Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

Competition of SARS-CoV-2 variants on the pandemic transmission dynamics

SARS-CoV-2 has produced various variants during its ongoing evolution. The competitive behavior driven by the co-transmission of these variants has influenced the pandemic transmission dynamics. Therefore, studying the impact of competition between SARS-CoV-2 variants on pandemic transmission dynamics is of considerable practical importance. In order to formalize the mechanism of competition between SARS-CoV-2 variants, we propose an epidemic model that takes into account the co-transmission of competing variants. The model focuses on how cross-immunity influences the transmission dynamics of SARS-CoV-2 through competitive mechanisms between strains. We found that inter-strain competition affects not only both the final size and the replacement time of the variants, but also the invasive behavior of new variants in the future. Due to the limited extent of cross-immunity in previous populations, we predict that the new strain may infect the largest number of individuals in China without control interventions. Moreover, we also observed the possibility of periodic outbreaks in the same lineage and the possibility of the resurgence of previous lineages. Without the invasion of a new variant, the previous variant (Delta variant) is projected to resurgence as early as 2023. However, its resurgence may be prevented by a new variant with a greater competitive advantage.

Intermediate levels of asymptomatic transmission can lead to the highest epidemic fatalities

Asymptomatic infections have hampered the ability to characterize and prevent the transmission of SARS-CoV-2 throughout the pandemic. Although asymptomatic infections reduce severity at the individual level, they can make population-level outcomes worse if asymptomatic individuals-unaware they are infected-transmit more than symptomatic individuals. Using an epidemic model, we show that intermediate levels of asymptomatic infection lead to the highest levels of epidemic fatalities when the decrease in symptomatic transmission, due either to individual behavior or mitigation efforts, is strong. We generalize this result to include presymptomatic transmission, showing that intermediate levels of nonsymptomatic transmission lead to the highest levels of fatalities. Finally, we extend our framework to illustrate how the intersection of asymptomatic spread and immunity profiles determine epidemic trajectories, including population-level severity, of future variants. In particular, when immunity provides protection against symptoms, but not against infections or deaths, epidemic trajectories can have faster growth rates and higher peaks, leading to more total deaths. Conversely, even modest levels of protection against infection can mitigate the population-level effects of asymptomatic spread.

Improving the patient decision-making experience for cataract surgery during the COVID-19 era

OBJECTIVE

To explore whether video-based patient decision aids (VBPDAs) for cataract surgery consultation can enhance a patient's decision-making process while upholding safety regulations during the coronavirus disease 2019 (COVID-19) pandemic.

DESIGN

Single-centre consecutive case study.

PARTICIPANTS

147 patients, with an average age of 70 years, who came in for a cataract surgery consult were enrolled in this study.

METHODS

All patients watched part 1 of the VBPDA outlining the process of cataract surgery and the decisions involved. Patients then underwent cataract surgery consultation with an ophthalmologist. Afterward, if the patient was indicated for surgery, part 2 of the VBPDA was played. At the end of the visit, all patients completed a survey assessing the effects of COVID-19 safety precautions on their appointment. In addition, patients who had gone forward with surgery complete the Decisional Conflict Scale (DCS).

RESULTS

For patients proceeding with cataract surgery, the median DCS score was 9.38 (range, 0-54.69, min-max) on a scale from 0 to 100 (low-high decisional conflict). A DCS score <25 indicates low decisional conflict (n = 76, 68.47%) and a score >25 indicates feeling unsure (n = 35, 31.53%). The DCS also can be separated into various subscales: the informed subscale (median = 8.33; min-max = 0-66.67), values subscale (16.67, 0-58.33), support subscale (8.33, 0-50.00), uncertainty subscale (8.33, 0-83.33), and effective decision subscale (0, 0-37.50).

CONCLUSION

Our study found VBPDAs to be an effective tool to enhance the patient decision-making process for cataract surgery during the COVID-19 era.

Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan

BACKGROUND

The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) and the Omicron variant presents a formidable challenge for control and prevention worldwide, especially for low- and middle-income countries (LMICs). Hence, taking Kazakhstan and Pakistan as examples, this study aims to explore COVID-19 transmission with the Omicron variant at different contact, quarantine and test rates.

METHODS

A disease dynamic model was applied, the population was segmented, and three time stages for Omicron transmission were established: the initial outbreak, a period of stabilization, and a second outbreak. The impact of population contact, quarantine and testing on the disease are analyzed in five scenarios to analysis their impacts on the disease. Four statistical metrics are employed to quantify the model's performance, including the correlation coefficient (CC), normalized absolute error, normalized root mean square error and distance between indices of simulation and observation (DISO).

RESULTS

Our model has high performance in simulating COVID-19 transmission in Kazakhstan and Pakistan with high CC values greater than 0.9 and DISO values less than 0.5. Compared with the present measures (baseline), decreasing (increasing) the contact rates or increasing (decreasing) the quarantined rates can reduce (increase) the peak values of daily new cases and forward (delay) the peak value times (decreasing 842 and forward 2 days for Kazakhstan). The impact of the test rates on the disease are weak. When the start time of stage II is 6 days, the daily new cases are more than 8 and 5 times the rate for Kazakhstan and Pakistan, respectively (29,573 vs. 3259; 7398 vs. 1108). The impact of the start times of stage III on the disease are contradictory to those of stage II.

CONCLUSIONS

For the two LMICs, Kazakhstan and Pakistan, stronger control and prevention measures can be more effective in combating COVID-19. Therefore, to reduce Omicron transmission, strict management of population movement should be employed. Moreover, the timely application of these strategies also plays a key role in disease control.

Seasonal effects decouple SARS-CoV-2 haplotypes worldwide

Background: Variants of concern (VOCs) have been replacing each other during the still rampant COVID-19 pandemic. As a result, SARS-CoV-2 populations have evolved increasingly intricate constellations of mutations that often enhance transmissibility, disease severity, and other epidemiological characteristics. The origin and evolution of these constellations remain puzzling. Methods: Here we study the evolution of VOCs at the proteome level by analyzing about 12 million genomic sequences retrieved from GISAID on July 23, 2022. A total 183,276 mutations were identified and filtered with a relevancy heuristic. The prevalence of haplotypes and free-standing mutations was then tracked monthly in various latitude corridors of the world. Results: A chronology of 22 haplotypes defined three phases driven by protein flexibility-rigidity, environmental sensing, and immune escape. A network of haplotypes illustrated the recruitment and coalescence of mutations into major VOC constellations and seasonal effects of decoupling and loss. Protein interaction networks mediated by haplotypes predicted communications impacting the structure and function of proteins, showing the increasingly central role of molecular interactions involving the spike (S), nucleocapsid (N), and membrane (M) proteins. Haplotype markers either affected fusogenic regions while spreading along the sequence of the S-protein or clustered around binding domains. Modeling of protein structure with AlphaFold2 showed that VOC Omicron and one of its haplotypes were major contributors to the distortion of the M-protein endodomain, which behaves as a receptor of other structural proteins during virion assembly. Remarkably, VOC constellations acted cooperatively to balance the more extreme effects of individual haplotypes. Conclusions: Our study uncovers seasonal patterns of emergence and diversification occurring amid a highly dynamic evolutionary landscape of bursts and waves. The mapping of genetically-linked mutations to structures that sense environmental change with powerful ab initio modeling tools demonstrates the potential of deep-learning for COVID-19 predictive intelligence and therapeutic intervention.

Mathematical modeling and investigation on the role of demography and contact patterns in social distancing measures effectiveness in COVID-19 dissemination

In this article, we investigate the importance of demography and contact patterns in determining the spread of COVID-19 and to the effectiveness of social distancing policies. We investigate these questions proposing an augmented epidemiological model with an age-structured model, with the population divided into susceptible (S), exposed (E), asymptomatic infectious (A), hospitalized (H), symptomatic infectious (I) and recovered individuals (R), to simulate COVID-19 dissemination. The simulations were carried out using six combinations of four types of isolation policies (work restrictions, isolation of the elderly, community distancing and school closures) and four representative fictitious countries generated over alternative demographic transition stage patterns (aged developed, developed, developing and least developed countries). We concluded that the basic reproduction number depends on the age profile and the contact patterns. The aged developed country had the lowest basic reproduction number ($R0=1.74$) due to the low contact rate among individuals, followed by the least developed country ($R0=2.00$), the developing country ($R0=2.43$) and the developed country ($R0=2.64$). Because of these differences in the basic reproduction numbers, the same intervention policies had higher efficiencies in the aged and least developed countries. Of all intervention policies, the reduction in work contacts and community distancing were the ones that produced the highest decrease in the $R0$ value, prevalence, maximum hospitalization demand and fatality rate. The isolation of the elderly was more effective in the developed and aged developed countries. The school closure was the less effective intervention policy, though its effects were not negligible in the least developed and developing countries.

Government interventions and control policies to contain the first COVID-19 outbreak: An analysis of evidence

BACKGROUND

The overarching aim of this study was to evaluate the effectiveness over time of government interventions and policy restrictions and the impact of determinants on spread and mortality during the first-wave of the COVID-19 pandemic, globally, regionally and by country-income level, up to 18 May 2020.

METHODS

We created a global database merging World Health Organization daily case reports (from 218 countries/territories) with other socio-demographic and population health measures from 21 January to 18 May 2020. A four-level government policy interventions score (low to very high) was created based on the Oxford Stringency Index.

RESULTS

Our results support the use of very high government interventions to suppress both COVID-19 spread and mortality effectively during wave one globally compared to other policy levels of control. Similar trends in virus propagation and mortality were observed in all country-income levels and specific regions.

CONCLUSIONS

Rapid implementation of government interventions was needed to contain the first wave of the COVID-19 outbreak and to reduce COVID-19-related mortality.

Experiences and support needs of parents/caregivers of children with cancer through the COVID-19 pandemic in the UK: a longitudinal study

OBJECTIVE

To explore the experiences, information and support needs of parents/caregivers of children with cancer and how these changed as the COVID-19 pandemic evolved.

DESIGN

Online surveys containing closed and free-text questions on experiences, information and support needs were completed at four time points (between April 2020 and October 2021) during the COVID-19 pandemic. Descriptive statistics of closed items and content analysis of qualitative data were conducted.

SETTING

Online.

PARTICIPANTS

Parents/caregivers of children with cancer.

RESULTS

335 parents/caregivers completed the survey over four time points. Findings revealed that parents'/caregivers' worry about the virus and vigilance about their child's virus symptoms decreased over time. Parents reporting the need for support on how to reduce their worries and/or family members during the virus outbreak were low, however parents reported a slight increase in need for support at T3 when schools reopened. Qualitative findings reported the following themes: (1) Psychological well-being of parents/caregivers, (2) Changing perceptions of risks/priorities, (3) Adjusting to COVID-19: Living with continued caution, (4) Healthcare and treatment provision, (5) Information seeking and needs during COVID-19.

CONCLUSIONS

The COVID-19 pandemic disrupted people's lives and routines in relation to access to support, finances, education and social lives, leading to psychological distress. Parents highlighted the need for timely, up-to-date and personalised information in relation to COVID-19 and their child with cancer. Further consideration of the development of technology-based health solutions may provide an efficient and safe way to connect with and support parent/caregivers.

Quantifying the effect of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study

BACKGROUND

The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3 weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the SARS-CoV-2 alpha variant prompted the UK to extend the interval between doses to 12 weeks. In this study, we aimed to quantify the effect of delaying the second vaccine dose in England.

METHODS

We used a previously described model of SARS-CoV-2 transmission, calibrated to COVID-19 surveillance data from England, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data, using a Bayesian evidence-synthesis framework. We modelled and compared the epidemic trajectory in the counterfactual scenario in which vaccine doses were administered 3 weeks apart against the real reported vaccine roll-out schedule of 12 weeks. We estimated and compared the resulting numbers of daily infections, hospital admissions, and deaths. In sensitivity analyses, we investigated scenarios spanning a range of vaccine effectiveness and waning assumptions.

FINDINGS

In the period from Dec 8, 2020, to Sept 13, 2021, the number of individuals who received a first vaccine dose was higher under the 12-week strategy than the 3-week strategy. For this period, we estimated that delaying the interval between the first and second COVID-19 vaccine doses from 3 to 12 weeks averted a median (calculated as the median of the posterior sample) of 58 000 COVID-19 hospital admissions (291 000 cumulative hospitalisations [95% credible interval 275 000-319 000] under the 3-week strategy vs 233 000 [229 000-238 000] under the 12-week strategy) and 10 100 deaths (64 800 deaths [60 200-68 900] vs 54 700 [52 800-55 600]). Similarly, we estimated that the 3-week strategy would have resulted in more infections compared with the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. In results by age group, the 12-week strategy led to more hospitalisations and deaths in older people in spring 2021, but fewer following the emergence of the delta variant during summer 2021.

INTERPRETATION

England's delayed-second-dose vaccination strategy was informed by early real-world data on vaccine effectiveness in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial (single-dose) vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths overall.

FUNDING

UK National Institute for Health Research; UK Medical Research Council; Community Jameel; Wellcome Trust; UK Foreign, Commonwealth and Development Office; Australian National Health and Medical Research Council; and EU.

Recent advances in respiratory immunization: A focus on COVID-19 vaccines

The development of vaccines has always been an essential task worldwide since vaccines are regarded as powerful weapons in protecting the global population. Although the vast majority of currently authorized human vaccinations are administered intramuscularly or subcutaneously, exploring novel routes of immunization has been a prominent area of study in recent years. This is particularly relevant in the face of pandemic diseases, such as COVID-19, where respiratory immunization offers distinct advantages, such as inducing systemic and mucosal responses to prevent viral infections in both the upper and lower respiratory tracts and also leading to higher patient compliance. However, the development of respiratory vaccines confronts challenges due to the physiological barriers of the respiratory tract, with most of these vaccines still in the research and development stage. In this review, we detail the structure of the respiratory tract and the mechanisms of mucosal immunity, as well as the obstacles to respiratory vaccination. We also examine the considerations necessary in constructing a COVID-19 respiratory vaccine, including the dosage form of the vaccines, potential excipients and mucosal adjuvants, and delivery systems and devices for respiratory vaccines. Finally, we present a comprehensive overview of the COVID-19 respiratory vaccines currently under clinical investigation. We hope this review can provide valuable insights and inspiration for the future development of respiratory vaccinations.

Adherence to social distancing during the Covid-19 pandemic in Italy: The role of autonomous motivation and defiance

To maintain social distancing in the long term, in the current COVID-19 scenario, people's motivation must be strong and of high quality. Many governments adopted measures enforcing social distancing. Enforcement, however, can produce feelings of defiance and backfiring effects. The present work aims at investigating the relationship between autonomous motivation and intentions to maintain social distancing, through adherence to recommendations and feelings of defiance. A sample of 502 Italian residents, from different parts of Italy, completed an online survey assessing their present behavior, levels of autonomous motivation and feelings of defiance, as well as intentions to observe social distancing in the short and long term. Results support the hypotheses that autonomous motivation is related to stronger intentions to maintain social distancing, particularly in the long term, and that feelings of defiance mediate this relationship. These results underline importance of promoting understanding and internalizing reasons for social distancing, beyond norms.

SARS-CoV-2 elicits non-sterilizing immunity and evades vaccine-induced immunity: implications for future vaccination strategies

Neither vaccination nor natural infection result in long-lasting protection against SARS-COV-2 infection and transmission, but both reduce the risk of severe COVID-19. To generate insights into optimal vaccination strategies for prevention of severe COVID-19 in the population, we extended a Susceptible-Exposed-Infectious-Removed (SEIR) mathematical model to compare the impact of vaccines that are highly protective against severe COVID-19 but not against infection and transmission, with those that block SARS-CoV-2 infection. Our analysis shows that vaccination strategies focusing on the prevention of severe COVID-19 are more effective than those focusing on creating of herd immunity. Key uncertainties that would affect the choice of vaccination strategies are: (1) the duration of protection against severe disease, (2) the protection against severe disease from variants that escape vaccine-induced immunity, (3) the incidence of long-COVID and level of protection provided by the vaccine, and (4) the rate of serious adverse events following vaccination, stratified by demographic variables.

Numerical simulation of social distancing of preventing airborne transmission in open space with lateral wind direction, taking into account temperature of human body and floor surface

This paper presents the numerical results of particle propagation in open space, taking into account the temperature of the human body and the surface of the ground. And also, the settling of particles or droplets under the action of gravitational force and transport in the open air is taken into account, taking into account the temperature during the process of breathing and sneezing or coughing. The temperature of the body and the surface of the ground, different rates of particle emission from the mouth, such as breathing and coughing or sneezing, are numerically investigated. The effect of temperature, cross-inlet wind, and the velocity of particle ejection from a person's mouth on social distancing is being investigated using a numerical calculation. The variable temperature of the human body forms a thermal plume, which affects the increase in the trajectory of the particle propagation, taking into account the lateral air flow. The thermal plume affects the particles in the breathing zone and spreads the particles over long distances in the direction of the airflow. The result of this work shows that in open space, taking into account the temperature of the body and the surface of the ground, a 2-m social distance may be insufficient for the process of sneezing and social distance must be observed depending on the breathing mode.

How urban versus rural residency relates to COVID-19 vaccine hesitancy: A large-scale national Chinese study

RATIONALE

Although urban-rural residency has been shown to influence individual COVID-19 vaccine hesitancy, the differences between urban and rural China have yet to be uncovered.

OBJECTIVE

This study aims to assess the current prevalence and factors associated with COVID-19 vaccine hesitancy in urban and rural areas and explore whether the rural versus urban residency is associated with COVID-19 vaccine hesitancy.

METHODS

A national, cross-sectional, online survey among Chinese urban and rural adults (≥18 years old) was conducted from 6th to August 9, 2021. A questionnaire was used to collect data on sociodemographic factors, perceptions of the COVID-19 pandemic and vaccination status. A multivariable logistic regression model was used to identify the factors that influence COVID-19 vaccine hesitancy. Propensity score matching (PSM) analysis was performed to explore the association between urban versus rural residency and COVID-19 vaccine hesitancy.

RESULTS

In total, 29,925 participants (80.56% urban participants) were recruited. Urban participants had a higher COVID-19 vaccine hesitancy than their rural counterparts (9.39% vs. 4.26%). After adjusting for potential confounders, we found that COVID-19 vaccine hesitancy among females was lower than that in males in both urban (aOR = 0.78, 95% CI [0.69-0.88]) and rural areas (aOR = 0.54, 95% CI [0.39-0.75]). The lack of trust towards vaccine producers was found to be associated with vaccine hesitancy among the urban participants (aOR = 2.76, 95% CI [2.22-3.43]). The rural floating population had a lower COVID-19 vaccine hesitancy than the rural permanent residents (aOR = 0.58, 95% CI [0.42-0.80]). PSM analysis revealed a 2.38% difference in COVID-19 vaccine hesitancy between urban and rural participants.

CONCLUSIONS

Urban participants were more hesitant to receive the COVID-19 vaccine than rural participants. Priority should be placed on boosting confidence in the healthcare system to reduce COVID-19 vaccine hesitancy among urban residents. Furthermore, we advocate for extra incentives and vaccination education for rural permanent residents.

COVID-19 endgame: From pandemic to endemic? Vaccination, reopening and evolution in low- and high-vaccinated populations

COVID-19 remains a major public health concern, with large resurgences even where there has been widespread uptake of vaccines. Waning immunity and the emergence of new variants will shape the long-term burden and dynamics of COVID-19. We explore the transition to the endemic state, and the endemic incidence in British Columbia (BC), Canada and South Africa (SA), to compare low and high vaccination coverage settings with differing public health policies, using a combination of modelling approaches. We compare reopening (relaxation of public health measures) gradually and rapidly as well as at different vaccination levels. We examine how the eventual endemic state depends on the duration of immunity, the rate of importations, the efficacy of vaccines and the transmissibility. These depend on the evolution of the virus, which continues to undergo selection. Slower reopening leads to a lower peak level of incidence and fewer overall infections in the wave following reopening: as much as a 60% lower peak and a 10% lower total in some illustrative simulations; under realistic parameters, reopening when 70% of the population is vaccinated leads to a large resurgence in cases. The long-term endemic behaviour may stabilize as late as January 2023, with further waves of high incidence occurring depending on the transmissibility of the prevalent variant, duration of immunity, and antigenic drift. We find that long term endemic levels are not necessarily lower than current pandemic levels: in a population of 100,000 with representative parameter settings (Reproduction number 5, 1-year duration of immunity, vaccine efficacy at 80% and importations at 3 cases per 100K per day) there are over 100 daily incident cases in the model. Predicted prevalence at endemicity has increased more than twofold after the emergence and spread of Omicron. The consequent burden on health care systems depends on the severity of infection in immunized or previously infected individuals.

Microbead-based extracorporeal immuno-affinity virus capture: a feasibility study to address the SARS-CoV-2 pandemic

In this paper, we report on the utilization of micro-technology based tools to fight viral infections. Inspired by various hemoperfusion and immune-affinity capture systems, a blood virus depletion device has been developed that offers highly efficient capture and removal of the targeted virus from the circulation, thus decreasing virus load. Single-domain antibodies against the Wuhan (VHH-72) virus strain produced by recombinant DNA technology were immobilized on the surface of glass micro-beads, which were then utilized as stationary phase. For feasibility testing, the virus suspension was flown through the prototype immune-affinity device that captured the viruses and the filtered media left the column. The feasibility test of the proposed technology was performed in a Biosafety Level 4 classified laboratory using the Wuhan SARS-CoV-2 strain. The laboratory scale device actually captured 120,000 virus particles from the culture media circulation proving the feasibility of the suggested technology. This performance has an estimated capture ability of 15 million virus particles by using the therapeutic size column design, representing three times over-engineering with the assumption of 5 million genomic virus copies in an average viremic patient. Our results suggested that this new therapeutic virus capture device could significantly lower virus load thus preventing the development of more severe COVID-19 cases and consequently reducing mortality rate.

Potent Therapeutic Strategies for COVID-19 with Single-Domain Antibody Immunoliposomes Neutralizing SARS-CoV-2 and Lip/cGAMP Enhancing Protective Immunity

The worldwide spread of COVID-19 continues to impact our lives and has led to unprecedented damage to global health and the economy. This highlights the need for an efficient approach to rapidly develop therapeutics and prophylactics against SARS-CoV-2. We modified a single-domain antibody, SARS-CoV-2 VHH, to the surface of the liposomes. These immunoliposomes demonstrated a good neutralizing ability, but could also carry therapeutic compounds. Furthermore, we used the 2019-nCoV RBD-SD1 protein as an antigen with Lip/cGAMP as the adjuvant to immunize mice. Lip/cGAMP enhanced the immunity well. It was demonstrated that the combination of RBD-SD1 and Lip/cGAMP was an effective preventive vaccine. This work presented potent therapeutic anti-SARS-CoV-2 drugs and an effective vaccine to prevent the spread of COVID-19.

Critical assessment of the impact of vaccine-type and immunity on the burden of COVID-19

The COVID-19 pandemic continues to have a devastating impact on health systems and economies across the globe. Implementing public health measures in tandem with effective vaccination strategies have been instrumental in curtailing the burden of the pandemic. With the three vaccines authorized for use in the U.S. having varying efficacies and waning effects against major COVID-19 strains, understanding the impact of these vaccines on COVID-19 incidence and fatalities is critical. Here, we formulate and use mathematical models to assess the impact of vaccine type, vaccination and booster uptake, and waning of natural and vaccine-induced immunity on the incidence and fatalities of COVID-19 and to predict future trends of the disease in the U.S. when existing control measures are reinforced or relaxed. The results show a 5-fold reduction in the control reproduction number during the initial vaccination period and a 1.8-fold (2-fold) reduction in the control reproduction number during the initial first booster (second booster) uptake period, compared to the respective previous periods. Due to waning of vaccine-induced immunity, vaccinating up to 96% of the U.S. population might be required to attain herd immunity, if booster uptake is low. Additionally, vaccinating and boosting more people from the onset of vaccination and booster uptake, especially with the Pfizer-BioNTech and Moderna vaccines (which confer superior protection than the Johnson & Johnson vaccine) would have led to a significant reduction in COVID-19 cases and deaths in the U.S. Furthermore, adopting natural immunity-boosting measures is important in fighting COVID-19 and transmission rate reduction measures such as mask-use are critical in combating COVID-19. The emergence of a more transmissible COVID-19 variant, or early relaxation of existing control measures can lead to a more devastating wave, especially if transmission rate reduction measures and vaccination are relaxed simultaneously, while chances of containing the pandemic are enhanced if both vaccination and transmission rate reduction measures are reinforced simultaneously. We conclude that maintaining or improving existing control measures, and boosting with mRNA vaccines are critical in curtailing the burden of the pandemic in the U.S.

Evaluating the Virology and Evolution of Seasonal Human Coronaviruses Associated with the Common Cold in the COVID-19 Era

Approximately 15-30% of all cases of the common cold are due to human coronavirus infections. More recently, the emergence of the more severe respiratory coronaviruses, SARS-CoV and MERS-CoV, have highlighted the increased pathogenic potential of emergent coronaviruses. Lastly, the current emergence of SARS-CoV-2 has demonstrated not only the potential for significant disease caused by emerging coronaviruses, but also the capacity of novel coronaviruses to promote pandemic spread. Largely driven by the global response to the COVID-19 pandemic, significant research in coronavirus biology has led to advances in our understanding of these viruses. In this review, we evaluate the virology, emergence, and evolution of the four endemic coronaviruses associated with the common cold, their relationship to pandemic SARS-CoV-2, and discuss the potential for future emergent human coronaviruses.

Optimal non-pharmaceutical pandemic response strategies depend critically on time horizons and costs

The COVID-19 pandemic has called for swift action from local governments, which have instated non-pharmaceutical interventions (NPIs) to curb the spread of the disease. The swift implementation of social distancing policies has raised questions about the costs and benefits of strategies that either aim to keep cases as low as possible (suppression) or aim to reach herd immunity quickly (mitigation) to tackle the COVID-19 pandemic. While curbing COVID-19 required blunt instruments, it is unclear whether a less-transmissible and less-deadly emerging pathogen would justify the same response. This paper illuminates this question using a parsimonious transmission model by formulating the social distancing lives vs. livelihoods dilemma as a boundary value problem using calculus of variations. In this setup, society balances the costs and benefits of social distancing contingent on the costs of reducing transmission relative to the burden imposed by the disease. We consider both single-objective and multi-objective formulations of the problem. To the best of our knowledge, our approach is distinct in the sense that strategies emerge from the problem structure rather than being imposed a priori. We find that the relative time-horizon of the pandemic (i.e., the time it takes to develop effective vaccines and treatments) and the relative cost of social distancing influence the choice of the optimal policy. Unsurprisingly, we find that the appropriate policy response depends on these two factors. We discuss the conditions under which each policy archetype (suppression vs. mitigation) appears to be the most appropriate.

Identification of evolutionary trajectories shared across human betacoronaviruses

Comparing the evolution of distantly related viruses can provide insights into common adaptive processes related to shared ecological niches. Phylogenetic approaches, coupled with other molecular evolution tools, can help identify mutations informative on adaptation, whilst the structural contextualization of these to functional sites of proteins may help gain insight into their biological properties. Two zoonotic betacoronaviruses capable of sustained human-to-human transmission have caused pandemics in recent times (SARS-CoV-1 and SARS-CoV-2), whilst a third virus (MERS-CoV) is responsible for sporadic outbreaks linked to animal infections. Moreover, two other betacoronaviruses have circulated endemically in humans for decades (HKU1 and OC43). To search for evidence of adaptive convergence between established and emerging betacoronaviruses capable of sustained human-to-human transmission (HKU1, OC43, SARS-CoV-1 and SARS-CoV-2), we developed a methodological pipeline to classify shared non-synonymous mutations as putatively denoting homoplasy (repeated mutations that do not share direct common ancestry) or stepwise evolution (sequential mutations leading towards a novel genotype). In parallel, we look for evidence of positive selection, and draw upon protein structure data to identify potential biological implications. We find 30 mutations, with four of these [codon sites 18121 (nsp14/residue 28), 21623 (spike/21), 21635 (spike/25) and 23948 (spike/796); SARS-CoV-2 genome numbering] displaying evolution under positive selection and proximity to functional protein regions. Our findings shed light on potential mechanisms underlying betacoronavirus adaptation to the human host and pinpoint common mutational pathways that may occur during establishment of human endemicity.

A new method for spatio-temporal transmission prediction of COVID-19

COVID-19 is the most serious public health event of the 21st century and has had a huge impact across the world. The spatio-temporal pattern analysis and simulation of epidemic spread have become the focus of current research. LSTM model has made a lot of achievements in the prediction of infectious diseases by virtue of its advantages in time prediction, but lacks the spatial expression. CA model plays an important role in epidemic spatial propagation modeling due to its unique evolution characteristics from local to global. However, no existing studies of CA have considered long-term dependence due to the impact of time changes on the evolution of the epidemic, and few have modeled using location data from actual diagnosed patients. Therefore, we proposed a LSTM-CA model to solve above mentioned problems. Base on the advantages of LSTM in temporal level and CA in spatial level, LSTM and CA are integrated from the spatio-temporal perspective of geography based on the fine-grained characteristics of epidemic data. The method divides the study area into regular grids, simulates the spatial interactions between neighborhood cells with the help of CA model, and extracts the parameters affecting the transition probability in CA with the help of LSTM model to assist evolution. Simulations are conducted in Python 3.4 to model the propagation of COVID-19 between Feb, 6 to Mar 20, 2020 in China. Experimental results show that, LSTM-CA performs a higher statistical accuracy than LSTM and spatial accuracy than CA, which could demonstrate the effectiveness of the proposed model. This method could be universal for the temporal and spatial transmission of major public health events. Especially in the early stage of the epidemic, we can quickly understand its development trend and cycle, so as to provide an important reference for epidemic prevention and control and public sentiment counseling.

Impact of coronavirus disease 2019 on lung cancer patients: A meta-analysis

Background

The coronavirus disease 2019 (COVID-19) pandemic poses a great challenge to the treatment of lung cancer patients.

Materials and methods

The PubMed, Embase, and Web of Science databases were searched for studies published before March 15, 2022, and Stata 14.0 software was used to perform a meta-analysis with a random-effects model. The odds ratio (OR) along with the corresponding 95% confidence interval (CI) was reported.

Results

Our meta-analysis included 80 articles with 318,352 patients involved. The proportion of lung cancer patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 2.4% (95% CI: 0.02-0.03) prior to the Omicron variant outbreak. Among COVID-19 patients, those with lung cancer showed a higher mortality rate than those with other types of malignant solid tumors (OR = 1.82, 95% CI: 1.61-2.06) and non-cancer patients (OR = 4.67, 95% CI: 3.61-6.05); however, no significant difference was observed in the mortality rate between patients with lung cancer and those with hematologic malignancies (OR = 1.07, 95% CI: 0.85-1.33). SARS-CoV-2 infection significantly increased the mortality rate in lung cancer patients (OR = 8.94, 95% CI: 6.50-12.31). By contrast, the all-cause mortality rate in lung cancer patients (OR = 1.04, 95% CI: 0.69-1.57) and the proportion of patients diagnosed with advanced lung cancer (OR = 1.04, 95% CI: 0.85-1.27) did not significantly change before and after the pandemic.

Conclusions

More attention should be paid on improving the health of lung cancer patients during the COVID-19 pandemic.

Disaster Preparedness Among Nurses in China: A Cross-Sectional Study

BACKGROUND

Increasingly frequent global disasters such as coronavirus disease 2019 pose a threat to human health and life. The World Health Organization has called on countries to formulate detailed plans to prepare for disasters. It is critical to investigate and evaluate the disaster preparedness of nurses.

PURPOSE

This study was designed to investigate the disaster preparedness and psychological condition of nurses in China and analyze the significant factors influencing their disaster preparedness.

METHODS

A cross-sectional survey was conducted in 2020, and 1,313 nurses were enrolled using convenience sampling. The study questionnaires were distributed and collected via a networking platform equivalent to Amazon Mechanical Turk. The disaster preparedness of the respondents was measured using the Disaster Preparedness Evaluation Tool, the Hospital Anxiety and Depression Scale was used to evaluate anxiety and depression status, and a self-designed questionnaire developed based on a review of the literature was used to explore the potential factors of influence on disaster preparedness.

RESULTS

The average score for disaster preparedness among the participants was 186.34 ( SD = 40.80), which corresponded with a moderate level, especially in skill (mean score = 42.01, SD = 12.39). Items with higher scores included support for the government, personal protection, and health education, whereas items with lower scores included nursing leadership in the community, capacity to cope with chemical or biological attacks, and assessment of posttraumatic stress disorder. Disaster preparedness was negatively related with mental health, including depression and anxiety. The main factors affecting disaster preparedness included educational background, nursing specialty, prior disaster training, prior disaster rescue experience, and depression level.

CONCLUSIONS/IMPLICATIONS FOR PRACTICE

The disaster preparedness of Chinese nurses must be improved. More attention should be paid to disaster preparedness in nurses, and future tailored interventions are urgently needed to promote nursing leadership in the community, the ability to cope with chemical or biological attacks, and posttraumatic stress disorder assessments. Moreover, relieving negative emotions to promote the mental health of nurses should receive greater attention.

Characteristics of subarachnoid hemorrhage associated with COVID-19 infection: A systematic review and descriptive analysis

OBJECTIVE

Subarachnoid hemorrhage (SAH) has been reported as a neurological manifestation in 0.1% of COVID-19 patients. This systematic review investigated the outcomes and predictive factors of SAH in patients with COVID-19.

MATERIALS AND METHODS

An electronic literature search was conducted on PubMed, Embase, and Scopus from inception to 10th September 2021. Studies reporting SAH in COVID-19 patients were included. Demographic characteristics, risk factors for disease, severity of COVID-19, and mortality of SAH in COVID-19 patients were analyzed. Subgroup analyses stratified by COVID-19 severity and mortality were conducted.

RESULTS

17 case reports, 11 case series, and 2 retrospective cohort studies, with a total of 345 cases of SAH in COVID-19 patients, were included for analysis. Most published cases were reported in the US. Mean age was 55±18.4 years, and 162 patients (48.5%) were female. 242 patients (73.8%) had severe-to-critical COVID-19, 56.7% had aneurysmal SAH, 71.4% were on anticoagulation, and 10.8% underwent surgical treatment. 136 out of 333 patients (40.8%) died. Among patients with severe-to-critical COVID-19, 11 out of 18 (61.1%) died, and 8 out of 8 (100.0%) were non-aneurysmal SAH.

CONCLUSIONS

SAH is a rare but morbid occurrence in COVID-19. The mortality rate of COVID-SAH patients was 40.8%, with a higher prevalence of severe-to-critical COVID-19 (100% versus 53.8%) and non-aneurysmal SAH (85.7% versus 44.6%) among COVID-SAH deaths. Given the changing landscape of COVID-19 variants, further studies investigating the association between COVID-19 and SAH may be warranted to identify the long-term effects of COVID-19.

Modelling the impact of the Omicron BA.5 subvariant in New Zealand

New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.

A general urban spreading pattern of COVID-19 and its underlying mechanism

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

Operation Moonshot: rapid translation of a SARS-CoV-2 targeted peptide immunoaffinity liquid chromatography-tandem mass spectrometry test from research into routine clinical use

OBJECTIVES

During 2020, the UK's Department of Health and Social Care (DHSC) established the Moonshot programme to fund various diagnostic approaches for the detection of SARS-CoV-2, the pathogen behind the COVID-19 pandemic. Mass spectrometry was one of the technologies proposed to increase testing capacity.

METHODS

Moonshot funded a multi-phase development programme, bringing together experts from academia, industry and the NHS to develop a state-of-the-art targeted protein assay utilising enrichment and liquid chromatography tandem mass spectrometry (LC-MS/MS) to capture and detect low levels of tryptic peptides derived from SARS-CoV-2 virus. The assay relies on detection of target peptides, ADETQALPQRK (ADE) and AYNVTQAFGR (AYN), derived from the nucleocapsid protein of SARS-CoV-2, measurement of which allowed the specific, sensitive, and robust detection of the virus from nasopharyngeal (NP) swabs. The diagnostic sensitivity and specificity of LC-MS/MS was compared with reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) via a prospective study.

RESULTS

Analysis of NP swabs (n=361) with a median RT-qPCR quantification cycle (Cq) of 27 (range 16.7-39.1) demonstrated diagnostic sensitivity of 92.4% (87.4-95.5), specificity of 97.4% (94.0-98.9) and near total concordance with RT-qPCR (Cohen's Kappa 0.90). Excluding Cq>32 samples, sensitivity was 97.9% (94.1-99.3), specificity 97.4% (94.0-98.9) and Cohen's Kappa 0.95.

CONCLUSIONS

This unique collaboration between academia, industry and the NHS enabled development, translation, and validation of a SARS-CoV-2 method in NP swabs to be achieved in 5 months. This pilot provides a model and pipeline for future accelerated development and implementation of LC-MS/MS protein/peptide assays into the routine clinical laboratory.

Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference

BACKGROUND

One of the main challenges of the COVID-19 pandemic is to make sense of available, but often heterogeneous and noisy data. This contribution presents a data-driven methodology that allows exploring the hospitalization dynamics of COVID-19, exemplified with a study of 17 autonomous regions in Spain from summer 2020 to summer 2021.

METHODS

We use data on new daily cases and hospitalizations reported by the Spanish Ministry of Health to implement a Bayesian inference method that allows making short-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country.

RESULTS

We show how to use the temporal series for the number of daily admissions and discharges from hospital to reproduce the hospitalization dynamics of COVID-19 patients. For the case-study of the region of Aragon, we estimate that the probability of being admitted to hospital care upon infection is 0.090 [0.086-0.094], (95% C.I.), with the distribution governing hospital admission yielding a median interval of 3.5 days and an IQR of 7 days. Likewise, the distribution on the length of stay produces estimates of 12 days for the median and 10 days for the IQR. A comparison between model parameters for the regions analyzed allows to detect differences and changes in policies of the health authorities.

CONCLUSIONS

We observe important regional differences, signaling that to properly compare very different populations, it is paramount to acknowledge all the diversity in terms of culture, socio-economic status, and resource availability. To better understand the impact of this pandemic, much more data, disaggregated and properly annotated, should be made available.

Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States

Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complementary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are designed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods-tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of multiple population sizes across the United States-frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number R_t becomes larger than 1 for a period of 2 weeks.

Comparison of ocular trauma between normalized and the COVID-19 epidemic periods in China: a multi-center cross-sectional study

AIM

To compare the feature of ocular trauma between normalized period and the COVID-19 epidemic period in China, and to provide a profile for eye injuries in special times in future.

METHODS

This is a multi-center cross-sectional study with 30 participated hospitals involving the China Ocular Trauma Society members. All hospitalized cases who visited the Ophthalmology Department in participated hospitals with eye injuries during the normalized period (2019) and the COVID-19 epidemic period (2020) were included in this study. Demographic characteristic of cases, date of injury, sites and types of injury were collected.

RESULTS

This study involved 13 525 (61 cases with both eyes) injured cases. There were 7269 (53.74%) eye-injured cases and 6256 (46.26%) eye-injured cases in 2019 and 2020 separately. Compared with 2019, the incidence of ocular trauma in retirees, housewives and unemployed increased with year-on-year of 4.96%, 102.67%, and 11.64% among all occupations. In 2020, the incidence of eye injuries decreased in all injury sites except for an increase in home (30.29% year-on-year). The incidence of mechanical eye injuries decreased, while that of non-mechanical eye injuries (chemical/thermal/radiation) increased (47.45% year-on-year). There were 255 (3.51%, 255/7269) and 376 (6.01%, 376/6256) non-mechanical injured cases in 2019 and 2020 (Pearson Chi²=47.33, P<0.001) separately.

CONCLUSION

During the COVID-19 epidemic period, the total cases of ocular trauma decrease but the proportion of non-mechanical ocular trauma increase. Penetrating is still the highest proportion among all types of mechanical ocular trauma. From a preventive point of view, protection for retired persons, housewives and unemployed persons should be improved during public health events period.

Determinants of social distancing adherence

Introduction

Governments and public health authorities across many jurisdictions implemented social (physical) distancing measures to contain the spread of the 2019 novel coronavirus disease (COVID-19). Adherence to these measures is variable and likely influenced by various factors. This study aimed to 1) identify the individual sociodemographic, COVID-19 and social distancing related, and psychological determinants of social distancing adherence, and 2) explore regional differences in social distancing adherence in the United States (U.S.) and English-speaking Canada based on each region's discrepant response to social distancing restrictions.

Methods

A web-based repeated cross-sectional survey was conducted in 4,942 English-speaking participants from the four most populous U.S. states, specifically New York, California, Texas, and Florida, and Canada (www.covid19-database.com). The study was conducted at two timepoints, from May 1 to 5, 2020 (n = 1,019, Canadian participants only) and from July 6 to 10, 2020 (n = 3,923). Separate univariate models were computed for individual sociodemographic, COVID-19 and social distancing related, and psychological determinants of social distancing adherence. To determine the total variance explained, a univariate analysis including all of the determinants was performed. Regional differences in social distancing were compared between the four U.S. states and Canada, and between the U.S. as a whole and Canada.

Results

Adherence to social distancing was higher in May (mean = 4.4/5.0±0.7) compared to July (mean = 4.3/5.0±0.7) [t ₍₄₉₄₀₎ = 6.96, p < 0.001], likely a reflection of relaxing restrictions. There were no regional differences in adherence. Sociodemographic, COVID-19 and social distancing related, and psychological determinants explained 10, 36, and 23% of the variance of social distancing adherence, respectively. Higher perceived seriousness of COVID-19 [β (SE) = 0.39 (0.01), p < 0.001, partial η² = 0.22], lower risk propensity [β (SE) = -0.15 (0.01), p < 0.001, partial η² = 0.06], germ aversion [β (SE) = 0.12 (0.01), p < 0.001, partial η² = 0.03], age [β (SE) = 0.01 (0.00), p < 0.001, partial η² = 0.02], and greater social support [β (SE) = 0.03 (0.00), p < 0.001, partial η² = 0.02] had the largest effects on social distancing adherence.

Conclusion

Public service initiatives to emphasize the serious consequences of infection and targeted interventions toward certain sociodemographic groups, such as younger adults and vulnerable individuals in greater need of social support, may help enhance the public's adherence to social distancing measures during subsequent waves of COVID-19 and future pandemics.

Densely attention mechanism based network for COVID-19 detection in chest X-rays

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.

COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic has prompted a lot of questions globally regarding the range of information about the virus's possible routes of transmission, diagnostics, and therapeutic tools. Worldwide studies have pointed out the importance of monitoring and early surveillance techniques based on the identification of viral RNA in wastewater. These studies indicated the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in human feces, which is shed via excreta including mucus, feces, saliva, and sputum. Subsequently, they get dumped into wastewater, and their presence in wastewater provides a possibility of using it as a tool to help prevent and eradicate the virus. Its monitoring is still done in many regions worldwide and serves as an early "warning signal"; however, a lot of limitations of wastewater surveillance have also been identified.

Living with COVID-19 and preparing for future pandemics: revisiting lessons from the HIV pandemic

In April, 2020, just months into the COVID-19 pandemic, an international group of public health researchers published three lessons learned from the HIV pandemic for the response to COVID-19, which were to: anticipate health inequalities, create an enabling environment to support behavioural change, and engage a multidisciplinary effort. We revisit these lessons in light of more than 2 years' experience with the COVID-19 pandemic. With specific examples, we detail how inequalities have played out within and between countries, highlight factors that support or impede the creation of enabling environments, and note ongoing issues with the scarcity of integrated science and health system approaches. We argue that to better apply lessons learned as the COVID-19 pandemic matures and other infectious disease outbreaks emerge, it will be imperative to create dialogue among polarised perspectives, identify shared priorities, and draw on multidisciplinary evidence.

COVID-19 and Eye Banking: Ongoing Impacts of the Pandemic

PURPOSE

The purpose of this study was to assess the impact of ongoing waves of the COVID-19 pandemic and resulting guidelines on the corneal donor pool with resumption of clinical operations.

METHODS

A retrospective analysis of donors deemed eligible for corneal transplantation at an eye bank from July 1, 2020, through December 31, 2021. Donors ineligible due to meeting Eye Bank Association of America (EBAA) COVID-19 guidelines or a positive postmortem COVID-19 testing were examined. The correlation between COVID-19 rule outs and state COVID positivity was calculated. The number of scheduled surgeries, suitable corneas, imports, and international exports was compared with a pre-COVID period. Postmortem testing was reduced for the final 5 months of the study, and numbers were compared before and after the policy change.

RESULTS

2.85% of referrals to the eye bank were ruled out because of EBAA guidelines. 3.2% of postmortem tests were positive or indeterminate resulting in an ineligible tissue donor (0.42% of referrals). Over the 18-month period, there was a 4.30% shortage of suitable corneas compared with transplantation procedures. There was a significant correlation between postmortem testing and state COVID-19 positivity (r = 0.37, P <0.01), but not with EBAA guidelines (r = 0.19, P = 0.07). When postmortem testing was reduced, significantly more corneas were exported internationally.

CONCLUSIONS

Although corneal transplant procedures were back to normal levels, there was a shortage of suitable corneal tissue. The discontinuation of postmortem testing was associated with a significant increase in international exports of corneal donor tissue.

The mitigating effect of masks on the spread of Covid-19

We use US state-level data from early in the pandemic -March 15, 2020 to November 15, 2020- to estimate the effects of mask mandates and compliance with mandates on Covid-19 cases and deaths, conditional on mobility. A one-standard-deviation increase in mobility is associated with a 6 to 20 percent increase in the cases growth rate; a mask mandate can offset about one third of this increase with our most conservative estimates. Also, mask mandates are more effective in states with higher compliance. Given realized mobility, our estimates imply that total infections in the US on November 15, 2020 would have been 23.7 to 30.4 percent lower if a national mask mandate had been enacted on May 15, 2020. This reduction in cases translates to a 25 to 35 percent smaller decline in aggregate hours worked over the same period relative to a 2019 baseline.

A hospital demand and capacity intervention approach for COVID-19

The mathematical interpretation of interventions for the mitigation of epidemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of the number of infections to manage impact. Whilst these methods may work in theory, in order to implement effectively they may require information which is not likely to be available in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In reality, testing and cases data can only be as good as the policy of implementation and the compliance of the individuals, which implies that accurately estimating the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we demonstrate a different approach to the mathematical modelling of interventions, not based on optimality or cases, but based on demand and capacity of hospitals who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate a susceptible-exposed-infectious-recovered-died type model to infer parameters that depict the dynamics of the epidemic in several regions of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture. We provide an optimisation method to capture when, in terms of healthcare demand, an intervention should be put into place given a maximum capacity on the service. By using an equivalent agent-based approach, we demonstrate uncertainty quantification on the likelihood that capacity is not breached, by how much if it does, and the limit on demand that almost guarantees capacity is not breached.

A 'health message' on sustainable physical and mental health for the prolonged COVID-19 and other pandemics

The world has been living with another pandemic physical inactivity (PI) and sedentary behavior (SB) and of other nature for years. As social measures to prevent the spread of COVID-19 have been strengthened, the physical activity (PA) barrier has been hit and the PI/SB pandemic has been exacerbated, and in the current situation, triggered by its prolonged period, it is emerging as a problem to be considered in order to combat the PI/SB pandemic through PA and to maintain continuous physical and mental health. This review aims to summarize existing and recent studies on the effects of PA/SB on physical and mental health in preparation for the context of prolonged COVID-19 and provides an easy and fun way to motivate, access PA, and PA recommendations for maintaining physical and mental health. We performed search strategies on PubMed, Medline, Scopus, and Google Scholar databases, and used a literature review of the current available literature on the COVID-19 and the effects of PI, SB, and PA on physical and mental health. The clinical relevance of this review shows that integrating PA into daily life and reducing prolonged sitting time is an important strategy for continuing a healthy life physically and mentally. As a result, for those who have not yet incorporated exercise as part of their daily routine, fun and easy access to PA suggests that in the long run, may be an opportunity to implement newer habits. In addition, in an emergency such as the COVID-19 quarantine period or other pandemics, it is essential for one to maintain health through PA at home, and underscore the 'health message' of the current review that reducing SB, progressively increasing PA, and doing more PA regularly in the current context prolonged COVID-19 can bring significant benefits to long-term habits and physical and mental health.

Duration of immune protection of SARS-CoV-2 natural infection against reinfection

BACKGROUND

The future of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic hinges on virus evolution and duration of immune protection of natural infection against reinfection. We investigated the duration of protection afforded by natural infection, the effect of viral immune evasion on duration of protection and protection against severe reinfection, in Qatar, between 28 February 2020 and 5 June 2022.

METHODS

Three national, matched, retrospective cohort studies were conducted to compare the incidence of SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) severity among unvaccinated persons with a documented SARS-CoV-2 primary infection, to incidence among those infection-naïve and unvaccinated. Associations were estimated using Cox proportional hazard regression models.

RESULTS

Effectiveness of pre-Omicron primary infection against pre-Omicron reinfection was 85.5% [95% confidence interval (CI): 84.8-86.2%]. Effectiveness peaked at 90.5% (95% CI: 88.4-92.3%) in the 7th month after the primary infection, but waned to ~ 70% by the 16th month. Extrapolating this waning trend using a Gompertz curve suggested an effectiveness of 50% in the 22nd month and < 10% by the 32nd month. Effectiveness of pre-Omicron primary infection against Omicron reinfection was 38.1% (95% CI: 36.3-39.8%) and declined with time since primary infection. A Gompertz curve suggested an effectiveness of < 10% by the 15th month. Effectiveness of primary infection against severe, critical or fatal COVID-19 reinfection was 97.3% (95% CI: 94.9-98.6%), irrespective of the variant of primary infection or reinfection, and with no evidence for waning. Similar results were found in sub-group analyses for those ≥50 years of age.

CONCLUSIONS

Protection of natural infection against reinfection wanes and may diminish within a few years. Viral immune evasion accelerates this waning. Protection against severe reinfection remains very strong, with no evidence for waning, irrespective of variant, for over 14 months after primary infection.

High Seroprevalence of Anti-SARS-CoV-2 IgM/IgG among Inhabitants of Sakaka City, Aljouf, Saudi Arabia

(1) Backgrounds and Objectives: The global battle to contain the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is still ongoing. This cross-sectional study aimed to detect the seroprevalence of anti-SARS-CoV-2 IgM/IgG among previously symptomatic/asymptomatic and vaccinated/unvaccinated inhabitants of Sakaka City, Aljouf, Saudi Arabia. (2) Methods: Blood samples of 400 participants were tested for the presence of anti-SARS-CoV-2 IgM/IgG using colloidal gold immuno-chromatography lateral flow immunoassay cards. (3) Results: The prevalence of anti-SARS-CoV-2 IgM and IgG positivity was 45.8% and 42.3%, respectively. Statistically significant correlations (p < 0.05) were found between the previous RT-PCR testing for SARS-CoV-2-RNA and positivity for IgM and/or IgG. The highest seroprevalence of IgM and IgG were detected among smokers, participants aged ≥40 years, and patients with chronic diseases. Although most of the participants (58.5%) did not previously experience COVID-19 like symptoms, the anti-SARS-CoV-2 IgM and IgG seropositivity amongst them was 49.1% and 25.6%, respectively, with higher seroprevalence among males than females. At the time of the study, the SARS-CoV-2 vaccination rate at our locality in Saudi Arabia was 43.8% with statistically significant correlation (p < 0.001) between being vaccinated and anti-SARS-CoV-2 IgM and/or IgG positivity, with more positivity after receiving the second vaccine dose. (4) Conclusions: Public assessment reflects the real scale of the disease exposure among the community and helps in identifying the asymptomatic carriers that constitute a major problem for controlling the SARS-CoV-2. To limit the spread of the virus, rigorous implementation of large-scale SARS-CoV-2 vaccination and anti-SARS-CoV-2 serological testing strategies should be empowered.

Drivers of COVID-19 policy stringency in 175 countries and territories: COVID-19 cases and deaths, gross domestic products per capita, and health expenditures

Background

New data on COVID-19 may influence the stringency of containment policies, but these potential effect are not understood. We aimed to understand the associations of new COVID-19 cases and deaths with policy stringency globally and regionally.

Methods

We modelled the marginal effects of new COVID-19 cases and deaths on policy stringency (scored 0-100) in 175 countries and territories, adjusting for gross domestic product (GDP) per capita and health expenditure (% of GDP), and public expenditure on health. The time periods examined were March to August 2020, September 2020 to February 2021, and March to August 2021.

Results

Policy response to new cases and deaths was faster and more stringent early in the COVID-19 pandemic (March to August 2020) compared to subsequent periods. New deaths were more strongly associated with stringent policies than new cases. In an average week, one new death per 100 000 people was associated with a stringency increase of 2.1 units in the March to August 2020 period, 1.3 units in the September 2020 to February 2021 period, and 0.7 units in the March to August 2021 period. New deaths in Africa and the Western Pacific were associated with more stringency than in other regions. Higher health expenditure as a percentage of GDP was associated with less stringent policies. Similarly, higher public expenditure on health by governments was mostly associated with less stringency across all three periods. GDP per capita did not have consistent patterns of associations with stringency.

Conclusions

The stringency of COVID-19 policies was more strongly associated with new deaths than new cases. Our findings demonstrate the need for enhanced mortality surveillance to ensure policy alignment during health emergencies. Countries that invest less in health or have a lower public expenditure on health may be inclined to enact more stringent policies. This new empirical understanding of COVID-19 policy drivers can help public health officials anticipate and shape policy responses in future health emergencies.

Has COVID-19 Modified the Weight of Known Systemic Inflammation Indexes and the New Ones (MCVL and IIC) in the Assessment as Predictive Factors of Complications and Mortality in Acute Pancreatitis?

We aimed at evaluating the prognostic capacity of the inflammatory indices derived from routine complete blood cell counts in two groups of patients with acute pancreatitis from two different time periods, before and during the COVID-19 pandemic, when a high incidence of complications with surgical risk and mortality was found. Two new markers were introduced: the mean corpuscular volume to lymphocyte ratio (MCVL) and the cumulative inflammatory index (IIC), which were calculated at a baseline in the two groups of patients. Of the already established markers, none of them managed to effectively predict the complications with surgical risk and mortality, with a decrease of less than 50% in specificity in the peri-COVID group. The MCVL had the best prediction of complications with surgical risk in both the pre-COVID and peri-COVID groups, validated it as an independent factor by multivariate analysis. The IIC had the best prediction of mortality in both periods and was proven to be an independent factor by multivariate analysis. As the IIC predicted death best, we tested the occurrence of death and found that patients with PA who had an IIC > 12.12 presented a risk of death 4.08 times higher in the pre-COVID group and 3.33 times higher in the peri-COVID group. The new MCVL and IIC independent markers had a superior sensitivity and specificity in predicting surgical risk complications and, respectively, mortality in the group of patients with acute pancreatitis during the COVID-19 pandemic, which makes them widely applicable in populations with modified immune and inflammatory status. Conclusions: In patients with acute pancreatitis, MCVL has a significant predictive value regarding complications with surgical risk (abscess, necrosis, and pseudocyst), and the IIC has a significant predictive value for mortality.

Agent-based model using GPS analysis for infection spread and inhibition mechanism of SARS-CoV-2 in Tokyo

Analyzing the SARS-CoV-2 pandemic outbreak based on actual data while reflecting the characteristics of the real city provides beneficial information for taking reasonable infection control measures in the future. We demonstrate agent-based modeling for Tokyo based on GPS information and official national statistics and perform a spatiotemporal analysis of the infection situation in Tokyo. As a result of the simulation during the first wave of SARS-CoV-2 in Tokyo using real GPS data, the infection occurred in the service industry, such as restaurants, in the city center, and then the infected people brought back the virus to the residential area; the infection spread in each area in Tokyo. This phenomenon clarifies that the spread of infection can be curbed by suppressing going out or strengthening infection prevention measures in service facilities. It was shown that pandemic measures in Tokyo could be achieved not only by strong control, such as the lockdown of cities, but also by thorough infection prevention measures in service facilities, which explains the curb phenomena in real Tokyo.

Access to healthcare for people aged 50+ in Europe during the COVID-19 outbreak

This paper combines SHARE Corona Survey and SHARE Wave 7 data for 25 European countries and Israel (N = 40,919) with institutional and epidemic-related country characteristics to investigate healthcare access for Europeans aged 50+ during the outbreak of COVID-19. We use a micro-macro approach to examine whether and to what extent barriers to accessing healthcare measured by reported unmet healthcare needs vary within and between countries. We consider various aspects of barriers and distinguish among: (1) respondents who forewent medical treatment because they were afraid of becoming infected with the Coronavirus; (2) respondents who had pre-scheduled medical appointments postponed by health providers due to the outbreak; and (3) respondents who tried to arrange a medical appointment but were denied one. Limited access to healthcare during the initial outbreak was more common for the occupationally active, women, the more educated and those living in urban areas. A bad economic situation, poor overall health and higher healthcare utilisation were robust predictors of unmet healthcare. People aged 50+ in countries of 'Old' Europe, countries with higher universal health coverage and stricter containment and closure policies were more likely to have medical services postponed. Policymakers should address the healthcare needs of older people with chronic health conditions and a poor socio-economic status who were made more vulnerable by this pandemic. In the aftermath of the health crisis, public health systems might experience a great revival in healthcare demand, a challenge that should be mitigated by careful planning and provision of healthcare services.

Can We Communicate Autonomy Support and a Mandate? How Motivating Messages Relate to Motivation for Staying at Home across Time during the COVID-19 Pandemic

A multi-wave study across two months tested changes in motivation for staying at home at the beginning of the COVID-19 outbreak in the UK and US in 683 living-alone older adults (mean age = 53 years), those that might experience greater psychological costs of being isolated for long periods of time. The study was focused on changes in two types of motivation: autonomous motivation- finding importance in the task of staying at home, and controlled motivation- staying at home because of felt pressure or choicelessness, as autonomous motivation predicts effective behavior change better than controlled motivation, especially long-term. Predictions grounded in self-determination theory (SDT) tested whether three motivating aspects of messages to stay at home from governmental and public health agencies, physicians, the news, and family and friends predicted changes in these motivations across time. Perceiving messages to stay at home as controlling predicted increases in controlled motivation and decreases in autonomous motivation over two months. Conversely, perceiving messages to stay at home as autonomy supportive predicted increases in autonomous motivation over two months. Results for mandated orders to stay at home were intriguing: they related to increases in both controlled and autonomous motivations over time. Exploratory analyses revealed that increases in autonomous motivation over time predicted actual time spent at home reported at Wave 2, whereas increases in controlled motivation did not relate. Discussion focuses on contributions to theory and public health messaging about behavioral change.

Optimizing prevalence estimates for a novel pathogen by reducing uncertainty in test characteristics

Emergence of a novel pathogen drives the urgent need for diagnostic tests that can aid in defining disease prevalence. The limitations associated with rapid development and deployment of these tests result in a dilemma: In efforts to optimize prevalence estimates, would tests be better used in the lab to reduce uncertainty in test characteristics or to increase sample size in field studies? Here, we provide a framework to address this question through a joint Bayesian model that simultaneously analyzes lab validation and field survey data, and we define the impact of test allocation on inferences of sensitivity, specificity, and prevalence. In many scenarios, prevalence estimates can be most improved by apportioning additional effort towards validation rather than to the field. The joint model provides superior estimation of prevalence, sensitivity, and specificity, compared with typical analyses that model lab and field data separately, and it can be used to inform sample allocation when testing is limited.