Relaxing restrictions at the pace of vaccination increases freedom and guards against further COVID-19 waves

Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios

In the realm of infectious disease control, accurate modeling of the transmission dynamics is pivotal. As human mobility and commuting patterns are key components of communicable disease spread, we introduce a novel travel time aware metapopulation model. Our model aims to enhance estimations of disease transmission. By providing more reliable assessments on the efficacy of interventions, curtailing personal rights or human mobility behavior through interventions can be minimized. The proposed model is an advancement over traditional compartmental models, integrating explicit transmission on travel and commute, a factor available in agent-based models but often neglected with metapopulation models. Our approach employs a multi-edge graph ODE-based (Graph-ODE) model, which represents the intricate interplay between mobility and disease spread. This granular modeling is particularly important when assessing the dynamics in densely connected urban areas or when heterogeneous structures across entire countries have to be assessed. The given approach can be coupled with any kind of ODE-based model. In addition, we propose a novel multi-layer waning immunity model that integrates waning of different paces for protection against mild and severe courses of the disease. As this is of particular interest for late-phase epidemic or endemic scenarios, we consider the late-phase of SARS-CoV-2 in Germany. The results of this work show that accounting for resolved mobility significantly influences the pattern of outbreaks. The improved model provides a refined tool for predicting outbreak trajectories and evaluating intervention strategies in relation to mobility by allowing us to assess the transmission that result on traveling. The insights derived from this model can serve as a basis for decisions on the implementation or suspension of interventions, such as mandatory masks on public transportation. Eventually, our model contributes to maintaining mobility as a social good while reducing exuberant disease dynamics potentially driven by travel activities.

The impact of COVID-19 vaccination rate on traffic recovery

At the beginning of the COVID-19 pandemic in the US, traffic sharply fell due to social distancing policies in many locations. Correspondingly, many regions observed an increase in traffic volume (traffic recovery) as the pandemic eased in 2022. We examine how vaccination rates influence traffic recovery in Los Angeles County (LAC), controlling for differences in case counts, demographics, and socioeconomic factors across areas with different vaccination rates. We use arterial road sensor data as a proxy for the traffic volume within each ZIP code, alongside their respective demographic and socioeconomic characteristics. We find that a higher vaccination rate is statistically significantly associated with a larger traffic recovery, a finding that remains consistent across all explored models. This implies that an increased vaccination rate could reduce the public's perception of the risks of disease infection, leading to a larger traffic recovery. Moreover, we found that variables including population, income, race, work industry, and commuting preferences were correlated with vaccination rates. This highlights potential inequalities based on race, income, and industry sectors in the COVID-19 vaccination and a return to normal traffic flow.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

A quantitative evaluation of the impact of vaccine roll-out rate and coverage on reducing deaths: insights from the first 2 years of COVID-19 epidemic in Iran

BACKGROUND

Vaccination has played a pivotal role in reducing the burden of COVID-19. Despite numerous studies highlighting its benefits in reducing the risk of severe disease and death, we still lack a quantitative understanding of how varying vaccination roll-out rates influence COVID-19 mortality.

METHODS

We developed a framework for estimating the number of avertable COVID-19 deaths (ACDs) by vaccination in Iran. To achieve this, we compared Iran's vaccination roll-out rates with those of eight model countries that predominantly used inactivated virus vaccines. We calculated net differences in the number of fully vaccinated individuals under counterfactual scenarios where Iran's per-capita roll-out rate was replaced with that of the model countries. This, in turn, enabled us to determine age specific ACDs for the Iranian population under counterfactual scenarios where number of COVID-19 deaths are estimated using all-cause mortality data. These estimates covered the period from the start of 2020 to 20 April 2022.

RESULTS

We found that while Iran would have had an approximately similar number of fully vaccinated individuals under counterfactual roll-out rates based on Bangladesh, Nepal, Sri Lanka, and Turkey (~ 65-70%), adopting Turkey's roll-out rates could have averted 50,000 (95% confidence interval: 38,100-53,500) additional deaths, while following Bangladesh's rates may have resulted in 52,800 (17,400-189,500) more fatalities in Iran. Surprisingly, mimicking Argentina's slower roll-out led to only 12,600 (10,400-13,300) fewer deaths, despite a higher counterfactual percentage of fully vaccinated individuals (~ 79%). Emulating Montenegro or Bolivia, with faster per capita roll-out rates and approximately 50% counterfactual full vaccination, could have prevented more deaths in older age groups, especially during the early waves. Finally, replicating Bahrain's model as an upper-bound benchmark, Iran could have averted 75,300 (56,000-83,000) deaths, primarily in the > 50 age groups.

CONCLUSIONS

Our analysis revealed that faster roll-outs were consistently associated with higher numbers of averted deaths, even in scenarios with lower overall coverage. This study offers valuable insights into future decision-making regarding infectious disease epidemic management through vaccination strategies. It accomplishes this by comparing various countries' relative performance in terms of timing, pace, and vaccination coverage, ultimately contributing to the prevention of COVID-19-related deaths.

Rotavirus A during the COVID-19 Pandemic in Brazil, 2020-2022: Emergence of G6P[8] Genotype

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.

Polarimetric imaging for the detection of synthetic models of SARS-CoV-2: A proof of concept

Objective

To conduct a proof-of-concept study of the detection of two synthetic models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using polarimetric imaging.

Approach

Two SARS-CoV-2 models were prepared as engineered lentiviruses pseudotyped with the G protein of the vesicular stomatitis virus, and with the characteristic Spike protein of SARS-CoV-2. Samples were prepared in two biofluids (saline solution and artificial saliva), in four concentrations, and deposited as 5-µL droplets on a supporting plate. The angles of maximal degree of linear polarization (DLP) of light diffusely scattered from dry residues were determined using Mueller polarimetry from87 samples at 405 nm and 514 nm. A polarimetric camera was used for imaging several samples under 380-420 nm illumination at angles similar to those of maximal DLP. Per-pixel image analysis included quantification and combination of polarization feature descriptors in 475 samples.

Main results

The angles (from sample surface) of maximal DLP were 3° for 405 nm and 6° for 514 nm. Similar viral particles that differed only in the characteristic spike protein of the SARS-CoV-2, their corresponding negative controls, fluids, and the sample holder were discerned at 10-degree and 15-degree configurations.

Significance

Polarimetric imaging in the visible spectrum may help improve fast, non-contact detection and identification of viral particles, and/or other microbes such as tuberculosis, in multiple dry fluid samples simultaneously, particularly when combined with other imaging modalities. Further analysis including realistic concentrations of real SARS-CoV-2 viral particles in relevant human fluids is required. Polarimetric imaging under visible light may contribute to a fast, cost-effective screening of SARS-CoV-2 and other pathogens when combined with other imaging modalities.

Evaluating vaccine allocation strategies using simulation-assisted causal modeling

We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the coronavirus disease 2019 (COVID-19) pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modeling approach that combines a compartmental infection-dynamics simulation, a coarse-grained causal model, and literature estimates for immunity waning. We compare Israel's strategy, implemented in 2021, with counterfactual strategies such as no prioritization, prioritization of younger age groups, or a strict risk-ranked approach; we find that Israel's implemented strategy was indeed highly effective. We also study the impact of increasing vaccine uptake for given age groups. Because of its modular structure, our model can easily be adapted to study future pandemics. We demonstrate this by simulating a pandemic with characteristics of the Spanish flu. Our approach helps evaluate vaccination strategies under the complex interplay of core epidemic factors, including age-dependent risk profiles, immunity waning, vaccine availability, and spreading rates.

COVID-19 Government restriction policy, COVID-19 vaccination and stock markets: Evidence from a global perspective

We use the COVID-19 stringency index to investigate the relationship among COVID-19 government restriction policy, COVID-19 vaccination and stock markets. We find that the impact of the change rate of COVID-19 stringency index on stock returns turns from significant in the pre-vaccination period to insignificant in the post-vaccination period. Bad news from COVID-19 restriction policy cause more stock volatilities than good news. The advent of COVID-19 vaccination weakens the linkage of COVID-19 stringency index and stock market, while COVID-19 stringency index only plays a partially mediate role in the correlation between COVID-19 cumulative vaccination rate and stock market performance.

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.

Moral, self-interested, and social motivation each predict compliance with social distancing rules: utilitarianism is an indirect positive predictor

BACKGROUND

Social distancing rules have proven to be essential in reducing the spread of COVID-19. However, we can optimise these rules if we identify factors which predict compliance. Thus, in this study we investigated whether compliance with distancing rules is predicted by whether an individual is motivated by moral, self-interested, or social reasons. We also investigated the impact of an individual's utilitarian orientation both on compliance itself and on reasons for compliance.

METHODS

Our sample consisted of 301 participants recruited from four US states - California, Oregon, Mississippi, and Alabama - who completed an anonymous online survey. Six vignettes describing hypothetical social distancing rules were developed for the study. Participants indicated (i) how likely they were to violate each hypothetical distancing rule, (ii) how morally wrong violating each rule would be, (iii) how much risk of contracting COVID-19 they would tolerate in order to violate each rule, and (iv) how much social condemnation they would tolerate in order to violate each rule. Based on these responses, we gauged each participant's overall degree of compliance with social distancing rules as well as the extent to which each participant's compliance is motivated by moral, self-interested, and social reasons. We also measured other variables that could affect compliance including personality, level of religiosity, and inclination to engage in utilitarian reasoning. Multiple regression and exploratory structural equation modelling were used to determine predictors of compliance with social distancing rules.

RESULTS

We found that moral, self-interested, and social motivation each positively predicted compliance, with self-interested motivation being the strongest predictor. Furthermore, utilitarian orientation indirectly predicted compliance, with moral, self-interested, and social motivation as positive mediating factors. No controlled covariates (personality factors, religiosity, political orientation, or other background variables) predicted compliance.

CONCLUSION

These findings have implications not only for the design of social distancing rules but also for efforts to ensure vaccine uptake. Governments need to consider how to harness moral, self-interested, and social motivation to promote compliance, perhaps by co-opting utilitarian reasoning, which positively influences these motivational forces.

Variation in Global Policy Responses to COVID-19: A Bidirectional Analysis

Against the unprecedented outbreaks of the COVID-19 variants, countries have introduced restrictive measures with discretion, ranging from lifting the closure thoroughly to implementing stringent policies, but all together guarding the global public health. Under the changing circumstances, we firstly apply the panel data vector autoregression (PVAR) model, using a sample of 176 countries/territories from 15 June 2021 to 15 April 2022, to estimate the potential associations among the policy responses, the progression of COVID-19 in deaths and vaccination, and medical resources possessed. Furthermore, we use the random effect method and the fixed effect speculation, to examine the determinants of policy variances across regions and over time. Our work has four main findings. Firstly, it showed the existence of a bidirectional relationship between the policy stringency and variables of interest including new daily deaths, the fully vaccinated percentage and health capacity. Secondly, conditional on the availability of vaccines, the sensitivity of policy responses to the death numbers tends to decline. Thirdly, the role of health capacity matters in coexisting with the virus mutation. Fourthly, regarding the variance in policy responses over time, the impact of new deaths tends to be seasonal. As to geographical differences in policy responses, we present the analysis for Asia, Europe, and Africa, and they show different levels of dependencies on the determinants. These findings suggest that bidirectional correlations exist in the complex context of wrestling with the COVID-19, as government interventions exert influence on the virus spread, the policy responses also progress alongside multiple factors evolving in the pandemic. This study will help policymakers, practitioners, and academia to formulate a comprehensive understanding of the interactions between policy responses and the contextualized implementation factors.

Association between face covering policies and the incidence of coronavirus disease 2019 in European countries

OBJECTIVES

This study was conducted to determine the impact of the strengthening or relaxation of face covering mandates on the subsequent national case incidence of coronavirus disease 2019 (COVID-19) in Europe as the full vaccination rate was increasing.

METHODS

European countries in which case incidence increased for 3 consecutive weeks were monitored and analyzed using COVID-19 incidence data shared by the World Health Organization (WHO). The epidemic trend of COVID-19 in Europe was compared with that of countries elsewhere in the world based on WHO weekly epidemiological reports from June 20 to October 30, 2021. In addition, this study provided insight into the impact of government mask mandates on COVID-19 incidence in Europe by measuring the index scores of those facial covering policies before and after mandate relaxation or strengthening. The effects of the vaccination rate and the speed of vaccination on COVID-19 incidence were also analyzed.

RESULTS

The incidence of COVID-19 after the relaxation of face covering mandates was significantly higher than before relaxation. However, no significant difference was observed in vaccination rate between countries with increased and decreased incidence. Instead, rapid vaccination delayed the resurgence in incidence.

CONCLUSIONS

The findings suggest that face covering policies in conjunction with rapid vaccination efforts are essential to help mitigate the spread of COVID-19.

Stabilization through self-coupling in networks of small-world and scale-free topology

Mechanisms that ensure the stability of dynamical systems are of vital importance, in particular in our globalized and increasingly interconnected world. The so-called connectivity-stability dilemma denotes the theoretical finding that increased connectivity between the components of a large dynamical system drastically reduces its stability. This result has promoted controversies within ecology and other fields of biology, especially, because organisms as well as ecosystems constitute systems that are both highly connected and stable. Hence, it has been a major challenge to find ways to stabilize complex systems while preserving high connectivity at the same time. Investigating the stability of networks that exhibit small-world or scale-free topology is of particular interest, since these topologies have been found in many different types of real-world networks. Here, we use an approach to stabilize recurrent networks of small-world and scale-free topology by increasing the average self-coupling strength of the units of a network. For both topologies, we find that there is a sharp transition from instability to asymptotic stability. Then, most importantly, we find that the average self-coupling strength needed to stabilize a system increases much slower than its size. It appears that the qualitative shape of this relationship is the same for small-world and scale-free networks, while scale-free networks can require higher magnitudes of self-coupling. We further explore the stabilization of networks with Kronecker-Leskovec topology. Finally, we argue that our findings, in particular the stabilization of large recurrent networks through small increases in the unit self-regulation, are of practical importance for the stabilization of diverse types of complex systems.

Progress Update on the Epidemiology of COVID-19 Variants and the Assessment Status of Developed Vaccines

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread rapidly and diffused to more than 180 countries at varying severities. This pandemic has accounted for increased mortality and morbidity in developed as well as developing nations. The WHO has announced that there is a persistent need for the evaluation of the COVID-19 vaccine effectiveness (VE) against major outcomes, which include severe diseases, symptomatic COVID-19, and mortalities related to COVID-19. Therefore, mass vaccination programs using vaccines of high effectiveness are among the strategies that have been used by governments worldwide to impede the COVID-19 pandemic transmission. In this regard, massive efforts were made by governments, scientists, biomedical researchers, and healthcare professionals leading to the successful development of various vaccines to bring this pandemic under control. This editorial aims to shed light on the epidemiological status of COVID-19 variants, namely, Delta, Omicron, and Deltacron variants as well as discuss the effectiveness of the currently available COVID-19 vaccines.

A topic trend analysis on COVID-19 literature

Objective

In the past 2 years, the number of scientific publications has grown exponentially. The COVID-19 outbreak hugely contributed to this dramatic increase in the volume of published research. Currently, text mining of the volume of SARS-CoV-2 and COVID-19 publications is limited to the first months of the outbreak. We aim to identify the major topics in COVID-19 literature collected from several citational sources and analyze the temporal trend from November 2019 to December 2021.

Methods

We performed an extensive literature search on SARS-Cov-2 and COVID-19 publications on PubMed, Scopus, and Web of Science (WoS) and a structural topic modelling on the retrieved abstracts. The temporal trend of the recognized topics was analyzed. Furthermore, a comparison between our corpus and the COVID-19 Open Research Dataset (CORD-19) repository was performed.

Results

We collected 269,186 publications and identified 10 topics. The most popular topic was related to the clinical pictures of the COVID-19 outbreak, which has a constant trend, and the least popular includes studies on COVID-19 literature and databases. "Telemedicine", "Vaccine development", and "Epidemiology" were popular topics in the early phase of the pandemic; increasing topics in the last period are "COVID-19 impact on mental health", "Forecasting", and "Molecular Biology". "Education" was the second most popular topic, which emerged in September 2020.

Conclusions

We identified 10 topics for classifying COVID-19 research publications and estimated a nonlinear temporal trend that gives an overview of their unfolding over time. Several citational databases must be searched to retrieve a complete set of studies despite the efforts to build repositories for COVID-19 literature. Our collected data can help build a more focused literature search between November 2019 and December 2021 when carrying out systematic and rapid reviews and our findings can give a complete picture on the topic.

Insights into Controlling the Spread of COVID-19: A Study Inspired by Seven of the Earliest Vaccinated Countries

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 5^th 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.

The heterogeneous mixing model of COVID-19 with interventions

The emergence of mutant strains of COVID-19 reduces the effectiveness of vaccines in preventing infection, but remains effective in preventing severe illness and death. This paper established a heterogeneous mixing model of age groups with pharmaceutical and non-pharmaceutical interventions by analyzing the transmission mechanism of breakthrough infection caused by the heterogeneity of protection period under the action of vaccine-preventable infection with the original strain. The control reproduction number Rc of the system is analyzed, and the existence and stability of equilibrium are given by the comparison principle. Numerical simulation was conducted to evaluate the vaccination program and intervention measures in the customized scenario, demonstrating that the group-3 coverage rate p3 plays a key role in Rc. It is proposed that accelerating the rate of admission and testing is conducive to epidemic control by further fitting data of COVID-19 transmission in real scenarios. The findings provide a general modeling idea for the emergence of new vaccines to prevent infection by mutant strains, as well as a solid theoretical foundation for mainland China to formulate future vaccination strategies for new vaccines. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Stamping the vaccine passport? Public support for lifting COVID-19 related restrictions for vaccinated citizens in France, Germany, and Sweden

During theCOVID-19pandemic,manycountries implementedrestrictionsto limit the spread of the SARS-CoV-2 virus (e.g. travel restrictions and lockdowns).One path to loosening restrictions is to do so selectively only for vaccinated individuals (e.g. by implementing vaccine passports domestically or as a prerequisite for international travel).Setting different rules based on people's vaccination statusis howevera contentious issue among health policy experts, government officials, and the public. Our analysis focuses on the levels and correlates of public support for the lifting of restrictions for the vaccinatedin April 2021, i.e. at a time when restrictions were in place and aselective lifting of these restrictions just for the vaccinatedwas debated in Europe.We use representative quota samples of the populations of France (N = 1,752), Germany (N = 1,759), and Sweden (N = 1,754). We find that a slight plurality support lifting restrictions for the vaccinated in France and Germany but not in Sweden. Vaccine hesitancy emerges as strong predictor of opposition to such a policy. Additionally, individuals who are already vaccinated (in France and Germany) and who are higher in risk-seeking express more support for the lifting of restrictions for the vaccinated. We discuss implications for the debate on vaccine passports.

Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.

Non-pharmaceutical interventions (NPIs) and COVID-19 vaccination have been implemented concurrently, making their relative effects difficult to measure. Here, the authors show that effects of NPIs reduced as vaccine coverage increased, but that NPIs could still be important in the context of more transmissible variants.

A Case for Cross-Border Governance? A Comparative Trend Assessment of COVID-19 Transmission, Vaccination, and Outcomes Among 35 Nations in Europe Across 18 months

OBJECTIVE

Coronavirus disease 2019 (COVID-19) spread globally, including across Europe, resulting in different morbidity and mortality outcomes. The aim of this study was to explore the progression of the COVID-19 pandemic over 18 mo in relation to the effect of COVID-19 vaccination at a population level across 35 nations in Europe, while evaluating the data for cross-border epidemiological trends to identify any pertinent lessons that can be implemented in the future.

METHODS

Epidemiological data were obtained from European Centre for Disease Prevention and Control and Our World in Data databases while Ministry of Health websites of each respective country and local newspapers were used for COVID-19-related vaccination strategies. Case, mortality, and vaccination incidence comparative analyses were made across neighboring countries.

RESULTS

Similar morbidity and mortality outcomes were evident across neighboring countries over 18 mo, with a bidirectional relationship evident between cumulative fully vaccinated population and case fatality rates.

CONCLUSION

Countries' COVID-19 outcome is related on national mitigative measures, vaccination rollouts, and neighboring countries' actions and COVID-19 situations. Mass population vaccination appeared to be effective in reducing COVID-19 case severity and mortality rates. Vaccination equity and pan-European commitment for cross-border governance appear to be the way forward to ensure populations' return to "normality."

Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant

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.

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

BACKGROUND

Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests.

METHODS

We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario.

RESULTS

We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics.

CONCLUSIONS

Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.

Survey of awareness, attitudes, and compliance with COVID-19 measures among Vermont residents

The objective of this research was to examine residents' awareness, attitudes, and compliance with COVID-19 public health guidelines in Vermont, which emerged as an early leader in national pandemic response. Our methods included conducting an online survey of adult Vermont residents between January and April 2021. We analyzed demographics associated with awareness and compliance, and identified features associated with non-compliance. Our results show that of the 2,208 adult Vermont residents who completed the survey, 90% were extremely aware of the state's COVID-19 guidelines, and 95% reported knowing exactly what to do to follow recommended actions. Political affiliation emerged as a primary factor related to attitudes and compliance. Self-identified Republicans were less likely to agree that public health measures keep people safe or help businesses stay open, and were less likely to follow masking, quarantine, social distancing, and vaccine guidance than Independents, Progressives, and Democrats. The large differences in COVID-19 infection and death rates across the country, and recent shift toward a "pandemic of the unvaccinated," underscore the need for identifying public health strategies that work in some areas in order to adapt and apply them to areas that have struggled with controlling the virus. Consistent with national surveys, our results show that resistance to public health guidance is a partisan challenge even in states with high compliance. Identifying populations that are less supportive or hesitant to follow guidelines while understanding factors that motivate compliance can help inform strategies for developing targeted programs to encourage collective action on pandemic response. Developing communication strategies that reach people who do not believe COVID-19 guidelines keep them safe is necessary to reach universal compliance.

COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit

BACKGROUND

Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline.

METHODS

We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits.

RESULTS

There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal).

CONCLUSIONS

Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.

Risk assessment of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes

The introduction of COVID-19 vaccination passes (VPs) by many countries coincided with the Delta variant fast becoming dominant across Europe. A thorough assessment of their impact on epidemic dynamics is still lacking. Here, we propose the VAP-SIRS model that considers possibly lower restrictions for the VP holders than for the rest of the population, imperfect vaccination effectiveness against infection, rates of (re-)vaccination and waning immunity, fraction of never-vaccinated, and the increased transmissibility of the Delta variant. Some predicted epidemic scenarios for realistic parameter values yield new COVID-19 infection waves within two years, and high daily case numbers in the endemic state, even without introducing VPs and granting more freedom to their holders. Still, suitable adaptive policies can avoid unfavorable outcomes. While VP holders could initially be allowed more freedom, the lack of full vaccine effectiveness and increased transmissibility will require accelerated (re-)vaccination, wide-spread immunity surveillance, and/or minimal long-term common restrictions.

Assessing the impact of vaccines, other public health measures, and declining immunity on SARS-CoV-2 control is challenging. This is particularly true in the context of vaccination passes, whereby vaccinated individuals have more freedom of making contacts than unvaccinated ones. Here, we use a mathematical model to simulate various scenarios and investigate the likelihood of containing COVID-19 outbreaks in example European countries. We demonstrate that both Alpha and Delta SARS-CoV-2 variants inevitably lead to recurring outbreaks when measures are lifted for vaccination pass holders. High re-vaccination rates and a lowered fraction of the unvaccinated population increase the benefit of vaccination passes. These observations are important for policy making, highlighting the need for continued vigilance, even where the epidemic is under control, especially when new variants of concern emerge.

Krueger, Gogolewski, and Bodych et al. assess the risk of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes. Their model predicts that new COVID-19 infection waves within two years from the onset of the vaccination program are possible but that suitable adaptive policies can help to avoid unfavorable outcomes.

The benefits, costs and feasibility of a low incidence COVID-19 strategy

In the summer of 2021, European governments removed most NPIs after experiencing prolonged second and third waves of the COVID-19 pandemic. Most countries failed to achieve immunization rates high enough to avoid resurgence of the virus. Public health strategies for autumn and winter 2021 have ranged from countries aiming at low incidence by re-introducing NPIs to accepting high incidence levels. However, such high incidence strategies almost certainly lead to the very consequences that they seek to avoid: restrictions that harm people and economies. At high incidence, the important pandemic containment measure 'test-trace-isolate-support' becomes inefficient. At that point, the spread of SARS-CoV-2 and its numerous harmful consequences can likely only be controlled through restrictions. We argue that all European countries need to pursue a low incidence strategy in a coordinated manner. Such an endeavour can only be successful if it is built on open communication and trust.

When to end a lock down? How fast must vaccination campaigns proceed in order to keep health costs in check?

We propose a simple rule of thumb for countries which have embarked on a vaccination campaign while still facing the need to keep non-pharmaceutical interventions (NPI) in place because of the ongoing spread of SARS-CoV-2. If the aim is to keep the death rate from increasing, NPIs can be loosened when it is possible to vaccinate more than twice the growth rate of new cases. If the aim is to keep the pressure on hospitals under control, the vaccination rate has to be about four times higher. These simple rules can be derived from the observation that the risk of death or a severe course requiring hospitalization from a COVID-19 infection increases exponentially with age and that the sizes of age cohorts decrease linearly at the top of the population pyramid. Protecting the over 60-year-olds, which constitute approximately one-quarter of the population in Europe (and most OECD countries), reduces the potential loss of life by 95 percent.

What Happens to the Immune System after Vaccination or Recovery from COVID-19?

Due to its leading role in fighting infections, the human immune system has been the focus of many studies in the context of Coronavirus disease 2019 (COVID-19). In a worldwide effort, the scientific community has transitioned from reporting about the effects of the novel coronavirus on the human body in the early days of the pandemic to exploring the body's many immunopathological and immunoprotecting properties that have improved disease treatment and enabled the development of vaccines. The aim of this review is to explain what happens to the immune system after recovery from COVID-19 and/or vaccination against SARS-CoV-2, the virus that causes the disease. We detail the way in which the immune system responds to a SARS-CoV-2 infection, including innate and adaptive measures. Then, we describe the role of vaccination, the main types of COVID-19 vaccines and how they protect us. Further, we explain the reason why immunity after COVID-19 infection plus a vaccination appears to induce a stronger response compared with virus exposure alone. Additionally, this review reports some correlates of protection from SARS-CoV-2 infection. In conclusion, we reinforce that vaccination is safe and important in achieving herd immunity.

Low case numbers enable long-term stable pandemic control without lockdowns

The traditional long-term solutions for epidemic control involve eradication or population immunity. Here, we analytically derive the existence of a third viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events and only moderate restrictions remain necessary. In this equilibrium, daily cases stabilize around ten or fewer new infections per million people. However, stability is endangered if restrictions are relaxed or case numbers grow too high. The latter destabilization marks a tipping point beyond which the spread self-accelerates. We show that a lockdown can reestablish control and that recurring lockdowns are not necessary given sustained, moderate contact reduction. We illustrate how this strategy profits from vaccination and helps mitigate variants of concern. This strategy reduces cumulative cases (and fatalities) four times more than strategies that only avoid hospital collapse. In the long term, immunization, large-scale testing, and international coordination will further facilitate control.

A look into the future of the COVID-19 pandemic in Europe: an expert consultation

How will the coronavirus disease 2019 (COVID-19) pandemic develop in the coming months and years? Based on an expert survey, we examine key aspects that are likely to influence the COVID-19 pandemic in Europe. The challenges and developments will strongly depend on the progress of national and global vaccination programs, the emergence and spread of variants of concern (VOCs), and public responses to non-pharmaceutical interventions (NPIs). In the short term, many people remain unvaccinated, VOCs continue to emerge and spread, and mobility and population mixing are expected to increase. Therefore, lifting restrictions too much and too early risk another damaging wave. This challenge remains despite the reduced opportunities for transmission given vaccination progress and reduced indoor mixing in summer 2021. In autumn 2021, increased indoor activity might accelerate the spread again, whilst a necessary reintroduction of NPIs might be too slow. The incidence may strongly rise again, possibly filling intensive care units, if vaccination levels are not high enough. A moderate, adaptive level of NPIs will thus remain necessary. These epidemiological aspects combined with economic, social, and health-related consequences provide a more holistic perspective on the future of the COVID-19 pandemic.

On the heterogeneous spread of COVID-19 in Chile

Non-pharmaceutical interventions (NPIs) have played a crucial role in controlling the spread of COVID-19. Nevertheless, NPI efficacy varies enormously between and within countries, mainly because of population and behavioral heterogeneity. In this work, we adapted a multi-group SEIRA model to study the spreading dynamics of COVID-19 in Chile, representing geographically separated regions of the country by different groups. We use national mobilization statistics to estimate the connectivity between regions and data from governmental repositories to obtain COVID-19 spreading and death rates in each region. We then assessed the effectiveness of different NPIs by studying the temporal evolution of the reproduction number R t . Analysing data-driven and model-based estimates of R t , we found a strong coupling of different regions, highlighting the necessity of organized and coordinated actions to control the spread of SARS-CoV-2. Finally, we evaluated different scenarios to forecast the evolution of COVID-19 in the most densely populated regions, finding that the early lifting of restriction probably will lead to novel outbreaks.