, VaxiJen 2.0 and ToxinPred tools, and allergic, nonantigen, and toxic epitopes were eliminated. As a result, 11 possible nonallergic, high antigen and nontoxic epitope candidates were proposed that could be used in protein-based COVID-19 vaccine design ("VGGNYNY", "VNFNFNGLTG", "RQIAPGQTGKI", "QIAPGQTGKIA", "SYECDIPIGAGI", "STFKCYGVSPTKL", "GVVFLHVTYVPAQ", "KNHTSPDVDLGDI", "NHTSPDVDLGDIS", "AGAAAYYVGYLQPR", "KKSTNLVKNKCVNF"). It is predicted that the few epitopes determined by machine learning-based in silico methods will help biotechnologists design fast and accurate vaccines by reducing the number of trials in the laboratory environment.

Medical and non-medical students' knowledge, attitude and willingness towards the COVID-19 vaccine in China: a cross-sectional online survey

In the context of the novel Coronavirus outbreak and China’s official policy of free vaccination against COVID-19 for all, medical students’ attitudes and knowledge toward vaccines can influence public acceptance to some extent, however, the large base of non-medical students cannot be ignored. We aimed to investigate the knowledge, attitude, and willingness toward the COVID-19 vaccine among medical and non-medical students. Online surveys were completed by 652 medical students and 590 non-medical students to compare differences in knowledge and attitude of COVID-19 vaccine and vaccination willingness from three universities in the Zhejiang Province. The awareness rate of the vaccine among medical students (65.3%) was higher than that of non-medical students (53.6%). The approval rate of medical students for the safety and efficacy of the COVID-19 vaccine was higher than that of non-medical students. 81.8% of university students were willing to be vaccinated against COVID-19; Multiple stepwise regression analyses showed that lower class grades, rural students’ origin, COVID-19 vaccine attitude and higher cognition level of health self-management influenced the acceptance of COVID-19 vaccination among medical students. However, urban origin, COVID-19 vaccine attitude were the factors hindering non-medical students’ vaccination against COVID-19. The knowledge, attitude and willingness toward the COVID-19 vaccine on medical and non-medical students had different characteristics. Moreover, health self-management was associated with COVID-19 vaccination willingness. Staff involved in the university should pay more attention to the self-managementability of students, send out accurate and transparent information to enhance their cognitive level, further improving the students’ willingness to receive the COVID-19 vaccine.

Association of Online Search Trends With Vaccination in the United States: June 2020 Through May 2021

Stagnating COVID-19 vaccination rates and vaccine hesitancy remain a threat to public health. Improved strategies for real-time tracking and estimation of population-level behavior regarding vaccinations are needed. The aim of this study was to evaluate whether online search trends for COIVD-19 and influenza mirror vaccination rates. State-level weekly fraction of online searches for top vaccination-related search terms and CDC vaccination data were obtained from June 1, 2020, to May 31, 2021. Next, trends in online search and vaccination data for COVID-19 and influenza were analyzed for visual and quantitative correlation patterns using Spearman’s rank correlation analysis. Online searches in the US for COVID-19 vaccinations increased 2.71-fold (95% CI: 1.98-3.45) in the 4 weeks after the FDA emergency authorization compared to the precedent 4 weeks. In March-April 2021, US online searches reached a plateau that was followed by a decline of 83.3% (95% CI: 31.2%-135.3%) until May 31, 2021. The timing of peaks in online searches varied across US states. Online searches were strongly correlated with vaccination rates (r=0.71, 95% CI: 0.45 - 0.87), preceding actual reported vaccination rates in 44 of 51 states. Online search trends preceded vaccination trends by a median of 3.0 weeks (95% CI: 2.0-4.0 weeks) across all states. For influenza vaccination searches, seasonal peaks in September-October between 2016-2020 were noted. Influenza search trends highly correlated with the timing of actual vaccinations for the 2019-2020 (r=0.82, 95% CI: 0.64 – 0.93) and 2020-2021 season (r=0.91, 95% CI: 0.78 – 0.97). Search trends and real-world vaccination rates are highly correlated. Temporal alignment and correlation levels were higher for influenza vaccinations; however, only online searches for COVID-19 vaccination preceded vaccination trends. These findings indicate that US online search data can potentially guide public health efforts, including policy changes and identifying geographical areas to expand vaccination campaigns.

Testing the Club Convergence Dynamics of the COVID-19 Vaccination Rates Across the OECD Countries

Vaccines are essential to create a more resilient economic growth model. Ending the COVID-19 pandemic requires a more coordinated, effective, and equitable distribution of vaccines across the countries. Therefore, governments are in a race to increase the vaccination rates of the population. Given this backdrop, this paper focuses on the daily vaccinations per million data from March 1, 2021, to October 15, 2021, in 37 Organization for Economic Co-operation and Development (OECD) countries and examines the stochastic properties of the vaccination rates. We adopt the club convergence econometric methodology to investigate the club convergence paths of COVID-19 vaccination rates in OECD regions. The results indicate a significant convergence of the vaccination rates in seven clubs across 30 OECD countries. Moreover, there are seven OECD countries demonstrate non-convergent characteristics, which raises questions about ineffective vaccine balance. In addition, the paper also discusses the potential implications for the post-COVID-19 era.

Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends

Background: COVID-19 caused by the Severe Acute Respiratory Syndrome Coronavirus 2 placed the health systems around the entire world in a battle against the clock. While most of the existing studies aimed at forecasting the infections trends, our study focuses on vaccination trend(s). Material and methods: Based on these considerations, we used standard analyses and ARIMA modeling to predict possible scenarios in Romania, the second-lowest country regarding vaccinations from the entire European Union. Results: With approximately 16 million doses of vaccine against COVID-19 administered, 7,791,250 individuals had completed the vaccination scheme. From the total, 5,058,908 choose Pfizer−BioNTech, 399,327 Moderna, 419,037 AstraZeneca, and 1,913,978 Johnson & Johnson. With a cumulative 2147 local and 17,542 general adverse reactions, the most numerous were reported in recipients of Pfizer−BioNTech (1581 vs. 8451), followed by AstraZeneca (138 vs. 6033), Moderna (332 vs. 1936), and Johnson & Johnson (96 vs. 1122). On three distinct occasions have been reported >50,000 individuals who received the first or second dose of a vaccine and >30,000 of a booster dose in a single day. Due to high reactogenicity in case of AZD1222, and time of launching between the Pfizer−BioNTech and Moderna vaccine could be explained differences in terms doses administered. Furthermore, ARIMA(1,1,0), ARIMA(1,1,1), ARIMA(0,2,0), ARIMA(2,1,0), ARIMA(1,2,2), ARI-MA(2,2,2), ARIMA(0,2,2), ARIMA(2,2,2), ARIMA(1,1,2), ARIMA(2,2,2), ARIMA(2,1,1), ARIMA(2,2,1), and ARIMA (2,0,2) for all twelve months and in total fitted the best models. These were regarded according to the lowest MAPE, p-value (p < 0.05, p < 0.01, and p < 0.001) and through the Ljung−Box test (p < 0.05, p < 0.01, and p < 0.001) for autocorrelations. Conclusions: Statistical modeling and mathematical analyses are suitable not only for forecasting the infection trends but the course of a vaccination rate as well.

Contingency Management and SARS-CoV-2 Testing Among People Who Inject Drugs

OBJECTIVES

People who inject drugs (PWID) are especially vulnerable to morbidity and mortality as a result of SARS-CoV-2 infection because of social and physical health vulnerabilities. Routine testing for SARS-CoV-2 is critical to reduce transmission. Contingency management-the provision of tangible rewards to reinforce positive behavior-can promote the use of health services among PWID. Evidence is scarce on the utility of contingency management to promote SARS-CoV-2 testing. The objective of this study was to evaluate the effectiveness of contingency management to increase testing among PWID.

METHODS

SARS-CoV-2 testing was implemented at 9 syringe exchange program sites in partnership with an Oregon-based nonprofit organization for 5 weeks without contingency management and for 6 weeks with contingency management (a $10 financial incentive for testing) from February 1 through mid-April 2021. We measured rates of testing among syringe exchange program clients before and after implementation of contingency management.

RESULTS

Before contingency management, SARS-CoV-2 testing occurred during approximately 131 of 1410 (9.3%) client encounters, and 123 of 997 (12.3%) unique clients were tested. During contingency management, testing occurred during approximately 571 of 1756 (32.5%) client encounters, and 407 of 1151 (35.4%) unique clients were tested. Rates of testing increased from 0.04 (SD, 0.04) before contingency management implementation to 0.25 (SD, 0.15) after implementation (t₈ = -3.88; P = .005; Cohen d = 1.46).

CONCLUSIONS

Contingency management facilitated uptake of SARS-CoV-2 testing among PWID. Contingency management may be an effective strategy for improving communicable disease testing beyond testing for SARS-CoV-2 and for improving vaccine uptake among PWID and warrants additional research.

Comparison of Rapid Nucleic Acid Extraction Methods for SARS-CoV-2 Detection by RT-qPCR

Since 2020, humanity has been facing the COVID-19 pandemic, a respiratory disease caused by the SARS-CoV-2. The world’s response to pandemic went through the development of diagnostics, vaccines and medicines. Regarding diagnostics, an enormous challenge was faced due to shortage of materials to collect and process the samples, and to perform reliable mass diagnosis by RT-qPCR. In particular, time-consuming and high cost of nucleic acid extraction procedures have hampered the diagnosis; moreover, several steps in the routine for the preparation of the material makes the extracted sample susceptible to contamination. Here two rapid nucleic acid extraction reagents were compared as extraction procedures for SARS-CoV-2 detection in clinical samples by singleplex and multiplex RT-qPCR analysis, using different transport media, samples with high and low viral load, and different PCR machines. As observed, rapid nucleic acid extraction procedures can be applied for reliable diagnosis using a TaqMan-based assay, over multiple platforms. Ultimately, prompt RNA extraction may reduce costs with reagents and plastics, the chances of contamination, and the overall time to diagnosis by RT-qPCR.

Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement

This paper is devoted to modeling and predicting COVID-19 confirmed cases through a multiple linear regression. Especially, prediction intervals of the COVID-19 cases are extensively studied. Due to long-memory feature of the COVID-19 data, a heterogeneous autoregression (HAR) is adopted with Growth rates and Vaccination rates; it is called HAR-G-V model. Top eight affected countries are taken with their daily confirmed cases and vaccination rates. Model criteria results such as root mean square error (RMSE), mean absolute error (MAE), R 2 , AIC and BIC are reported in the HAR models with/without the two rates. The HAR-G-V model performs better than other HAR models. Out-of-sample forecasting by the HAR-G-V model is conducted. Forecast accuracy measures such as RMSE, MAE, mean absolute percentage error and root relative square error are computed. Furthermore, three types of prediction intervals are constructed by approximating residuals to normal and Laplace distributions, as well as by employing bootstrap procedure. Empirical coverage probability, average length and mean interval score are evaluated for the three prediction intervals. This work contributes three folds: a novel trial to combine both growth rates and vaccination rates in modeling COVID-19; construction and comparison of three types of prediction intervals; and an attempt to improve coverage probability and mean interval score of prediction intervals via bootstrap technique.

A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine

B-cell epitope prediction research has received growing interest since the development of the first method. B-cell epitope identification with the aid of an accurate prediction method is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. Nevertheless, using experimental methods in epitope mapping is very time-consuming, costly, and labor-intensive. Therefore, although successful predictions with in silico methods are very important in epitope prediction, there are limited studies in this area. The aim of this study is to propose a new approach for successfully predicting B-cell epitopes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, the SARS-CoV B-cell epitope prediction performances of different fuzzy learning classification models genetic cooperative competitive learning (GCCL), fuzzy genetics-based machine learning (GBML), Chi's method (CHI), Ishibuchi's method with weight factor (W), structural learning algorithm on vague environment (SLAVE) and the state-of-the-art ensemble fuzzy classification model were compared. The obtained results showed that the proposed ensemble approach has the lowest error in SARS-CoV B-cell epitope estimation compared to the base fuzzy learners (average error rates; ensemble fuzzy=8.33, GCCL=30.42, GBML=23.82, CHI=29.17, W=46.25, and SLAVE=20.42). SARS-CoV and SARS-CoV-2 have high genome similarities. Therefore, the most successful method determined for SARS-CoV B-cell epitope prediction was used in SARS-CoV-2 cell epitope prediction. Finally, the eventual B-cell epitope prediction results obtained for SARS-CoV-2 with the ensemble fuzzy classification model were compared with the epitope sequences predicted by the BepiPred server and immunoinformatics studies in the literature for the same protein sequences according to VaxiJen 2.0 scores. We hope that the developed epitope prediction method will help design effective vaccines and drugs against future outbreaks of the coronavirus family, especially SARS-CoV-2 and its possible mutations.

Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US

A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.

Discovery of 9,10-dihydrophenanthrene derivatives as SARS-CoV-2 3CLpro inhibitors for treating COVID-19

The epidemic coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has now spread worldwide and efficacious therapeutics are urgently needed. 3-Chymotrypsin-like cysteine protease (3CLpro) is an indispensable protein in viral replication and represents an attractive drug target for fighting COVID-19. Herein, we report the discovery of 9,10-dihydrophenanthrene derivatives as non-peptidomimetic and non-covalent inhibitors of the SARS-CoV-2 3CLpro. The structure-activity relationships of 9,10-dihydrophenanthrenes as SARS-CoV-2 3CLpro inhibitors have carefully been investigated and discussed in this study. Among all tested 9,10-dihydrophenanthrene derivatives, C1 and C2 display the most potent SARS-CoV-2 3CLpro inhibition activity, with IC50 values of 1.55 ± 0.21 μM and 1.81 ± 0.17 μM, respectively. Further enzyme kinetics assays show that these two compounds dose-dependently inhibit SARS-CoV-2 3CLprovia a mixed-inhibition manner. Molecular docking simulations reveal the binding modes of C1 in the dimer interface and substrate-binding pocket of the target. In addition, C1 shows outstanding metabolic stability in the gastrointestinal tract, human plasma, and human liver microsome, suggesting that this agent has the potential to be developed as an orally administrated SARS-CoV-2 3CLpro inhibitor.

Psychological Distress Among Occupational Health Professionals During Coronavirus Disease 2019 Pandemic in Spain: Description and Effect of Work Engagement and Work Environment

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the mental health of hospital health professionals has been widely described, but few studies have focused on occupational health professionals. Therefore, the objective of this study was to assess psychological distress (PD) of occupational health workers and its relationship with their work engagement (WE) and work environment characteristics. A cross-sectional survey was conducted. A sample of 499 nurses and physicians participated in the study. Variables included demographic data, work environment characteristics, work engagement Utrecht Work Engagement Scale (UWES-9) and psychological distress General Health Questionnaire (GHQ-12). The Chi-square Automatic Interaction Detection method was performed for data analysis. Data collection took place via the internet between April 23 and June 24, 2020. A total of 65.53% of the participants had PD, and the total mean score of the UWES-9 scale was 34.80 (SD = 10.69). Workload, conflicts, stressful situations, and less job satisfaction were significantly related to a higher percentage of PD (p < 0.05). Participants with low engagement showed higher levels of PD (76.7%; p < 0.001). The dedication was revealed as the most significant dimension. Interventions aimed at promoting resilience and coping strategies are suggested. WE should be fostered as a preventive measure against PD among occupational health workers. By protecting workers, occupational health departments have a shared responsibility with public health in containing the pandemic. Therefore, it is essential to prevent the psychological impact that this responsibility may have on occupational health workers by implementing prevention measures.

COVID-19 Vaccine Hesitancy among Patients with Inflammatory Bowel Disease Receiving Biologic Therapies in Kuwait: A Cross-Sectional Study

BACKGROUND

COVID-19 vaccinations have been shown to be effective in reducing risk of severe infection, hospitalization, and death. They have also been shown to be safe and effective in patients with inflammatory bowel disease (IBD) who are receiving biologic therapies. In this study, we aimed to evaluate the prevalence of vaccination among patients receiving biologic therapies for IBD.

METHODS

A single-center prospective cross-sectional study conducted at a tertiary care inflammatory bowel disease center in Kuwait. Data from patients with inflammatory bowel disease (IBD) who attended the gastroenterology infusion clinic from 1 June 2021 until 31 October 2021 were retrieved. Patients who received infliximab or vedolizumab at least six weeks before recruitment were included. The primary outcome was prevalence of COVID-19 vaccination. The secondary outcome was to assess whether prevalence of COVID-19 vaccination differed based on sex, age, type of biologic therapy and nationality.

RESULTS

The total number of inflammatory bowel disease (IBD) patients enrolled in the study was 280 (56.0% male and 44.0% female). Of the total, 112 (40.0%) patients were diagnosed with ulcerative colitis and 168 (60.0%) with Crohn's disease. The number of ulcerative colitis patients who were vaccinated was 49 (43.8%) and the number of Crohn's disease patients who were vaccinated was 68 (40.5%). The median age was 33.2 years and BMI was 24.8 kg/m2. With respect to the total number of patients, 117 (41.8%) were vaccinated with either BNT162b2 or ChAdOx1 nCoV-19 and 163 (58.2%) were not vaccinated. Female patients were more likely to receive the vaccine compared to male patients (83.0% vs. 63.8%, p < 0.001). In addition, patients above the age 50 were more likely to receive the vaccine than patients below the age of 50 (95.6% vs. 31.2% p < 0.001). Expatriates were more likely to receive the vaccine than citizens (84.8% vs. 25.0%, p < 0.001). There was no statistical difference between patients on infliximab and vedolizumab with regard to prevalence of vaccination (40.0% vs 48.0%, p = 0.34).

CONCLUSION

The overall prevalence of COVID-19 vaccination among patients with inflammatory bowel disease (IBD) on biologic therapies was lower than that of the general population and world health organization (WHO) recom-mendation. Female patients, patients above the age of 50, and expatriates were more likely to receive the vaccine. Physicians should reinforce the safety and efficacy of COVID-19 vaccines among patients, especially IBD patients on biologic therapies, who express hesitancy towards them.

Recommendation and Roadmap of Mass Vaccination against Coronavirus Disease 2019 Pandemic in Bangladesh as a Lower-Middle-Income Country

Low-income countries (LICs) and lower-middle-income countries (LMICs) are still deprived of the optimum doses of coronavirus disease 2019 (COVID-19) vaccines for their population, equal access and distribution, as well as mass immunization roadmaps to be implemented for achieving herd immunity and protection from the ongoing pandemic. In this short report, we are interacting with the world public health experts, as well as national and global leaders for warranting the mass vaccination drive to be more progressive against COVID-19 with equitable access of vaccines to LICs or LMICs to save the lives of the poorest country people and refugees. From several scientific databases, such as Google Scholar, PubMed, as well as national and international news websites, the data were collected data by utilizing appropriate keywords regarding the topic. Bangladesh might be exemplified in this brief communication as the representative of LMIC. As of October 14, 2021, 48% of the world's people have received at least one dose of the COVID-19 vaccine. In contrast, only 2.5% of people from LICs have come in under COVID-19 vaccination for at least a single shot. Both LICs and LMICs need far more vision and ambition, including political, administrative, and diplomatic progress along with enhancing the vaccination drive for their population to be immunized through simultaneous mass vaccination progress of other countries with implementing public health safety measures against the COVID-19 pandemic.

Evaluating Rates and Determinants of COVID-19 Vaccine Hesitancy for Adults and Children in the Singapore Population: Strengthening Our Community's Resilience against Threats from Emerging Infections (SOCRATEs) Cohort

COVID-19 vaccines are crucial for achieving sufficient immunisation coverage to manage the pandemic, but vaccine hesitancy persists. This study aimed to investigate the prevalence and determinants of vaccine hesitancy in adults and in parents for vaccinating their children using an integrated social cognition model. A community-based cohort in Singapore [N = 1623] completed a survey (wave 25) between June and July 2021 which measured their risk perceptions, distress, trust, vaccination beliefs, and vaccine intentions/behaviours. Results indicated low rates of hesitancy (9.9%) for own vaccination, with most concerns citing side effects, safety, and hasty development. Remaining respondents were vaccinated (69%) or intended to vaccinate (21%). The multivariable model (non-vaccinated respondents) indicated that, living with people in poor health, subjective norm, moral norm, benefits, and necessity of vaccination were associated with lower vaccine hesitancy (R² Cox & Snell: 51.4%; p < 0.001). Hesitancy rates were higher for children’s vaccination (15.9%), with male gender, lower perceived vaccine benefits, high COVID-19 risk perceptions, vaccination concerns, and necessity beliefs associated with higher odds of parental vaccine hesitancy (R² Cox & Snell = 36.4%; p < 0.001). While levels of vaccine acceptance are high, more targeted messages are needed. For adults’ vaccination, more emphasis should be on benefits and social gains, while for parental hesitancy, messages related to safety should be prioritised.

Immune Responses against SARS-CoV-2-Questions and Experiences

Understanding immune reactivity against SARS-CoV-2 is essential for coping with the COVID-19 pandemic. Herein, we discuss experiences and open questions about the complex immune responses to SARS-CoV-2. Some people react excellently without experiencing any clinical symptoms, they do not get sick, and they do not pass the virus on to anyone else ("sterilizing" immunity). Others produce antibodies and do not get COVID-19 but transmit the virus to others ("protective" immunity). Some people get sick but recover. A varying percentage develops respiratory failure, systemic symptoms, clotting disorders, cytokine storms, or multi-organ failure; they subsequently decease. Some develop long COVID, a new pathologic entity similar to fatigue syndrome or autoimmunity. In reality, COVID-19 is considered more of a systemic immune-vascular disease than a pulmonic disease, involving many tissues and the central nervous system. To fully comprehend the complex clinical manifestations, a profound understanding of the immune responses to SARS-CoV-2 is a good way to improve clinical management of COVID-19. Although neutralizing antibodies are an established approach to recognize an immune status, cellular immunity plays at least an equivalent or an even more important role. However, reliable methods to estimate the SARS-CoV-2-specific T cell capacity are not available for clinical routines. This deficit is important because an unknown percentage of people may exist with good memory T cell responsibility but a low number of or completely lacking peripheral antibodies against SARS-CoV-2. Apart from natural immune responses, vaccination against SARS-CoV-2 turned out to be very effective and much safer than naturally acquired immunity. Nevertheless, besides unwanted side effects of the currently available vector and mRNA preparations, concerns remain whether these vaccines will be strong enough to defeat the pandemic. Altogether, herein we discuss important questions, and try to give answers based on the current knowledge and preliminary data from our laboratories.