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Genomic surveillance (GS) programmes were crucial in identifying and quantifying the mutating patterns of SARS-CoV-2 during the COVID-19 pandemic. In this work, we develop a Bayesian framework to quantify the relative transmissibility of different variants tailored for regions with limited GS. We use it to study the relative transmissibility of SARS-CoV-2 variants in Chile. Among the 3443 SARS-CoV-2 genomes collected between January and June 2021, where sampling was designed to be representative, the Gamma (P.1), Lambda (C.37), Alpha (B.1.1.7), B.1.1.348, and B.1.1 lineages were predominant. We found that Lambda and Gamma variants' reproduction numbers were 5% (95% CI: [1%, 14%]) and 16% (95% CI: [11%, 21%]) larger than Alpha's, respectively. Besides, we observed a systematic mutation enrichment in the Spike gene for all circulating variants, which strongly correlated with variants' transmissibility during the studied period (r = 0.93, p-value = 0.025). We also characterised the mutational signatures of local samples and their evolution over time and with the progress of vaccination, comparing them with those of samples collected in other regions worldwide. Altogether, our work provides a reliable method for quantifying variant transmissibility under subsampling and emphasises the importance of continuous genomic surveillance. [ABSTRACT FROM AUTHOR]

Stay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to 70 % · 70 % = 49 % of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has presented multiple psychological challenges for healthcare workers, such as anxiety, depression, burnout, and substance use disorders. In this research, we investigate the different ways Romanian physicians dealt with the difficult period of the COVID-19 pandemic. We also analyze how positive and negative stress-reducing strategies, as well as demographic variables, affect their psychological resilience and quality of life. Our goal is to provide a comprehensive overview of how physicians coped with the unprecedented global health challenges. Methods: We carried out a national cross-sectional study of 265 physicians in Romania between January 2021 and January 2022 using a web-based questionnaire. The study employed a web-based questionnaire to assess coping mechanisms using the COPE inventory, resilience through the Connor-Davidson Resilience Scale 25 (CD-RISC 25), and quality of life via the WHOQOL-BREF scale. The COPE inventory, consisting of 60 items across 15 subscales, categorizes coping strategies into problem-focused, emotion-focused, and dysfunctional types, with each item rated on a 4-point scale. The CD-RISC 25 measures resilience on a 5-point Likert scale, with total scores ranging from 0 to 100. WHOQOL-BREF assesses quality of life through 26 items in 4 domains: physical, mental, social relations, and environmental, scored from 1 to 5 and converted to a 0–100 scale for domain scores. Univariate and multivariate linear regression models were employed to discern the intricate relationships between coping strategies, resilience levels, quality of life dimensions, and pertinent demographic factors. Results: The average CD-RISC score among participants was 66.2. The mean scores for the values for the QOL subscales were 64.0 for physical well-being, 61.7 for psychological well-being, 61.2 for social relationships, and 64.7 for environment. Individuals tend to use problem-focused and emotion-focused coping more than dysfunctional mechanisms, according to the COPE inventory. Problem-focused and emotion-focused coping are positively correlated with resilience, while dysfunctional coping is negatively correlated. Resilience is significantly influenced by gender and professional status, with males and senior specialists reporting higher levels while younger physicians and residents reporting lower levels. Conclusions: Our data points to specific protective characteristics and some detrimental factors on physicians' resilience and quality of life during the pandemic. [ABSTRACT FROM AUTHOR]

Background: Healthcare workers (HCWs) have shown increased adherence to infection control practices during the COVID-19 pandemic. However, there is a need to assess their adherence to and attitude toward COVID-19 guidelines after being vaccinated. The purpose of this pilot study is to assess the adherence to and attitudes toward the adherence to COVID-19 guidelines among HCWs who have been vaccinated. Methods: A retrospective cross-sectional design was employed. One hundred and eight participants were recruited via email from a medical center in the Midwest United States. The participants completed online surveys measuring the level of adherence to and attitudes toward the adherence to COVID-19 guidelines. The response rate was 5.4%. Findings: Most participants were female (73.1%) and white (82.4%). The participants adhered to COVID-19 guidelines 79.7% of the time. The most frequently followed guidelines were performing hand hygiene, wearing a respirator or well-fitting mask in areas where patients may be present, and wearing eye protection when entering the room for a patient with suspected or confirmed COVID-19 infection. The least performed precautions were performing COVID-19 testing after exposure to a suspected or confirmed COVID-19 case and maintaining social distancing. There was a significant decrease in the perceived importance of adherence to COVID-19 precautions post-vaccination (p <.001, 95% CI [−0.78, −0.35]). Conclusions: The increased perception of safety after receiving COVID-19 vaccination may negatively influence HCWs' adherence to COVID-19 precautionary guidelines. Continuous education and monitoring of HCWs' safety practices are important to influence HCWs' attitudes to adhere to COVID-19 precautions, particularly after vaccination. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, it became evident that the effectiveness of applying intervention measures is significantly influenced by societal acceptance, which, in turn, is affected by the processes of opinion formation. This article explores one among the many possibilities of coupled opinion–epidemic systems. The findings reveal either intricate periodic patterns or chaotic dynamics, leading to substantial fluctuations in opinion distribution and, consequently, significant variations in the total number of infections over time. Interestingly, the model exhibits a protective pattern. [ABSTRACT FROM AUTHOR]

Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts—often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy—is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks of infectious diseases requires a firm understanding of the effectiveness of such containment strategies. To this end, mathematical models provide a promising tool. In this work, we present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates the assumption of limited TTIQ capacities, providing insights into the reduced effectiveness of testing and tracing in high prevalence scenarios. In addition, we account for potential transmission during the early phase of an infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Our numerical experiments inspired by the early spread of COVID-19 in Germany demonstrate the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent, which is representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses reveal both disease-dependent and disease-independent factors that impede or enhance the success of TTIQ. Studying the diminishing impact of TTIQ along simulations of an epidemic wave, we highlight consequences for intervention strategies. [ABSTRACT FROM AUTHOR]

Copyright of Annali di Igiene, Medicina Preventiva e di Comunità is the property of Societa Editrice Universo s.r.l. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A model of epidemic dynamics is developed that incorporates continuous variables for infection age and vaccination age. The model analyzes pre-symptomatic and symptomatic periods of an infected individual in terms of infection age. This property is shown to be of major importance in the severity of the epidemic, when the infectious period of an infected individual precedes the symptomatic period. The model also analyzes the efficacy of vaccination in terms of vaccination age. The immunity to infection of vaccinated individuals varies with vaccination age and is also of major significance in the severity of the epidemic. Application of the model to the 2003 SARS epidemic in Taiwan and the COVID-19 epidemic in New York provides insights into the dynamics of these diseases. It is shown that the SARS outbreak was effectively contained due to the complete overlap of infectious and symptomatic periods, allowing for the timely isolation of affected individuals. In contrast, the pre-symptomatic spread of COVID-19 in New York led to a rapid, uncontrolled epidemic. These findings underscore the critical importance of the pre-symptomatic infectious period and the vaccination strategies in influencing the dynamics of an epidemic. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present a model depicting the relationship between the behavioral intention of tourists in the conditions prevailing during a pandemic and other variables. Design/methodology/approach: In constructing the research procedure, two measurements of tourist behavioral intention were taken into account, which were taken far apart in time. In verifying the developed model, the results of surveys of 1,615 people carried out in June 2021 and 917 people carried out in December 2021 were considered. Findings: As a result of the habituation process, tourists show greater acceptance of the restrictions. Practical implications: Information on the basis of which companies make management decisions plays a significant role in the creation of company value. In the tourism sector, the information concerns primarily consumer behavior. Originality/value: Changes over time in risk perception, health protection motivation, and reactance due to perceived pandemic-related restrictions were taken into account in the context of behavioral intention towards tourism. [ABSTRACT FROM AUTHOR]

The essence of the stochastic processes behind the empirical data on infection and fatality during pandemics is the complex interdependence between biological and social factors. Their balance can be checked on the data of new virus outbreaks, where the population is unprepared to fight the viral biology and social measures and healthcare systems adjust with a delay. Using a complex systems perspective, we combine network mapping with K-means clustering and multifractal detrended fluctuations analysis to identify typical trends in fatality rate data. We analyse global data of (normalised) fatality time series recorded during the first two years of the recent pandemic caused by the severe acute respiratory syndrome coronavirus 2 as an appropriate example. Our results reveal six clusters with robust patterns of mortality progression that represent specific adaptations to prevailing biological factors. They make up two significant groups that coincide with the topological communities of the correlation network, with stabilising (group g1) and continuously increasing rates (group g2). Strong cyclic trends and multifractal small-scale fluctuations around them characterise these patterns. The rigorous analysis and the proposed methodology shed more light on the complex nonlinear shapes of the pandemic's main characteristic curves, which have been discussed extensively in the literature regarding the global infectious diseases that have affected humanity throughout its history. In addition to better pandemic preparedness in the future, the presented methodology can also help to differentiate and predict other trends in pandemics, such as fatality rates, caused simultaneously by different viruses in particular geographic locations. [ABSTRACT FROM AUTHOR]

Describing viral outbreaks, such as the COVID-19 pandemic, often involves employing compartmental models composed of ordinary differential equation (ODE) systems. Estimating the parameter values for these ODE models is crucial and relies on accessible data. To accurately represent realistic pandemic scenarios with diverse situations, it is necessary to consider model parameters as time dependent. However, estimating such time-dependent parameters, like transition rates in compartmental models, is notoriously challenging due to the unknown function class of these parameters. In this study, we propose a novel approach by using an Augmented Kalman Smoother (AKS) combined with an Expectation-Maximization (EM) algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. Our approach can be applied to general ODE systems with time-varying parameters, requiring no prior knowledge of model parameters or additional assumptions on the function class of the ODE time dependencies. A key advantage of our method compared to other methods is that it does not require assumptions about the parameterization of the serial interval distribution for estimating SIRD model parameters. Applying our approach to COVID-19 data in Germany, we adequately describe time-series data with strong fluctuations and multiple waves, obtaining non-parametric model-based time-course estimates for the effective reproduction number. [ABSTRACT FROM AUTHOR]

Copyright of Berliner Journal für Soziologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Emergent research identifies cultural tightness‐looseness as an important factor for understanding cross‐national outcome differences during the coronavirus pandemic. Because perceived tightness‐looseness can be measured as an individual‐level difference rather than a nation‐level difference, and because tightness‐looseness may shift during large‐scale crises, we investigated whether such shifts occurred early in the coronavirus pandemic in both China (a relatively tight nation, n = 3642) and the U.S. (a relatively loose nation, n = 3583) across three cohorts. Tightness increased across cohorts in China and reduced across cohorts in the U.S. These changes transmitted corresponding indirect effects whereby compliance and institutional trust (scientific and government) about the pandemic were increased in China across cohorts, but decreased in the U.S. across cohorts. These patterns extend advice that national governments can increase compliance and trust via "tightening" by cautioning against norm‐setters signaling the reverse (that norms about compliance are loose) given the outcomes observed in the U.S. samples. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global threat. Despite strict control measures implemented worldwide and immunization using novel vaccines, the pandemic continues to rage due to emergence of several variants of SARS-CoV-2 with increased transmission and immune escape. The rapid spread of variants of concern (VOC) in the recent past has created a massive challenge for the control of COVID-19 pandemic via the currently used vaccines. Vaccines that are safe and effective against the current and future variants of SARS-CoV-2 are essential in controlling the COVID-19 pandemic. Rapid production and massive rollout of next-generation vaccines against the variants are key steps to control the COVID-19 pandemic and to help us return to normality. Coordinated surveillance of SARS-CoV-2, rapid redesign of new vaccines and extensive vaccination are needed to counter the current SARS-CoV-2 variants and prevent the emergence of new variants. In this article, we review the latest information on the VOCs and variants of interest (VOIs) and present the information on the clinical trials that are underway on evaluating the effectiveness of COVID-19 vaccines on VOCs. We also discuss the current challenges posed by the VOCs in controlling the COVID-19 pandemic and future strategies to overcome the threat posed by the highly virulent and rapidly transmissible variants of SARS-CoV2. The COVID-19 is a contagious respiratory disease caused by a virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in 2019. The COVID-19 has now spread to all part of the world and has become a global threat. Even after the strict control measures and immunization programs to prevent the disease, COVID-19 is still causing destruction due to appearance of new strains of SARS-CoV-2 that transmit faster and capable of escaping the immunity. The faster spread of the new strains of viruses that cause more severe disease is the biggest challenge to control the COVID-19 pandemic by using the presently available vaccines. To control the COVID-19 pandemic we urgently need safe and effective vaccines against the corona viral variants. This can be achieved by tracking the appearance of new viral types, design and rapid production and supply of new vaccines against the virus. This article presents the latest information on the new types of SARS-CoV-2, and on the status of vaccine trials and their effectiveness against these viruses. Similarly, the information on the challenges posed by the new viral strains in controlling the COVID-19 and future strategies to overcome the threat posed by corona viruses is also provided. [ABSTRACT FROM AUTHOR]

Background: Recent studies have raised the issue of delayed cancer care during the COVID-19 pandemic, but the extent of delays and cancellations in cancer treatment, screening and diagnosis varied widely by geographic region and study design, highlighting the need for further research. Methods: We used the Oncology Dynamics (OD) database featuring data from a cross-sectional, partially retrospective survey to analyze treatment delays in 30,171 GI cancer patients from five European countries (Germany, France, UK, Spain, and Italy). Risk factors for treatment delays were identified using multivariable logistic regression models. Results: Treatment delays were documented in 1342 (4.5%) of the study patients, with most patients having a delay of less than 3 months (3.2%). We observed decisive differences of treatment delay in relation to geographical, healthcare- and patient-related factors. Treatment delay was highest in France (6.7%) and Italy (6.5%) and lowest in Spain (1.9%, p < 0.001). 5.9% of patients treated at general hospitals but only 1.9% of those treated by office-based physicians experienced treatment delays (p < 0.001). Moreover, the difference between lines of therapy was highly significant and ranged from 7.2% for early-stage patients in primary therapy to 2.6% in advanced/metastatic cancer patients receiving 4th or later line therapy (p < 0.001). Finally, the proportion of cases with delayed treatments increased from 3.5% in asymptomatic patients (ECOG 0) to 9.9% in bedridden patients (ECOG IV, p < 0.001). Results were confirmed in multivariable logistic regression models. Summary: Our data highlight the problem of delayed treatment of tumor patients in the context of the COVID-19 pandemic. Identified risk factors for delayed treatment, such as poor general health or treatment in smaller hospitals, offer starting points for future concepts of "pandemic preparedness". [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the necessity for policymakers to design interventions that allow to promptly resume economic activities while taking control of the healthcare emergency. We analyze the response of differentiated policy measures by exploiting a meta-population SEIR model based on transaction data that map human mobility through daily physical transactions performed by cardholders of a major Italian bank. We calibrate multiple counterfactual scenarios and study the impact of alternative combinations of tailored mobility restrictions with different intensity across sectors. Although the Retail sector accounts for the largest portion of mobility and drive results in terms of infections and consumption dynamics, other economic activities, such as those related to Restaurants, have a relevant role in the design of the optimal policy. Finally, we show how the proposed approach can be used by policymakers to evaluate the trade-off between economic and healthcare impacts by identifying the alternative policy restrictions that minimize either the economic impact given a certain level of infections or the spread of contagion for a target value of economic impact. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic can be defined as a biological disaster whose impact is particularly evident for children and adolescents, vulnerable populations because of their level of cognitive and affective development. For this reason, it is of paramount relevance to develop actions focused on disaster-related prevention and preparedness, including psycho-education. In this study, we aimed at exploring key informants' representation of the pandemic, also to identify the contents for the first two levels of a psycho-educational web-application for children and adolescents, i.e., PandHEMOT® (Pandemics - Helmet for EMOTions). We involved 25 experts with different professional roles through semi-structured interviews about a variety of aspects related to the pandemic. We coded each proposition according to four categories (i.e., Basic knowledge, Procedural knowledge, Safety behaviors, and Consequences). First, a generalized linear mixed model revealed that Safety behaviors was the most frequent category, followed by Consequences, and in turn by Procedural and Basic knowledge. Second, through a cluster analysis we identified three clusters corresponding to different response patterns and pandemic representations. Third, we conducted a thematic analysis to select the items to be inserted in the first and second level of PandHEMOT®, focused on the nature of pandemics and pandemic-related protective measures. Notwithstanding the limitations related to the small sample, this research permitted to explore the representation of different experts concerning the COVID-19 pandemic. Also, these findings enabled to identify the contents for the initial levels of a web-application designed for increasing children and adolescents' knowledge on pandemics, emotions, and coping strategies. This initiative can be an example of a psycho-educational tool to enhance young people's disasterrelated resilience. [ABSTRACT FROM AUTHOR]

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas. [ABSTRACT FROM AUTHOR]

Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts. [ABSTRACT FROM AUTHOR]

Rotavirus A (RVA) remains a leading cause of acute gastroenteritis (AGE) hospitalizations in children worldwide. During the COVID-19 pandemic, a reduction in vaccination coverage in Brazil and elsewhere was observed, and some reports have demonstrated a reduction in AGE notifications during the pandemic. This study aims to investigate the diversity and prevalence of RVA genotypes in children and adults presenting with AGE symptoms in Brazil during the COVID-19 pandemic between 2020 and 2022. RVA was screened using RT-qPCR; then, G and P genotypes were characterized using one-step multiplex RT-PCR. A total of 2173 samples were investigated over the three-year period, and we detected RVA in 7.7% of samples (n = 167), being 15.5% in 2020, 0.5% in 2021, and 13.8% in 2022. Higher RVA prevalence was observed in the Northeastern region (19.3%) compared to the Southeastern (6.1%) and Southern regions (5.5%). The most affected age group was children aged between 0 and 6 months old; however, this was not statistically significant. Genotyping and phylogenetic analysis identified the emergence of G6P[8] during the period; moreover, it was detected in 10.6% of samples in 2020 and in 83.5% in 2022. In contrast, the prevalence of G3P[8], the previous dominant genotype, decreased from 72.3% in 2020 to 11.3% in 2022. We also identified unusual strains, such as G3P[9] and G9P[4], being sporadically detected during the period. This is the first report on the molecular epidemiology and surveillance of RVA during the COVID-19 pandemic period in Brazil. Our study provides evidence for the importance of maintaining high and sustainable levels of vaccine coverage to protect against RVA disease. Furthermore, it highlights the need to maintain nationwide surveillance in order to monitor future trends and changes in the epidemiology of RVA in Brazil. [ABSTRACT FROM AUTHOR]

Introduction: Socioeconomic factors have been recognized by the WHO as determinants of health, and it is important to consider these factors in decision making to curb existing inequality in vaccination for SARS-CoV-2, which causes COVID-19. Objective: We aimed to determine whether there is a correlation between socioeconomic factors and vaccination worldwide and measure inequality. Method: A study of secondary sources was carried out to assess inequality in vaccination against COVID-19 worldwide and its association with socioeconomic factors. For this assessment, 169 countries were chosen from January 2020 to March 2022 using LibreOffice and JASP 0.16.1.10. Several mathematical models and statistical tests were used, including a normality test, an analysis of frequencies and proportions, a Kruskal–Wallis test, Spearman's correlations, a Lorenz curve, a Concentration Index, and a slope. Results: Correlations were found between socioeconomic factors and vaccination with one, two, and three doses. As the GDP showed correlations of 0.71 for one dose and 0.82 for three doses, we found that the greater the competitiveness of the countries, the higher the percentage of vaccinated individuals in their populations. According to the Concentration Index, there was greater inequality in vaccination with regard to receiving a higher number of doses, as reflected in the life expectancy indices of 0.16–0.19 and 0.50. The continent with the highest degree of inequality was Africa, and the continent with the lowest degree was America. South Americans were vaccinated with two doses at a rate of 6.19%/month, which was 4.3 times faster than Africans, with 72% of the population being vaccinated in South America, compared to only 16% in Africa. Conclusion: There is inequality in vaccination against COVID-19 with one, two, and three doses, which is associated with socioeconomic factors. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two‐dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID‐19, which includes the effect of nonpharmaceutical interventions and a concurrent two‐dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio‐economic costs associated with the epidemics. We calibrate our model to the 2021 COVID‐19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first‐dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first‐dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID‐19 vaccination campaign, and increase preparedness for future epidemics. [ABSTRACT FROM AUTHOR]

We develop in this manuscript a method for performing estimation and inference for the reproduction number of an epidemiological outbreak, focusing on the COVID-19 epidemic. The estimator is time-dependent and uses spline modelling to adapt to changes in the outbreak. This is accomplished by directly modelling the series of new infections as a function of time and subsequently using the derivative of the function to define a time-varying reproduction number, which is then used to assess the evolution of the epidemic for several countries. [ABSTRACT FROM AUTHOR]

Non-pharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic (and previous health crises) have included measures to restrict interaction between people and minimize non-essential mobility. Therefore, tourism travel is one of the main areas affected by the restrictions. Even when the majority of the population is vaccinated, some risk of infection will remain, and governments are obliged to consider NPI measures that balance the health risk of outbreaks against the economic and social benefits of resuming tourist activity. This study analyzes the effect of each of four categories of NPIs (Social Distancing; Public Healthcare-System Improvements; Tourist Controls; and Capacity and Opening-Hours Regulation) on three major objectives (the resumption of tourism activity; tourist travel intention; and the minimization of public health risk), taking a triangular perspective (destination managers, domestic tourists, and public healthcare managers, respectively). While it is difficult to fulfil public healthcare objectives while simultaneously responding to the economic interests of tourism-industry stakeholders, the study finds that, under vaccinated-population conditions, tourist controls (e.g. COVID Certificate) alongside improvements to the public healthcare system (e.g. adequate resourcing and an efficient epidemiological monitoring system) could constitute a viable combination of measures. [ABSTRACT FROM AUTHOR]

People are known for good predictions in domains they have rich experience with, such as everyday statistics and intuitive physics. But how well can they predict for problems they lack experience with, such as the duration of an ongoing epidemic caused by a new virus? Amid the first wave of COVID‐19 in China, we conducted an online diary study, asking each of over 400 participants to predict the remaining duration of the epidemic, once per day for 14 days. Participants' predictions reflected a reasonable use of publicly available information but were meanwhile biased, subject to the influence of negative affect and future time perspectives. Computational modeling revealed that participants neither relied on prior distributions of epidemic durations as in inferring everyday statistics, nor on mechanistic simulations of epidemic dynamics as in computing intuitive physics. Instead, with minimal experience, participants' predictions were best explained by similarity‐based generalization of the temporal pattern of epidemic statistics. In two control experiments, we further confirmed that such cognitive algorithm is not specific to the epidemic scenario and that minimal and rich experience do lead to different prediction behaviors for the same observations. We conclude that people generalize patterns in recent history to predict the future under minimal experience. [ABSTRACT FROM AUTHOR]

While public health crises such as the coronavirus pandemic transcend national borders, practical efforts to combat them are often instantiated at the national level. Thus, national group identities may play key roles in shaping compliance with and support for preventative measures (e.g., hygiene and lockdowns). Using data from 25,159 participants across representative samples from 21 nations, we investigated how different modalities of ingroup identification (attachment and glorification) are linked with reactions to the coronavirus pandemic (compliance and support for lockdown restrictions). We also examined the extent to which the associations of attachment and glorification with responses to the coronavirus pandemic are mediated through trust in information about the coronavirus pandemic from scientific and government sources. Multilevel models suggested that attachment, but not glorification, was associated with increased trust in science and compliance with federal COVID‐19 guidelines. However, while both attachment and glorification were associated with trust in government and support for lockdown restrictions, glorification was more strongly associated with trust in government information than attachment. These results suggest that both attachment and glorification can be useful for promoting public health, although glorification's role, while potentially stronger, is restricted to pathways through trust in government information. [ABSTRACT FROM AUTHOR]

Aim: To use a Delphi-panel-based assessment of the effectiveness of different non-pharmaceutical interventions (NPI) in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (susceptible, exposed, infectious, removed). Methods: We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs on the SARS-CoV-2 transmission rate R0 in Germany. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on the course of the pandemic. The model was populated with the Delphi-panel results and varied in sensitivity analyses. Results: Efficacy and compliance estimates for the three most effective NPIs were as follows: test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%. Applying all NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic. A combination of four NPIs at consented compliance rates might curb the CoViD-19 pandemic. Conclusion: Employing an evidence-educated Delphi-panel approach can support SARS-CoV-2 modelling. Future curbing scenarios require a combination of NPIs. A Delphi-panel-based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has highlighted the issue of mobilization policies, that is, government practices directed at making the mass public voluntarily perform various behaviors for the collective benefit during a crisis. As COVID‐19 vaccinations became accessible, governments faced the challenge of mass vaccination mobilization in order to achieve herd immunization. Aiming to effectively realize this goal, policy designers and regulators worldwide considered various mobilizing tools for vaccination compliance, including rewards and penalties, as they targeted vaccine opposers and hesitators, while trying to avoid the crowding‐out effect among individuals who were intrinsically motivated to get vaccinated. However, the unique circumstances of the Coronavirus pandemic may have eliminated the crowding‐out effect. Thus, our study explored the effect of regulation in the form of positive and negative incentivizing tools (i.e., rewards and penalties) during the coronavirus pandemic on vaccination intentions of 1184 Israeli citizens, prior to the national vaccination campaign. Results indicate that (1) both negative and positive incentives have a similar positive effect on individuals who declare they will not get vaccinated and those who hesitate to get the shot; (2) both positive and negative incentives induce the crowding‐out effect; and (3) negative incentives generate a larger crowding‐out effect in individuals who report preliminary intentions to get vaccinated, compared to positive ones. This emphasizes the need to avoid the crowding‐out effect during the current and similar crises, and suggests considering applying a gradual and adaptive policy design in order to maximize regulatory efficacy and compliance. [ABSTRACT FROM AUTHOR]

China is experiencing the same situation that we observed in Europe at the beginning of 2022, where all cases are due the Omicron-related variants and recombinations. Transmission dynamics of SARS-CoV-2 Omicron variant infections in Hangzhou, Zhejiang, China, January-February 2022. Figure 1 summarizes the distribution of the observed lineages in the last few weeks in Europe, USA, Italy, and China. Is a new COVID-19 wave coming from China due to an unknown variant of concern?. [Extracted from the article]

The aim of this paper is to present legal restrictions related to religious ceremonies during the Covid-19 pandemic, which are not currently in force in Poland but are being applied in selected European countries. The study examines the possible extent of their acceptance by believers in Poland, a country with persistently high levels of religious observance. It transpires that negative evaluations prevail in the case of the following restrictions: advanced registration for participation in religious services, the possibility of only vaccinated persons participating in services, and singing being prohibited. On the other hand, a more positive view was taken of designating specific places that can be occupied in the place of worship, and the requirement for all attendees to wear a mask (including priests and leaders of religious practices). The research revealed a general tendency for those who participate more frequently in religious practices to view restrictions more negatively. [ABSTRACT FROM AUTHOR]

As coronavirus variants are constantly occurring, we tried to understand more about the omicron and delta variants that have hit the world. We provided dynamic information on the case fatality rate (CFR) of the Omicron variant over time and to compare it with that of the Delta variant through meta‐analysis. Twenty‐four countries were selected by submission counts, submission dates, and confirmed cases. We defined the Delta or the Omicron epidemic period for individual countries as when each variant is over 90%. We further analyzed the Omicron period by dividing it into the initial plateau, increasing, and decreasing phases according to the number of newly confirmed daily cases. Finally, the meta‐analysis examined the summary and between‐study heterogeneity. The CFR of COVID‐19 during the Omicron epidemic was lower than that during the Delta epidemic (odds ratio [OR]: 0.252, 95% confidence interval [CI] 0.205−0.309). The CFR of COVID‐19 during the initial plateau phase of Omicron was higher than during other phases. (OR: 1.962, 95% CI 1.607−2.397). The CFR of COVID‐19 during the increasing phase was lower than during the decreasing phases (OR: 0.412, 95% CI 0.342–0.498). The Omicron variant had lower CFR compared to the Delta variant, and the initial plateau phase had higher CFR compared to the noninitial phases. These results can help establish global health policies for COVID‐19 in the future. [ABSTRACT FROM AUTHOR]

Preface: Coronavirus has several mechanisms that facilitate its entry into the target cell such as cholesterol by acting concurrently with angiotensin-converting enzyme (ACE2) at entry site, where the virus uses it to increase its entry and spread. We conducted a practical research for measuring Cholesterol, Triglyceride (TG) and Lactate dehydrogenase concentrations in COVID-19 patients. Objectives: The aim of this research is to see the correlation between Cholesterol, TG and Lactate dehydrogenase and the severity of coronavirus infection. Methods: This study is made at Al-Zahra Teaching Hospital during the period from March 2021 to July 2021 and it was performed on two groups: patients group that involved 30 samples (15 male and 15 female), age ranging from 18 to 84 years old and another group consisting of 30 individuals who were not infected with SARS-CoV-2. Results: Our findings show that the high value for markers under this study associated with coronavirus was detected in lactate dehydrogenase (5 0 5. 0 7 ± 2 6. 9 3 4 IU/l), (2 2 7. 8 3 ± 2 4. 6 6 0 Mg/dl) for TG and (2 2 7. 5 3 ± 1 9. 8 0 5 Mg/dl) for cholesterol. High concentration of cholesterol is shown in the age category between 54 and 64 years is represented as (487 Mg/dl and 502 Mg/dl), TG at ages 42, 54 and 64 years is represented as 497, 509 and 521 Mg/dl, Lactate dehydrogenase at ages 80–84 years that represented is as 739, 809 and 812 IU/l. According to physiological parameters which were elevated, a high concentration was observed in almost both sexes, as males were more affected than females when determining cholesterol at (500 Mg/dl), while females had more than males with respect to lactate dehydrogenase and TGs at (520 Mg/dl and 800 IU/l), respectively. In the correlation test, we found the highest significant differences, according to the correlation test, between cholesterol and lactate dehydrogenase at (1) cholesterol and TG at (0.000), TG and lactate dehydrogenase at (0.739). Conclusions: We conclude that COVID-19 patients suffer from increased markers under the studies on cholesterol, TG and lactate dehydrogenase. [ABSTRACT FROM AUTHOR]

Background: After COVID-19 vaccines received approval, vaccination campaigns were launched worldwide. Initially, these were characterized by a shortage of vaccine supply, and specific risk groups were prioritized. Once supply was guaranteed and vaccination coverage saturated, the focus shifted from risk groups to anti-vaxxers, the under-aged population, and regions of low coverage. At the same time, hopes to reach herd immunity by vaccination campaigns were put into perspective by the emergence and spread of more contagious and aggressive viral variants. Particularly, concerns were raised that not all vaccines protect against the new-emerging variants. The objective of this study is to introduce a predictive model to quantify the effect of vaccination campaigns on the spread of SARS-CoV-2 viral variants. Methods and findings: The predictive model introduced here is a comprehensive extension of the one underlying the pandemic preparedness tool CovidSim 2.0 (http://covidsim.eu/). The model is age and spatially stratified, incorporates a finite (but arbitrary) number of different viral variants, and incorporates different vaccine products. The vaccines are allowed to differ in their vaccination schedule, vaccination rates, the onset of vaccination campaigns, and their effectiveness. These factors are also age and/or location dependent. Moreover, the effectiveness and the immunizing effect of vaccines are assumed to depend on the interaction of a given vaccine and viral variant. Importantly, vaccines are not assumed to immunize perfectly. Individuals can be immunized completely, only partially, or fail to be immunized against one or many viral variants. Not all individuals in the population are vaccinable. The model is formulated as a high-dimensional system of differential equations, which is implemented efficiently in the programming language Julia. As an example, the model was parameterized to reflect the epidemic situation in Germany until November 2021 and future dynamics of the epidemic under different interventions were predicted. In particular, without tightening contact reductions, a strong epidemic wave is predicted during December 2021 and January 2022. Provided the dynamics of the epidemic in Germany, in late 2021 administration of full-dose vaccination to all eligible individuals (e.g. by mandatory vaccination) would be too late to have a strong effect on reducing the number of infections in the fourth wave in Germany. However, it would reduce mortality. An emergency brake, i.e., an incidence-based stepwise lockdown, would be efficient to reduce the number of infections and mortality. Furthermore, to specifically account for mobility between regions, the model was applied to two German provinces of particular interest: Saxony, which currently has the lowest vaccine rollout in Germany and high incidence, and Schleswig-Holstein, which has high vaccine rollout and low incidence. Conclusions: A highly sophisticated and flexible but easy-to-parameterize model for the ongoing COVID-19 pandemic is introduced. The model is capable of providing useful predictions for the COVID-19 pandemic, and hence provides a relevant tool for epidemic decision-making. The model can be adjusted to any country, and the predictions can be used to derive the demand for hospital or ICU capacities. [ABSTRACT FROM AUTHOR]

Background. Human behaviour, economic activity, vaccination, and social distancing are inseparably entangled in epidemic management. This study aims to investigate the effects of various parameters such as stay-at-home restrictions, work hours, vaccination, and social distance on the containment of pandemics such as COVID-19. Methods. To achieve this, we have developed an agent based model based on a time-dynamic graph with stochastic transmission events. The graph is constructed from a real-world social network. The edges of graph have been categorized into three cate-gories: home, workplaces, and social environment. The conditions needed to mitigate the spread of wild-type COVID-19 and the delta variant have been analyzed. Our purposeful agent based model has carefully executed tens of thousands of individual-based simulations. We propose simple relationships for the trade-offs between effective reproduction number (Re), transmission rate, working hours, vaccination, and stay-at-home restrictions. Results. We have found that the effect of a 13.6% increase in vaccination for wild-type (WT) COVID-19 is equivalent to reducing four hours of work or a one-day stay-at-home restriction. For the delta, 20.2% vaccination has the same effect. Also, since we can keep track of household and non-household infections, we observed that the change in household transmission rate does not significantly alter the Re. Household infections are not limited by transmission rate due to the high frequency of connections. For the specifications of COVID-19, the Re depends on the non-household transmissions rate. Conclusions. Our findings highlight that decreasing working hours is the least effective among the non-pharmaceutical interventions. Our results suggest that policymakers decrease work-related activities as a last resort and should probably not do so when the effects are minimal, as shown. Furthermore, the enforcement of stay-at-home restrictions is moderately effective and can be used in conjunction with other measures if absolutely necessary. [ABSTRACT FROM AUTHOR]

Since the start of the pandemic of COVID-19, it was clear that vaccination was the best way to combat it. A few vaccines were produced and approved in the last year. Many questions about the vaccinations' efficacy and safety arose due to this unprecedented vaccine development effort. This study aimed to evaluate the short-term side effects after receiving the available COVID-19 vaccines by the students of the Kut university college. This research was conducted as a cross-sectional, retrospective study using an online questionnaire distributed among COVID-19 vaccine recipients. The number of students who are enrolled in this study is 804, nearly (81%) of our study subjects were symptomatic after receiving the vaccination. Participants with a history of SARS-CoV-2 infection, comorbid diseases, females, non-smokers, and AstraZeneca vaccine receivers showed significantly (p<0.05) more severe side effects. In conclusion, the side effects described by our undergraduate students after receiving vaccinations from Pfizer BioNTech, Oxford AstraZeneca, and Sinopharm are similar to those reported in clinical trials, showing that these vaccines have safe profiles. More research is needed to assess the efficiency of current vaccinations in preventing SARS-CoV-2 reinfections. [ABSTRACT FROM AUTHOR]

Using the recently proposed Susceptible–Asymptomatic–Infected–Vaccinated–Removed (SAIVR) model, we study the impact of key factors affecting COVID-19 vaccine rollout effectiveness and the susceptibility to resurgent epidemics. The SAIVR model expands the widely used Susceptible–Infectious–Removed (SIR) model for describing epidemics by adding compartments to include the asymptomatic infected (A) and the vaccinated (V) populations. We solve the model numerically to make predictions on the susceptibility to resurgent COVID-19 epidemics depending on initial vaccination coverage, importation loads, continuing vaccination, and more contagious SARS-CoV-2 variants, under persistent immunity and immunity waning conditions. The parameters of the model represent reported epidemiological characteristics of the SARS-CoV-2 virus such as the disease spread in countries with high levels of vaccination coverage. Our findings help explain how the combined effects of different vaccination coverage levels and waning immunity lead to distinct patterns of resurgent COVID-19 epidemics (either surges or endemic), which are observed in countries that implemented different COVID-19 health policies and achieved different vaccinated population plateaus after the vaccine rollouts in the first half of 2021. [ABSTRACT FROM AUTHOR]

Background: The aim of the study is to derive deeper insights into the control of the spread of COVID-19 during the second half of 2021, from seven countries that are among the earliest to have accelerated the deployment of COVID-19 vaccines. Methodology. This study used data from the Global COVID-19 Index and Google COVID-19 Community Mobility Reports. Data was extracted on the 5th of each month from July to December 2021. Seven countries were selected-United Kingdom, United States of America, Israel, Canada, France, Italy, and Austria. The sample comprised number of new cases, hospitalisations, ICU admissions and deaths due to COVID-19, government stringency measures, partial and full vaccination coverage, and changes in human mobility. Principal component analysis was conducted, and the results were interpreted and visualized through 2-dimensional and 3-dimensional plots to reveal the systematic patterns of the data.Results: The first three principal components captured around 77.3% of variance in the data. The first component was driven by the spread of COVID-19 (31.6%), the second by mobility activities (transit, retail, and recreational) (24.3%), whereas the third by vaccination coverage, workplace-related mobility, and government stringency measures (21.4%). Visualizations showed lower or moderate levels of severity in COVID-19 during this period for most countries. By contrast, the surge in the USA was more severe especially in September 2021. Human mobility activities peaked in September for most countries and then receded in the following months as more stringent government measures were imposed, and countries began to grapple with a surge in COVID-19 cases.Conclusion: This study delineated the spread of COVID-19, human mobility patterns, widespread vaccination coverage, and government stringency measures on the overall control of COVID-19. While at least moderate levels of stringency measures are needed, high vaccine coverage is particularly important in curbing the spread of this disease. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak seems to be the most dangerous challenge of the third millennium due to its highly contagious nature. Amongst natural molecules for COVID-19 treatment, the flavonoid molecule quercetin (QR) is currently considered one of the most promising. QR is an active agent against SARS and MERS due to its antimicrobial, antiviral, anti-inflammatory, antioxidant, and some other beneficial effects. QR may hold therapeutic potential against SARS-CoV-2 due to its inhibitory effects on several stages of the viral life cycle. In fact, QR inhibits viral entry, absorption, and penetration in the SARS-CoV virus, which might be at least partly explained by the ability of QR and its derivatives to inhibit 3-chymotrypsin-like protease (3CLpro) and papain-like protease (PLpro). QR is a potent immunomodulatory molecule due to its direct modulatory effects on several immune cells, cytokines, and other immune molecules. QR-based nanopreparations possess enhanced bioavailability and solubility in water. In this review, we discuss the prospects for the application of QR as a preventive and treatment agent for COVID-19. Given the multifactorial beneficial action of QR, it can be considered a very valid drug as a preventative, mitigating, and therapeutic agent of COVID-19 infection, especially in synergism with zinc, vitamins C, D, and E, and other polyphenols. [ABSTRACT FROM AUTHOR]

Many countries have secured larger quantities of COVID-19 vaccines than their population is willing to take. The abundance and the large variety of vaccines created not only an unprecedented intensity of vaccine related public discourse, but also a historical moment to understand vaccine hesitancy better. Yet, the heterogeneity of hesitancy by vaccine types has been neglected in the existing literature so far. We address this problem by analysing the acceptance and the assessment of five vaccine types. We use information collected with a nationally representative survey at the end of the third wave of the COVID-19 pandemic in Hungary. During the vaccination campaign, individuals could reject the assigned vaccine to wait for a more preferred alternative that enables us to quantify revealed preferences across vaccine types. We find that hesitancy is heterogenous by vaccine types and is driven by individuals' trusted source of information. Believers of conspiracy theories are more likely to evaluate the mRNA vaccines (Pfizer and Moderna) unacceptable. Those who follow the advice of politicians are more likely to evaluate vector-based (AstraZeneca and Sputnik) or whole-virus vaccines (Sinopharm) acceptable. We argue that the greater selection of available vaccine types and the free choice of the individual are desirable conditions to increase the vaccination rate in societies. [ABSTRACT FROM AUTHOR]

Introduction: School closures due to the COVID-19 pandemic affect children's daily structure, mealtimes, physical activity, and sleeping habits, possibly exacerbating weight gain, particularly in vulnerable children with overweight and obesity. This study aimed to evaluate both perceived and objectively measured weight gain in children in the Netherlands during the COVID-19 pandemic and the effect of prior lifestyle intervention. Methods: A total of 150 children of the Children, Obesity and Lifestyle during COVID-19 (COLC) study (cohort A) reported perceptions of weight change during the COVID-19 pandemic. Anthropometric data of 65 children with overweight and obesity were collected at the expertise Centre for Overweight Adolescent and Children's Healthcare in the same period (COACH; cohort B). Results: In cohort A, 43% of children with overweight and obesity perceived weight gain during the pandemic, compared to 15% of lean children. In cohort B, the BMI z-score increased significantly (+0.065 SD) within 5 months. Participation in a lifestyle intervention for >1 year and having parents with Dutch background was associated with less weight gain, specifically in children with obesity. Discussion/Conclusion: In particular, children with overweight and obesity seem to be at risk for accelerated weight gain during the COVID-19 pandemic. Prior long-term participation in a lifestyle intervention protects against this weight gain, which emphasizes the importance of strong support for vulnerable populations during health crises and pleads for wide implementation of lifestyle interventions for children. [ABSTRACT FROM AUTHOR]

Numerous prediction models of SARS-CoV-2 pandemic were proposed in the past. Unknown parameters of these models are often estimated based on observational data. However, lag in case-reporting, changing testing policy or incompleteness of data lead to biased estimates. Moreover, parametrization is time-dependent due to changing age-structures, emerging virus variants, non-pharmaceutical interventions, and vaccination programs. To cover these aspects, we propose a principled approach to parametrize a SIR-type epidemiologic model by embedding it as a hidden layer into an input-output non-linear dynamical system (IO-NLDS). Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. This includes data issues such as known delays or biases in reporting. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a specific SIR-type model and data of Germany and Saxony demonstrating good prediction performances. Our approach can estimate and compare the relative effectiveness of non-pharmaceutical interventions and provide scenarios of the future course of the epidemic under specified conditions. It can be translated to other data sets, i.e., other countries and other SIR-type models. [ABSTRACT FROM AUTHOR]

We analyze the relaxation of non-pharmaceutical interventions (NPIs) under an increasing number of vaccinations in Germany. For the spread of SARS-CoV-2 we employ a SIR-type model that accounts for age-dependence and includes realistic contact patterns between age groups. The implementation of NPIs occurs on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. We account for spatial heterogeneity and commuting activities in between regions in Germany, and the testing of commuters is considered as a further NPI. We include the ongoing vaccination process and analyze the effect of the B.1.617.2 (Delta) variant, which is considered to be 40%–60% more infectious then the currently dominant B.1.1.7 (Alpha) variant. We explore different opening scenarios under the ongoing vaccination process by assuming that local restrictions are either lifted in early July or August with or without continued wearing of masks and testing. Our results indicate that we can counteract the resurgence of SARS-CoV-2 despite the Delta variant with appropriate timing for the relaxation of NPIs. In all cases, however, school children are hit the hardest. Author summary: One of the greatest challenges within the Covid-19 pandemic is to identify the timing and amount of non-pharmaceutical interventions (face masks, travel bans, school closures, etc). In the year 2021 more and more people are getting vaccinated. When can we finally lift all restrictions and stop wearing masks? In order to provide more insights to this question, we use a mathematical model which is capable of simulating the effects of non-pharmaceutical interventions in Germany while accounting for age-dependent factors as well as commuting activities between regions. We include the vaccination process and analyze the much more infectious Delta coronavirus variant. We simulate scenarios that consider the timing of the return to pre-pandemic contacts as well as when to suspend wearing masks and testing. Our results show that a later opening by 1 August in combination with masks and testing reduces the chance of a further infection wave considerably. From the retrospective view of the revision, we see that the rise in infections at the end of summer could have been well predicted by our scenarios that considered lifting of NPIs in July as it happened in many places. In all of our scenarios, the infection manifests in the younger age groups. [ABSTRACT FROM AUTHOR]

Recent research points towards age- and gender-specific transmission of COVID-19 infections and their outcomes. The effect of gender, however, has been overlooked in past modelling approaches of COVID-19 infections. The aim of our study is to explore how gender-specific contact behavior affects gender-specific COVID-19 infections and deaths. We consider a compartment model to establish short-term forecasts of the COVID-19 epidemic over a time period of 75 days. Compartments are subdivided into different age groups and genders, and estimated contact patterns, based on previous studies, are incorporated to account for age- and gender-specific social behaviour. The model is fitted to real data and used for assessing the effect of hypothetical contact scenarios all starting at a daily level of 10 new infections per million population. On day 75 after the end of the lockdown, infection rates are highest among the young and working-age, but they also have increased among the old. Sex ratios reveal higher infection risks among women than men at working ages; the opposite holds true at old age. Death rates in all age groups are twice as high for men as for women. Small changes in contact rates at working and young ages have a considerable effect on infections and mortality at old age, with elderly men being always at higher risk of infection and mortality. Our results underline the high importance of the non-pharmaceutical mitigation measures (NPMM) in low-infection phases of the pandemic to prevent that an increase in contact rates leads to higher mortality among the elderly, even if easing measures take place among the young. At young and middle ages, women's contribution to increasing infections is higher due to their higher number of contacts. Gender differences in contact rates may be one pathway that contributes to the spread of the disease and results in gender-specific infection rates and their mortality outcome. To further explore possible pathways, more data on contact behavior and COVID-19 transmission is needed, which includes gender- and socio-demographic information. [ABSTRACT FROM AUTHOR]

This work investigates the temporal statistical structure of time series of electric field (EF) intensity recorded with the aim of exploring the dynamical patterns associated with periods with different human activity in urban areas. The analyzed time series were obtained from a sensor of the EMF RATEL monitoring system installed in the campus area of the University of Novi Sad, Serbia. The sensor performs wideband cumulative EF intensity monitoring of all active commercial EF sources, thus including those linked to human utilization of wireless communication systems. Monitoring was performed continuously during the years 2019 and 2020, allowing us to investigate the effects on the patterns of EF intensity of varying conditions of human mobility, including regular teaching and exam activity within the campus, as well as limitations to mobility related to the COVID-19 pandemic. Time series analysis was performed using both simple statistics (mean and variance) and combining the information-theoretic measure of information storage (IS) with the method of surrogate data to quantify the regularity of EF dynamic patterns and detect the presence of nonlinear dynamics. Moreover, to assess the possible coexistence of dynamic behaviors across multiple temporal scales, IS analysis was performed over consecutive observation windows lasting one day, week, month, and year, respectively coarse grained at time scales of 6 min, 30 min, 2 h, and 1 day. Our results document that the EF intensity patterns of variability are modulated by the movement of people at daily, weekly, and monthly scales, and are blunted during periods of restricted mobility related to the COVID-19 pandemic. Mobility restrictions also affected significantly the regularity of the EF intensity time series, resulting in lower values of IS observed simultaneously with a loss of nonlinear dynamics. Thus, our analysis can be useful to investigate changes in the global patterns of human mobility both during pandemics or other types of events, and from this perspective may serve to implement strategies for safety assessment and for optimizing the design of networks of EF sensors. [ABSTRACT FROM AUTHOR]

During the COVID-19 epidemic, draconian countermeasures forbidding nonessential human activities have been adopted in several countries worldwide, providing an unprecedented setup for testing and quantifying the current impact of humankind on climate and for driving potential sustainability policies in the postpandemic era from a perspective of complex systems. In this study, we consider heterogeneous sources of environmental and human activity observables, considered as components of a complex socioenvironmental system, and apply information theory, network science, and Bayesian inference to analyze their structural relations and nonlinear dynamics between January 2019 and August 2020 in northern Italy, i.e., before, during, and after the national lockdown. The topological structure of a complex system strongly impacts its collective behavior; therefore, mapping this structure is essential to fully understand the functions of the system as a whole and its fragility to unexpected disruptions or shocks. To this aim, we unravel the causal relationships between the 16 environmental conditions and human activity variables, mapping the backbone of the complex interplay between intervening physical observables—such as NO2 emissions, energy consumption, intervening climate variables, and different flavors of human mobility flows—to a causal network model. To identify a tipping point during the period of observation, denoting the presence of a regime shift between distinct network states (i.e., before and during the shock), we introduce a novel information-theoretic method based on statistical divergence widely used in statistical physics. We find that despite a measurable decrease in NO2 concentration, due to an overall decrease in human activities, locking down a region as a climate change mitigation is an insufficient remedy to reduce emissions. Our results provide a functional characterization of socioenvironmental interdependent systems, and our analytical framework can be used, more generally, to characterize environmental changes and their interdependencies using statistical physics. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed significant challenges in modeling its complex epidemic transmissions, infection and contagion, which are very different from known epidemics. The challenges in quantifying COVID-19 complexities include effectively modeling its process and data uncertainties. The uncertainties are embedded in implicit and high-proportional undocumented infections, asymptomatic contagion, social reinforcement of infections, and various quality issues in the reported data. These uncertainties become even more apparent in the first 2 months of the COVID-19 pandemic, when the relevant knowledge, case reporting and testing were all limited. Here we introduce a novel hybrid approach SUDR by expanding the foundational compartmental epidemic Susceptible-Infected-Recovered (SIR) model with two compartments to a Susceptible-Undocumented infected-Documented infected-Recovered (SUDR) model. First, SUDR (1) characterizes and distinguishes Undocumented (U) and Documented (D) infections commonly seen during COVID-19 incubation periods and asymptomatic infections. Second, SUDR characterizes the probabilistic density of infections by capturing exogenous processes like clustering contagion interactions, superspreading, and social reinforcement. Lastly, SUDR approximates the density likelihood of COVID-19 prevalence over time by incorporating Bayesian inference into SUDR. Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes. To capture the uncertainties of temporal transmission and social reinforcement during COVID-19 contagion, the transmission rate is modeled by a time-varying density function of undocumented infectious cases. By sampling from the mean-field posterior distribution with reasonable priors, SUDR handles the randomness, noise and sparsity of COVID-19 observations widely seen in the public COVID-19 case data. The results demonstrate a deeper quantitative understanding of the above uncertainties, in comparison with classic SIR, time-dependent SIR, and probabilistic SIR models. [ABSTRACT FROM AUTHOR]

A widely used tool for analysing the Covid-19 pandemic is the standard SIR model. It seems often to be used as a black box, not taking into account that this model was derived as a special case of the seminal Kermack–McKendrick theory from 1927. This is our starting point. We explain the setup of the Kermack–McKendrick theory (passing to a discrete approach) and use medical information for specializing to a model called by us an adapted K-McK-model. It includes effects of vaccination, mass testing and mutants. We demonstrate the use of the model by applying it to the development in Germany and show, among others things, that a comparatively mild intervention reducing the time until quarantine by one day would lead to a drastic improvement. [ABSTRACT FROM AUTHOR]

The Bateman linear differential equations describing in time nuclear levels interconnected by transitions (in general, each with its own rate) are adapted to the epidemiology case where only two infection and recovery rates are used. Time‐dependent functions describing successive generations of infected people are thus derived. Closed mathematical expressions allow an easy calculation/prediction of the properties of these generations and therefore of the whole epidemics. The main limitation consists in reaching the point when the number of susceptible to the infection people substantially decreases. As testing ground, reasonable descriptions of the newly registered COVID‐19 cases per day is achieved for Europe and the United States till December 2020, including several so‐called Pandemic waves. The approach of the present work, by using transmission from one generation of infected people to a new generation allows a much more detailed tracking of the Pandemic spread than the approaches used so far. For example, the most widely used Susceptible Infectious Recovered (SIR) models (and their variants) use instead transitions from one big compartment of susceptible people to another new one of infected people followed by recovery leading to a third compartment. Clear correlations are found between features of the Pandemic process and the emergence of new SARS‐CoV‐2 variants, with some of them (especially the α$\alpha$ variant) immediately influencing the spread of the infection disease in the Pandemic wave of Autumn of 2020. [ABSTRACT FROM AUTHOR]

Whole-genome sequencing (WGS) has played a significant role in understanding the epidemiology and biology of SARS-CoV-2 virus. Here, we investigate the use of SARS-CoV-2 WGS in Southeast and East Asian countries as a genomic surveillance during the COVID-19 pandemic. Nottingham–Indonesia Collaboration for Clinical Research and Training (NICCRAT) initiative has facilitated collaboration between the University of Nottingham and a team in the Research Center for Biotechnology, National Research and Innovation Agency (BRIN), to carry out a small number of SARS-CoV-2 WGS in Indonesia using Oxford Nanopore Technology (ONT). Analyses of SARS- CoV-2 genomes deposited on GISAID reveal the importance of clinical and demographic metadata collection and the importance of open access and data sharing. Lineage and phylogenetic analyses of two periods defined by the Delta variant outbreak reveal that: (1) B.1.466.2 variants were the most predominant in Indonesia before the Delta variant outbreak, having a unique spike gene mutation N439K at more than 98% frequency, (2) Delta variants AY.23 sub-lineage took over after June 2021, and (3) the highest rate of virus transmissions between Indonesia and other countries was through interactions with Singapore and Japan, two neighbouring countries with a high degree of access and travels to and from Indonesia. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. [ABSTRACT FROM AUTHOR]