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

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Impact of early phase COVID-19 precautionary behaviors on seasonal influenza in Hong Kong: A time-series modeling approach

Background

Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies.

Methods

We estimated the effective reproduction number (R _t ) of seasonal influenza in 2019/20 winter using a time-series susceptible-infectious-recovered (TS-SIR) model with a Bayesian inference by integrated nested Laplace approximation (INLA). After taking account of changes in underreporting and herd immunity, the individual effects of the behavioral changes were quantified.

Findings

The model-estimated mean R _t reduced from 1.29 (95%CI, 1.27-1.32) to 0.73 (95%CI, 0.73-0.74) after the COVID-19 community spread began. Wearing face masks protected 17.4% of people (95%CI, 16.3-18.3%) from infections, having about half of the effect as avoiding crowded places (44.1%, 95%CI, 43.5-44.7%). Within the current model, if more than 85% of people had adopted both behaviors, the initial R _t could have been less than 1.

Conclusion

Our model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.

Revealing contagion

Mathematical models help predict and manage the course of pandemics

Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data

Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location-related data contributed by 10,527,737 smartphone users in Wuhan, China. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does, indicating a sub-sampled dataset would be as good at prediction. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.

Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China

Objectives: Waves of epidemics associated with Omicron variant of Coronavirus Disease 2019 (COVID-19) in major cities in China this year have been controlled. It is of great importance to study the transmission characteristics of these cases to support further interventions.

Methods: We simulate the transmission trajectory and analyze the intervention influences of waves associated with Omicron variant in major cities in China using the Suspected-Exposed-Infectious-Removed (SEIR) model. In addition, we propose a model using a function between the maximum daily infections and the duration of the epidemic, calibrated with data from Chinese cities.

Results: An infection period of 5 days and basic reproduction number R0 between 2 and 8.72 are most appropriate for most cases in China. Control measures show a significant impact on reducing R0, and the earlier control measures are implemented, the shorter the epidemic will last. Our proposed model performs well in predicting the duration of the epidemic with an average error of 2.49 days.

Conclusion: Our results show great potential in epidemic model simulation and predicting the end date of the Omicron epidemic effectively and efficiently.

Cities in a pandemic: Evidence from China

This paper studies the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city-specific and time-specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle.

Effectiveness of Lianhua Qingwen Capsule in Treatment of Asymptomatic COVID-19 Patients: A Randomized, Controlled Multicenter Trial

Background: Asymptomatic patients are unneglected sources in propagating transmission chain due to their high viral loads. However, treatments available based on symptoms seem not applicable to asymptomatic patients. In this study, the authors want to estimate the effectiveness of Lianhua Qingwen (LH) capsule on asymptomatic coronavirus disease 2019 (COVID-19) patients. Methods: A randomized controlled trial (RCT) was performed to explore the effectiveness and safety of LH capsule in treating asymptomatic COVID-19 patients. Patients were randomized to control group (isolated observation) and treatment group (LH, 4 capsules, thrice daily) for 14 days. The primary endpoints were the rate and time of nucleic acid turning negative during the isolation observation. Results: A total of 120 participants were included in the full analysis set (60 each in the control and treatment groups). Data showed that the rate of nucleic acid turning negative during the isolation observation in the treatment group was higher than that in the control group (rate difference: 21.66%, 95% confidence interval [CI]: 4.34 to 37.27, p = 0.0142). Patients in the treatment group have a shorter time of nucleic acid turning negative (7.5 vs. 14.5 days, p = 0.018). Moreover, the rate of clinical symptoms appearance in the treatment group was lower compared with that in the control group (rate difference: -31.67, 95% CI: -46.83 to -13.82, p = 0.0005). The proportion of confirmed mild and common cases in the treatment group was also lower (35.00% vs. 66.67%, p = 0.0005). No serious adverse events were documented. Conclusions: In this study, the authors illustrated that LH capsule is beneficial to asymptomatic COVID-19 patients. Considering the lack of interventions for treating asymptomatic COVID-19 patients at this stage, LH capsule could be considered as a choice. Chinese Clinical Trial Registry: ChiCTR2100042066.

COVID-19 and entrepreneurship entry and exit: Opportunity amidst adversity

We theoretically and empirically examine how acquiring new skills and increased financial worries influenced entrepreneurship entry and exit intentions during the pandemic. To that end, we analyze primary individual-level survey data we collected in the aftermath of the COVID-19's first wave in Russia, which has had one of the highest COVID-19 infection rates globally. Our results show that acquiring new skills during the pandemic helped owners keep their existing businesses and encouraged start-ups in sectors other than information technology (IT). For IT start-ups, having previous experience matters more than new skills. While the pandemic-driven financial worries are associated with business closure intentions, they also inspire new business start-ups, highlighting the pandemic's creative destruction power. Furthermore, preferences for formal employment and remote work also matter for entrepreneurial intentions. Our findings enhance the understanding of entrepreneurship formation and closure in a time of adversity and suggest that implementing entrepreneurship training and upskilling policies during recurring waves of the COVID-19 pandemic can be an important policy tool for innovative small business development.

Pre-clinical validation of a turbine-based ventilator for invasive ventilation-The ACUTE-19 ventilator

BACKGROUND

The Severe Acute Respiratory Syndrome (SARS)-Coronavirus 2 (CoV-2) pandemic pressure on healthcare systems can exhaust ventilator resources, especially where resources are restricted. Our objective was a rapid preclinical evaluation of a newly developed turbine-based ventilator, named the ACUTE-19, for invasive ventilation.

METHODS

Validation consisted of (a) testing tidal volume (V_T) delivery in 11 simulated models, with various resistances and compliances; (b) comparison with a commercial ventilator (VIVO-50) adapting the United Kingdom Medicines and Healthcare products Regulatory Agency-recommendations for rapidly manufactured ventilators; and (c) in vivo testing in a sheep before and after inducing acute respiratory distress syndrome (ARDS) by saline lavage.

RESULTS

Differences in V_T in the simulated models were marginally different (largest difference 33ml [95%-confidence interval (CI) 31-36]; P<.001ml). Plateau pressure (P_plat) was not different (-0.3cmH₂O [95%-CI -0.9 to 0.3]; P=.409), and positive end-expiratory pressure (PEEP) was marginally different (0.3 cmH₂O [95%-CI 0.2 to 0.3]; P<.001) between the ACUTE-19 and the commercial ventilator. Bland-Altman analyses showed good agreement (mean bias, -0.29, [limits of agreement, 0.82 to -1.42], and mean bias 0.56 [limits of agreement, 1.94 to -0.81], at a P_plat of 15 and 30cmH₂O, respectively). The ACUTE-19 achieved optimal oxygenation and ventilation before and after ARDS induction.

CONCLUSIONS

The ACUTE-19 performed accurately in simulated and animal models yielding a comparable performance with a VIVO-50 commercial device. The acute 19 can provide the basis for the development of a future affordable commercial ventilator.

Evolution and control of the COVID-19 pandemic: A global perspective

We investigated the factors influencing the progression of the pandemic from a global perspective by using the Geodetector and Correlation methods and explored the pandemic response policies and effects in different countries. The results yielded three notable findings. First, empirical results show the COVID-19 pandemic is influenced by various factors, including demographic and economic parameters, international travelers, urbanization ratio, urban population, etc. Among them, the correlation between urban population and confirmed cases is strongest. Cities become the key factor affecting the COVID-19 pandemic, with high urbanization levels and population mobility increases the risk of large-scale outbreaks. Second, among control measures, School-closures, International-travel-restrictions, and Public-gathering-restriction have the best control effect on the epidemic. In addition, the combination of different types of control measures is more effective in controlling the outbreak, especially for Public-gathering-restrictions ∩ School-closures, International-travel-restrictions ∩ Workplace-closures, Public-transport-restrictions ∩ International-travel-restrictions. Third, implementing appropriate control measures in the first month of an outbreak played a critical role in future pandemic trends. Since there are few local cases in this period and the control measures have an obvious effect.

How to design virus containment policies? A joint analysis of economic and epidemic dynamics under the COVID-19 pandemic

We analyze the impact of different designs of COVID-19-related lockdown policies on economic loss and mortality using a micro-level simulation model, which combines a multi-sectoral closed economy with an epidemic transmission model. In particular, the model captures explicitly the (stochastic) effect of interactions between heterogeneous agents during different economic activities on virus transmissions. The empirical validity of the model is established using data on economic and pandemic dynamics in Germany in the first 6 months after the COVID-19 outbreak. We show that a policy-inducing switch between a strict lockdown and a full opening-up of economic activity based on a high incidence threshold is strictly dominated by alternative policies, which are based on a low incidence threshold combined with a light lockdown with weak restrictions of economic activity or even a continuous weak lockdown. Furthermore, also the ex ante variance of the economic loss suffered during the pandemic is substantially lower under these policies. Keeping the other policy parameters fixed, a variation of the consumption restrictions during the lockdown induces a trade-off between GDP loss and mortality. Furthermore, we study the robustness of these findings with respect to alternative pandemic scenarios and examine the optimal timing of lifting containment measures in light of a vaccination rollout in the population.

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems.

Can a Two-Dose Influenza Vaccine Regimen Better Protect Older Adults? An Agent-Based Modeling Study

Older adults (age ≥ 65) are at high risk of influenza morbidity and mortality. This study evaluated the impact of a hypothetical two-dose influenza vaccine regimen per season to reduce symptomatic flu cases by providing preseason (first dose) and mid-season (second dose) protection to offset waning vaccine effectiveness (VE). The Framework for Reconstructing Epidemiological Dynamics (FRED), an agent-based modeling platform, was used to compare typical one-dose vaccination to a two-dose vaccination strategy. Primary models incorporated waning VE of 10% per month and varied influenza season timing (December through March) to estimate cases and hospitalizations in older adults. Additional scenarios modeled reductions in uptake and VE of the second dose, and overall waning. In seasons with later peaks, two vaccine doses had the largest potential to reduce cases (14.4% with February peak, 18.7% with March peak) and hospitalizations (13.1% with February peak, 16.8% with March peak). Reductions in cases and hospitalizations still resulted but decreased when 30% of individuals failed to receive a second dose, second dose VE was reduced, or overall waning was reduced to 7% per month. Agent-based modeling indicates that two influenza vaccine doses could decrease cases and hospitalizations in older individuals. The highest impact occurred in the more frequently observed late-peak seasons. The beneficial impact of the two-dose regimen persisted despite model scenarios of reduced uptake of the second dose, decreased VE of the second dose, or overall VE waning.

Ultrafast one-minute electronic detection of SARS-CoV-2 infection by 3CLpro enzymatic activity in untreated saliva samples

Since its onset in December 2019, severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, has caused over 6.5 million deaths worldwide as of October 2022. Attempts to curb viral transmission rely heavily on reliable testing to detect infections since a large number of transmissions are carried through asymptomatic individuals. Many available detection methods fall short in terms of reliability or point-of-care applicability. Here, we report an electrochemical approach targeting a viral proteolytic enzyme, 3CLpro, as a marker of active infection. We detect proteolytic activity directly from untreated saliva within one minute of sample incubation using a reduction-oxidation pH indicator. Importantly, clinical tests of saliva samples from 50 subjects show accurate detection of SARS-CoV-2, with high sensitivity and specificity, validated by PCR testing. These, coupled with our platform's ultrafast detection, simplicity, low cost and point-of-care compatibility, make it a promising method for the real-world SARS-CoV-2 mass-screening.

Flexible pressure and temperature dual-mode sensor based on buckling carbon nanofibers for respiration pattern recognition

Breathing condition is an essential physiological indicator closely related to human health. Wearable flexible breath sensors for respiration pattern recognition have attracted much attention as they can provide physiological signal details for personal medical diagnosis, health monitoring, etc. However, present smart mask based on flexible breath sensors using single-mode detection can only detect a relatively small number of respiration patterns, especially lacking the ability to accurately distinguish mouth breath from nasal one. Herein, a smart face mask incorporated with a dual-sensing mode breathing sensor that can recognize up to eight human respiration patterns is fabricated. The breathing sensor uses novel three dimensional (3D) buckling carbon nanofiber mats as active materials to realize the function of pressure and temperature sensing simultaneously. The pressure model of the sensors shows a high sensitivity that are able to precisely detect pressure generated by respiratory airflow, while the temperature model can realize non-contact temperature variation caused by breath. Benefit from the capacity of real-time recognition and accurate distinguishing between mouth breath and nasal breath, the face mask is further developed to monitor the development of mouth breathing syndrome. The dual-sensing mode sensor has great potential applications in health monitoring.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.

Estimation of R0 for the spread of SARS-CoV-2 in Germany from excess mortality

For SARS-CoV-2, R0 calculations in the range of 2-3 dominate the literature, but much higher estimates have also been published. Because capacity for RT-PCR testing increased greatly in the early phase of the Covid-19 pandemic, R0 determinations based on these incidence values are subject to strong bias. We propose to use Covid-19-induced excess mortality to determine R0 regardless of RT-PCR testing capacity. We used data from the Robert Koch Institute (RKI) on the incidence of Covid cases, Covid-related deaths, number of RT-PCR tests performed, and excess mortality calculated from data from the Federal Statistical Office in Germany. We determined R0 using exponential growth estimates with a serial interval of 4.7 days. We used only datasets that were not yet under the influence of policy measures (e.g., lockdowns or school closures). The uncorrected R0 value for the spread of SARS-CoV-2 based on RT-PCR incidence data was 2.56 (95% CI 2.52-2.60) for Covid-19 cases and 2.03 (95% CI 1.96-2.10) for Covid-19-related deaths. However, because the number of RT-PCR tests increased by a growth factor of 1.381 during the same period, these R0 values must be corrected accordingly (R0corrected = R0uncorrected/1.381), yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths. The R0 value based on excess deaths was calculated to be 1.34 (95% CI 1.32-1.37). A sine-function-based adjustment for seasonal effects of 40% corresponds to a maximum value of R0_January = 1.68 and a minimum value of R0_July = 1.01. Our calculations show an R0 that is much lower than previously thought. This relatively low range of R0 fits very well with the observed seasonal pattern of infection across Europe in 2020 and 2021, including the emergence of more contagious escape variants such as delta or omicron. In general, our study shows that excess mortality can be used as a reliable surrogate to determine the R0 in pandemic situations.

Reformulating the susceptible-infectious-removed model in terms of the number of detected cases: well-posedness of the observational model

Compartmental models are popular in the mathematics of epidemiology for their simplicity and wide range of applications. Although they are typically solved as initial value problems for a system of ordinary differential equations, the observed data are typically akin to a boundary value-type problem: we observe some of the dependent variables at given times, but we do not know the initial conditions. In this paper, we reformulate the classical susceptible-infectious-recovered system in terms of the number of detected positive infected cases at different times to yield what we term the observational model. We then prove the existence and uniqueness of a solution to the boundary value problem associated with the observational model and present a numerical algorithm to approximate the solution. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

The value of mass-produced COVID-19 scenarios: A quality evaluation of development processes and scenario content

Hundreds of scenarios were developed across the world in 2020, aimed at generating forward-looking conversations, better understanding for COVID-19 transmission rates, trialling economic outcomes, and stress-testing existing systems in light of the developing pandemic. In response, Cairns & Wright (2020) questioned the value of these mass-produced scenarios created retroactively to existing crises. We address their concerns by evaluating 213 COVID-19 scenarios developed in the first wave of the pandemic. We use two yardsticks as guiding maps against which we plot each scenario's profile and test for values of high-quality process and content. Our analyses reveal various points of high and low qualities, in both process and content. Though most reported processes fell towards lower quality standards, and content largely carried generic applications, the prolific levels of exploratory narratives reflected a mixture of high and low-quality values. Together, our papers develop and reinforce the message that scenario interventions, especially in times of crisis, should reflect more proactive efforts and ensure powerful stakeholders, decision-makers, and affected community members are included in the development of scenarios.

Seasonality of Common Human Coronaviruses, United States, 2014-20211

The 4 common types of human coronaviruses (HCoVs)-2 alpha (HCoV-NL63 and HCoV-229E) and 2 beta (HCoV-HKU1 and HCoV-OC43)-generally cause mild upper respiratory illness. Seasonal patterns and annual variation in predominant types of HCoVs are known, but parameters of expected seasonality have not been defined. We defined seasonality of HCoVs during July 2014-November 2021 in the United States by using a retrospective method applied to National Respiratory and Enteric Virus Surveillance System data. In the 6 HCoV seasons before 2020-21, season onsets occurred October 21-November 12, peaks January 6-February 13, and offsets April 18-June 27; most (>93%) HCoV detection was within the defined seasonal onsets and offsets. The 2020-21 HCoV season onset was 11 weeks later than in prior seasons, probably associated with COVID-19 mitigation efforts. Better definitions of HCoV seasonality can be used for clinical preparedness and for determining expected patterns of emerging coronaviruses.

Modelling of spatial infection spread through heterogeneous population: from lattice to partial differential equation models

We present a simple model for the spread of an infection that incorporates spatial variability in population density. Starting from first-principle considerations, we explore how a novel partial differential equation with state-dependent diffusion can be obtained. This model exhibits higher infection rates in the areas of higher population density-a feature that we argue to be consistent with epidemiological observations. The model also exhibits an infection wave, the speed of which varies with population density. In addition, we demonstrate the possibility that an infection can 'jump' (i.e. tunnel) across areas of low population density towards areas of high population density. We briefly touch upon the data reported for coronavirus spread in the Canadian province of Nova Scotia as a case example with a number of qualitatively similar features as our model. Lastly, we propose a number of generalizations of the model towards future studies.

The discrepancies in the impacts of COVID-19 lockdowns on electricity consumption in China: Is the short-term pain worth it?

The COVID-19 pandemic caused severe economic contraction and paralyzed industrial activity. Despite a growing body of literature on the impacts of COVID-19 mitigation measures, scant evidence currently exists on the impacts of lockdowns on the economic and industrial activities of developing countries. Our study provides an empirical assessment of lockdown measures using 298,354 data points on daily electricity consumption in 396 sub-industries. To infer causal relationships, we employ difference-in-differences models that compare cities with and without lockdown policies and provide quantitative evidence on whether the long-term gain of lockdowns outweighs the short-term loss. The results show that lockdown policies led to a significant short-term drop in electricity consumption of 15.2% relative to the control group. However, the electricity loss under the no-lockdown scenario is 2.6 times larger than that under the strict lockdown scenario within 4 months of the outbreak. Discrepancies in the impacts among industries are identified, and even within the same industry, lockdowns have heterogeneous effects. The impact of lockdowns on small and medium-sized enterprises in developing countries is seriously underestimated, raising concerns about the distributional impact of subsidy measures. This study serves as a crucial reference for the government when facing public health emergencies and shocks to support better policies.

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal

With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz-23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

Reaction of SARS-CoV-2 antibodies with other pathogens, vaccines, and food antigens

It has been shown that SARS-CoV-2 shares homology and cross-reacts with vaccines, other viruses, common bacteria and many human tissues. We were inspired by these findings, firstly, to investigate the reaction of SARS-CoV-2 monoclonal antibody with different pathogens and vaccines, particularly DTaP. Additionally, since our earlier studies have shown immune reactivity by antibodies made against pathogens and autoantigens towards different food antigens, we also studied cross-reaction between SARS-CoV-2 and common foods. For this, we reacted monoclonal and polyclonal antibodies against SARS-CoV-2 spike protein and nucleoprotein with 15 different bacterial and viral antigens and 2 different vaccines, BCG and DTaP, as well as with 180 different food peptides and proteins. The strongest reaction by SARS-CoV-2 antibodies were with DTaP vaccine antigen, E. faecalis, roasted almond, broccoli, soy, cashew, α+β casein and milk, pork, rice endochitinase, pineapple bromelain, and lentil lectin. Because the immune system tends to form immune responses towards the original version of an antigen that it has encountered, this cross-reactivity may have its advantages with regards to immunity against SARS-CoV-2, where the SARS-CoV-2 virus may elicit a “remembered” immune response because of its structural similarity to a pathogen or food antigen to which the immune system was previously exposed. Our findings indicate that cross-reactivity elicited by DTaP vaccines in combination with common herpesviruses, bacteria that are part of our normal flora such as E. faecalis, and foods that we consume on a daily basis should be investigated for possible cross-protection against COVID-19. Additional experiments would be needed to clarify whether or not this cross-protection is due to cross-reactive antibodies or long-term memory T and B cells in the blood.

Paradigm shift in infection control practices in dental clinics in response to COVID-19 among dental professionals in Thailand

Infection control (IC) practice routines depend mainly on knowledge, perception, and awareness of a disease among dental professionals. However, there has been no report on the perception, awareness, and adaptability to the new practice guidelines of Thai dental professionals (dentists, dental nurses, dental assistants, and dental technicians) to the COVID-19 pandemic. This study aims to investigate how dental professionals in Thailand perceive and are aware of COVID-19, and how they have changed their IC practices in response to the pandemic. Online cross-sectional surveys using convenience sampling during September 2021 were sent to Thai dental professionals. The data were analyzed using descriptive statistics and the Chi-square test. Statistical analysis was performed using the Statistical Package for Social Sciences, version 22.0. The tests were two-tailed, with a significance level of p < 0.05 and 95% confidence intervals (CIs). The 1,177 dental professionals who completed the questionnaire were from the public and private sectors. Most respondents obtained their knowledge about COVID-19 from social media (91.8%). 86.7% had adapted to the new IC practice guidelines. The respondents reported that they had modified their work practices in several aspects; changes in administrative control, 1,039 (88.3%); enhancing local source control of dental aerosols, 1,031 (87.6%); heightening sterilization and disinfection procedures, 1,032 (87.7%); and improving the ventilation system, 994 (84.5%). As of October 2021, 1,162 (98.7%) respondents were vaccinated, and 47 (3.99%) had tested positive for COVID-19 compared with 2.30% in the general population. Among infected individuals, 10 (21.3%) were suspected of being infected while working in the dental setting. In conclusion, with an average worry score well over 4.10 out of 5, more than 96% of Thai dental professionals reported seeking updated knowledge and agreed that escalation of IC measures was needed. However, only 86.7% improved their COVID-19 infection prevention practices in 4 aspects and appropriate PPE use. The infection rate in dental professionals was 3.99%, with the highest infection rate in dental assistants. Despite statistical insignificance of infection rate between changed and unchanged group, it cannot be concluded that stricter IC measures are negligible as ones might contract disease from setting other than work.

Pandemic control - do's and don'ts from a control theory perspective

Managing a pandemic is a difficult task. Pandemics are part of the dynamics of nonlinear systems with multiple different interactive features that co-adapt to each other (such as humans, animals, and pathogens). The target of controlling such a nonlinear system is best achieved using the control system theory developed in engineering and applied in systems biology. But is this theory and its principles actually used in controlling the current coronavirus disease-19 pandemic? We review the evidence for applying principles in different aspects of pandemic control related to different goals such as disease eradication, disease containment, and short- or long-term economic loss minimization. Successful policies implement multiple measures in concordance with control theory to achieve a robust response. In contrast, unsuccessful policies have numerous failures in different measures or focus only on a single measure (only testing, vaccines, etc.). Successful approaches rely on predictions instead of reactions to compensate for the costs of time delay, on knowledge-based analysis instead of trial-and-error, to control complex nonlinear systems, and on risk assessment instead of waiting for more evidence. Iran is an example of the effects of delayed response due to waiting for evidence to arrive instead of a proper risk analytical approach. New Zealand, Australia, and China are examples of appropriate application of basic control theoretic principles and focusing on long-term adaptive strategies, updating measures with the evolution of the pandemic.

Immunological memory to common cold coronaviruses assessed longitudinally over a three-year period pre-COVID19 pandemic

The immune memory to common cold coronaviruses (CCCs) influences SARS-CoV-2 infection outcome, and understanding its effect is crucial for pan-coronavirus vaccine development. We performed a longitudinal analysis of pre-COVID19-pandemic samples from 2016-2019 in young adults and assessed CCC-specific CD4+ T cell and antibody responses. Notably, CCC responses were commonly detected with comparable frequencies as with other common antigens and were sustained over time. CCC-specific CD4+ T cell responses were associated with low HLA-DR+CD38+ signals, and their magnitude did not correlate with yearly CCC infection prevalence. Similarly, CCC-specific and spike RBD-specific IgG responses were stable in time. Finally, high CCC-specific CD4+ T cell reactivity, but not antibody titers, was associated with pre-existing SARS-CoV-2 immunity. These results provide a valuable reference for understanding the immune response to endemic coronaviruses and suggest that steady and sustained CCC responses are likely from a stable pool of memory CD4+ T cells due to repeated earlier exposures and possibly occasional reinfections.

Five common misconceptions regarding flattening-the-curve of COVID-19

In the fight against COVID-19 pandemic, the phrase "Flattening the curve" (FTC) has become a rallying cry, popularized by government leaders and journalist in the news and on the social media. FTC is a succinct way of communicating an important public health message that physical distancing, mask-wearing and other public health measures will decrease the peak number of cases and prevent the healthcare system from being overwhelmed. However, while the message of FTC is right in the sense that limiting transmission will reduce the peak number of cases, some visualizations used to illustrate its effect are incorrect from an infectious disease modelling point of view. The misconceptions are misinterpretations of flattened curves, the effect of FTC on the duration of the pandemic, the dynamics of the curve to be flattened, and the overestimation of the importance of FTC.

Applications of cognitive internet of medical things in modern healthcare

The sudden outbreak of the novel coronavirus disease in 2019, known as COVID-19 has impacted the entire globe and has forced governments of various countries to a partial or full lockdown in the fear of the rapid spread of this disease. The major lesson learned from this pandemic is that there is a need to implement a robust system by using non-pharmaceutical interventions for the prevention and control of new contagious viruses. This goal can be achieved using the platform of the Internet of Things (IoT) because of its seamless connectivity and ubiquitous sensing ability. This technology-enabled healthcare sector is helpful to monitor COVID-19 patients properly by adopting an interconnected network. IoT is useful for improving patient satisfaction by reducing the rate of readmission in the hospital. The presented work discusses the applications and technologies of IoT like smart and wearable devices, drones, and robots which are used in healthcare systems to tackle the Coronavirus pandemic This paper focuses on applications of cognitive radio-based IoT for medical applications, which is referred to as "Cognitive Internet of Medical Things" (CIoMT). CIoMT is a disruptive and promising technology for dynamic monitoring, tracking, rapid diagnosis, and control of pandemics and to stop the spread of the virus. This paper explores the role of the CIoMT in the health domain, especially during pandemics, and also discusses the associated challenges and research directions.

CoviDetNet: A new COVID-19 diagnostic system based on deep features of chest x-ray

COVID-19 has emerged as a global pandemic affecting the world, and its adverse effects on society still continue. So far, about 243.57 million people have been diagnosed with COVID-19, of which about 4.94 million have died. In this study, a new model, called COVIDetNet, is proposed for automated COVID-19 detection. A lightweight CNN architecture trained instead of the popular and pretrained convolution neural network (CNN) models such as VGG16, VGG19, AlexNet, ResNet50, ResNet100, and MobileNetV2 from scratch with chest x-ray (CXR) images was designed. A new feature set was created by concatenating the features of all layers of the designed CNN architecture. Then, the most efficient features chosen among the features concatenating with the Relief feature selection algorithm were classified using the support vector machine (SVM) method. The experimental works were carried out on a public COVID-19 CXR database. Experimental results demonstrated 99.24% accuracy, 99.60% specificity, 99.39% sensitivity, 99.04% precision, and an F1 score of 99.21%. Also, in comparison to AlexNet and VGG16 models, the deep feature extraction durations were reduced by approximately 6-fold and 38-fold, respectively. The COVIDetNet model provided a higher accuracy score than state-of-the-art models when compared to multi-class research studies. Overall, the proposed model will be beneficial for specialist medical staff to detect COVID-19 cases, as it provides faster and higher accuracy than existing CXR-based approaches.

Containing novel SARS-CoV-2 variants at source is possible with high-intensity sequencing

Throughout the COVID-19 pandemic, control of transmission has been repeatedly thwarted by the emergence of variants of concern (VOC) and their geographic spread. Key questions remain regarding effective means of minimizing the impact of VOC, in particular the feasibility of containing them at source, in light of global interconnectedness. By analysing a stochastic transmission model of COVID-19, we identify the appropriate monitoring requirements that make containment at source feasible. Specifically, precise risk assessment informed primarily by epidemiological indicators (e.g. accumulated hospitalization or mortality reports), is unlikely prior to VOC escape. Consequently, decision makers will need to make containment decisions without confident severity estimates. In contrast, successfully identifying and containing variants via genomic surveillance is realistic, provided sequence processing and dissemination is prompt.

The enigma of the 1889 Russian flu pandemic: A coronavirus?

The "Russian flu", which raged from 1889 to 1894, is considered as the first pandemic of the industrial era for which statistics have been collected. This planetary event started in Turkestan and hit the Russian Empire, before reaching all European countries, the United States of America, and the whole world. Contemporaries were surprised by its high contagiousness as evidenced by attack rates averaging 60% in urban populations, its rapid spread in successive waves circling the globe in a few months by rail and sea, and the tendency of the disease to relapse. Despite its low case-fatality rate (0.10%-0.28%), it is estimated to have caused one million deaths worldwide. On serological grounds, it is generally accepted that the causative agent of Russian influenza was Myxovirus influenzae, the virus identified for all influenza pandemics since the "Spanish flu" of 1918. In light of the Covid-19 pandemic, which has underscored the extraordinary epidemic potential of coronaviruses, this assumption has recently been questioned. Coronaviruses come from wild reservoirs (bats, rodents, birds, …). They induce respiratory symptoms mimicking influenza, possibly leading to respiratory distress with pneumonia. In addition to the Covid-19 pandemic, recent deadly and limited epidemics, such as SARS in 2002 and MERS in 2012, have occurred. Russian influenza presented as an influenza-like syndrome with clinical peculiarities (multivisceral and neurological involvement, skin rash, early iterative relapses), evoking some particularities of Covid-19. Four other coronaviruses circulating in the human population for decades (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1) have been found to be responsible for 15 to 30% of seasonal colds. All of these viruses are of animal origin. Recently, phylogenetic studies have revealed the genetic proximity between a bovine coronavirus BCoV and the human virus HCoV-OC43, indicating that the latter emerged around 1890, at the time of the Russian flu, when an epizootic was raging among cattle throughout Europe. Could the current human virus be the attenuated remnant that appeared after the Russian flu in 1894? Was there a coronavirus pandemic before Covid-19 ?

Impact of teleconsultation on visual and refractive outcomes in patients undergoing laser refractive surgery during COVID-19

Purpose

To assess the role of remote teleconsultation (TC) follow-up care following a successful and uneventful laser vision correction.

Methods

The study is a retrospective, comparative analysis of patients undergoing laser vision correction at tertiary care eye hospital in Southern India. The patients were divided into two groups. The first group included patients operated on before the coronavirus disease (COVID-19) pandemic and were followed up with physical consultations during their follow-up visit (Group 1). The second group comprised patients operated on during the pandemic and had at least one remote TC during their post-operative follow-up (Group 2).

Results

A total of 1088 eyes of 564 patients and 717 eyes of 372 patients were included in Group 1 and 2, respectively. The mean number of visits for the patients from Group 2 during the COVID period (2.56 +/- 0.74 days) was significantly lesser (P < 0.0001) than that of Group 1 in the pre-COVID period (3.53 +/- 1.07 days). Close to 90% of the eyes achieved an uncorrected distance visual acuity (UDVA) of 20/20 in both groups (P = 0.925). 96.50% of the eyes in Group 1 and 98.18% of the eyes in Group 2 achieved UCVA 20/25 or better (P = 0.049). Eight eyes (0.73%) in Group 1 and one eye (0.14%) in Group 2 reported a loss of 2 or more lines. However, the results were not statistically significant (P = 0.156). None of the groups had any patients who had a sight-threatening complication.

Conclusion

Remote TC following refractive surgery is safe and can be effectively integrated into routine refractive practice to reduce travel to the hospital for a physical consult.

Impacts of timing, length, and intensity of behavioral interventions to COVID-19 dynamics: North Carolina county-level examples

We sought to examine how the impact of revocable behavioral interventions, e.g., shelter-in-place, varies throughout an epidemic, as well as the role that the proportion of susceptible individuals had on an intervention's impact. We estimated the theoretical impacts of start day, length, and intensity of interventions on disease transmission and illustrated them on COVID-19 dynamics in Wake County, North Carolina, to inform how interventions can be most effective. We used a Susceptible, Exposed, Infectious, and Recovered (SEIR) model to estimate epidemic curves with modifications to the disease transmission parameter (β). We designed modifications to simulate events likely to increase transmission (e.g., long weekends, holiday seasons) or behavioral interventions likely to decrease it (e.g., shelter-in-place, masking). We compared the resultant curves' shape, timing, and cumulative case count to baseline and across other modified curves. Interventions led to changes in COVID-19 dynamics, including moving the peak's location, height, and width. The proportion susceptible, at the start day, strongly influenced their impact. Early interventions shifted the curve, while interventions near the peak modified shape and case count. For some scenarios, in which the transmission parameter was decreased, the final cumulative count increased over baseline. We showed that the timing of revocable interventions has a strong impact on their effect. The same intervention applied at different time points, corresponding to different proportions of susceptibility, resulted in qualitatively differential effects. Accurate estimation of the proportion susceptible is critical for understanding an intervention's impact. The findings presented here provide evidence of the importance of estimating the proportion of the population that is susceptible when predicting the impact of behavioral infection control interventions. Greater emphasis should be placed on the estimation of this epidemic component in intervention design and decision-making. Our results are generic and are applicable to other infectious disease epidemics, as well as to future waves of the current COVID-19 epidemic. Developed into a publicly available tool that allows users to modify the parameters to estimate impacts of different interventions, these models could aid in evaluating behavioral intervention options prior to their use and in predicting case increases from specific events.

Telehealth Competencies in Medical Education: New Frontiers in Faculty Development and Learner Assessments

Telehealth visits have become an integral model of healthcare delivery since the COVID-19 pandemic. This rapid expansion of telehealthcare delivery has forced faculty development and trainee education in telehealth to occur simultaneously. In response, academic medical institutions have quickly implemented clinical training to teach digital health skills to providers across the medical education continuum. Yet, learners of all levels must still receive continual assessment and feedback on their skills to align with the telehealth competencies and milestones set forth by the Association of American Medical Colleges (AAMC) and the Accreditation Council for Graduate Medical Education (ACGME). This paper discusses key educational needs and emerging areas for faculty development in telehealth teaching and assessment of telehealth competencies. It proposes strategies for the successful integration of the AAMC telehealth competencies and ACGME milestones into medical education, including skills in communication, data gathering, and patient safety with appropriate telehealth use. Direct observation tools in the paper offer educators novel instruments to assess telehealth competencies in medical students, residents, and peer faculty. The integration of AAMC and ACGME telehealth competencies and the new assessment tools in this paper provide a unique perspective to advance clinical practice and teaching skills in telehealthcare delivery.

Nationally representative social contact patterns among U.S. adults, August 2020-April 2021

The response to the COVID-19 pandemic in the U.S prompted abrupt and dramatic changes to social contact patterns. Monitoring changing social behavior is essential to provide reliable input data for mechanistic models of infectious disease, which have been increasingly used to support public health policy to mitigate the impacts of the pandemic. While some studies have reported on changing contact patterns throughout the pandemic, few have reported differences in contact patterns among key demographic groups and none have reported nationally representative estimates. We conducted a national probability survey of US households and collected information on social contact patterns during two time periods: August-December 2020 (before widespread vaccine availability) and March-April 2021 (during national vaccine rollout). Overall, contact rates in Spring 2021 were similar to those in Fall 2020, with most contacts reported at work. Persons identifying as non-White, non-Black, non-Asian, and non-Hispanic reported high numbers of contacts relative to other racial and ethnic groups. Contact rates were highest in those reporting occupations in retail, hospitality and food service, and transportation. Those testing positive for SARS-CoV-2 antibodies reported a higher number of daily contacts than those who were seronegative. Our findings provide evidence for differences in social behavior among demographic groups, highlighting the profound disparities that have become the hallmark of the COVID-19 pandemic.

Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

SARS-CoV-2 infection prevalence and associated factors: a serial population-based study in Espírito Santo, Brazil, May to June 2020

OBJECTIVE

To analyze SARS-CoV-2 seroprevalence and association of sociodemographic and clinical aspects in the state of Espírito Santo, Brazil.

METHODS

This was a serial cross-sectional study carried out in four phases, using households as the unit of analysis, from May to June 2020. Eleven municipalities were surveyed, with a sample of 4,500 households in each phase.

RESULTS

Prevalence ranged from 2.1% (95%CI 1.7;2.5) on May 10 (first phase) to 9.6% (95%CI 8.8;10.4) on June 21 (fourth phase). In the Greater Vitória Metropolitan Region, the prevalence were 2.7% (95%CI 2.2;3.3) in the first phase, and 11.5% (95%CI 10.5;12.6) in the fourth phase; in the interior region of the state, prevalence ranged from 0.4% (95%CI 0.1;0.9) to 4.4% (95%CI 3.2;5.5) between the two phases.

CONCLUSION

The increase in SARS-CoV-2 seroprevalence found in the fourth phase highlighted the high transmission of the virus, information that can support management of the pandemic.

E-Adoption of Emerging Technology in the Health Sector During COVID-19

In the 21st century, COVID-19 made a profound impact on the world. This pandemic had a detrimental impact worldwide, causing massive economic damage and enormous mortality. Emerging technologies play an essential role in every sector, and the health sector is not exceptional in this line. This paper examines the health sector before, during, and after the COVID-19 era by taking a view of emerging technologies. Artificial intelligence, cloud computing, IoT, learning paradigms, blockchain, and others are emerging technologies. E-adoption of these technologies becomes important to face critical situations during COVID-19. Using these technologies, it is possible to care for and monitor remote patients by keeping medical record management. This study includes a brief examination of similar work. In addition, the impact of e-adoption on health sector is discussed in this research. Furthermore, this study suggested a paradigm for comprehending the application of developing technologies to manage and overcome the health sector's burden. Finally, research is concluded with remarks on the future.

Understanding the Potential Impact of Different Drug Properties on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission and Disease Burden: A Modelling Analysis

BACKGROUND

The public health impact of the coronavirus disease 2019 (COVID-19) pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear.

METHODS

Using a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care.

RESULTS

The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R = 1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics.

CONCLUSIONS

Advances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

Effective multiscale deep learning model for COVID19 segmentation tasks: A further step towards helping radiologist

Infection by the SARS-CoV-2 leading to COVID-19 disease is still rising and techniques to either diagnose or evaluate the disease are still thoroughly investigated. The use of CT as a complementary tool to other biological tests is still under scrutiny as the CT scans are prone to many false positives as other lung diseases display similar characteristics on CT scans. However, fully investigating CT images is of tremendous interest to better understand the disease progression and therefore thousands of scans need to be segmented by radiologists to study infected areas. Over the last year, many deep learning models for segmenting CT-lungs were developed. Unfortunately, the lack of large and shared annotated multicentric datasets led to models that were either under-tested (small dataset) or not properly compared (own metrics, none shared dataset), often leading to poor generalization performance. To address, these issues, we developed a model that uses a multiscale and multilevel feature extraction strategy for COVID19 segmentation and extensively validated it on several datasets to assess its generalization capability for other segmentation tasks on similar organs. The proposed model uses a novel encoder and decoder with a proposed kernel-based atrous spatial pyramid pooling module that is used at the bottom of the model to extract small features with a multistage skip connection concatenation approach. The results proved that our proposed model could be applied on a small-scale dataset and still produce generalizable performances on other segmentation tasks. The proposed model produced an efficient Dice score of 90% on a 100 cases dataset, 95% on the NSCLC dataset, 88.49% on the COVID19 dataset, and 97.33 on the StructSeg 2019 dataset as compared to existing state-of-the-art models. The proposed solution could be used for COVID19 segmentation in clinic applications. The source code is publicly available at https://github.com/RespectKnowledge/Mutiscale-based-Covid-_segmentation-usingDeep-Learning-models.

Population disruption: estimating changes in population distribution of the UK during the COVID-19 pandemic

Mobility data have demonstrated major changes in human movement patterns in response to COVID-19 and associated interventions in many countries. This can involve sub-national redistribution, short-term relocations as well as international migration. In this paper, we combine detailed location data from Facebook measuring the location of approximately 6 million daily active Facebook users in 5km2 tiles in the UK with census-derived population estimates to measure population mobility and redistribution. We provide time-varying population estimates and assess spatial population changes with respect to population density and four key reference dates in 2020 (First lockdown, End of term, Beginning of term, Christmas). We also show how population estimates derived from the distribution of Facebook users vary compared to mid-2020 small area population estimates by the UK national statistics agencies. We estimate that between March 2020 and March 2021, the total population of the UK declined and we identify important spatial variations in this population change, showing that low-density areas have experienced lower population decreases than urban areas. We estimate that, for the top 10% highest population tiles, the population has decreased by 6.6%. Further, we provide evidence that geographic redistributions of population within the UK coincide with dates of non-pharmaceutical interventions including lockdowns and movement restrictions, as well as seasonal patterns of migration around holiday dates. The methods used in this study reveal significant changes in population distribution at high spatial and temporal resolutions that have not previously been quantified by available demographic surveys in the UK. We found early indicators of potential longer-term changes in the population distribution of the UK although it is not clear if these changes may persist after the COVID-19 pandemic.

Structural Study of SARS-CoV-2 Antibodies Identifies a Broad-Spectrum Antibody That Neutralizes the Omicron Variant by Disassembling the Spike Trimer

The continuous emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses new challenges in the fight against the coronavirus disease 2019 (COVID-19) pandemic. The newly emerging Omicron strain caused serious immune escape and raised unprecedented concern all over the world. The development of an antibody targeting a conserved and universal epitope is urgently needed. A subset of neutralizing antibodies (NAbs) against COVID-19 from convalescent patients were isolated in our previous study. In this study, we investigated the accommodation of these NAbs to SARS-CoV-2 variants of concern (VOCs), revealing that IgG 553-49 neutralizes pseudovirus of the SARS-CoV-2 Omicron variant. In addition, we determined the cryo-electron microscopy (cryo-EM) structure of the SARS-CoV-2 spike (S) protein complexed with three monoclonal antibodies targeting different epitopes, including 553-49, 553-15, and 553-60. Notably, 553-49 targets a novel conserved epitope and neutralizes the virus by disassembling S trimers. IgG 553-15, an antibody that neutralizes all of the VOCs except Omicron, cross-links two S trimers to form a trimer dimer, demonstrating that 553-15 neutralizes the virus by steric hindrance and virion aggregation. These findings suggest the potential to develop 553-49 and other antibodies targeting this highly conserved epitope as promising therapeutic reagents for COVID-19.

IMPORTANCE The emergence of the Omicron strain of SARS-CoV-2 caused higher immune escape, raising unprecedented concerns about the effectiveness of antibody therapies and vaccines. In this study, we identified a SARS-CoV-2 neutralizing antibody, 553-49, which neutralizes all variants by targeting a completely conserved novel epitope. In addition, we revealed that IgG 553-15 neutralizes SARS-CoV-2 by cross-linking virions and that 553-60 functions by blocking receptor binding. Comparison of different receptor binding domain (RBD) epitopes revealed that the 553-49 epitope is hidden in the S trimer and keeps a high degree of conservation during SARS-CoV-2 evolution, making 553-49 a promising therapeutic reagent against the emerging Omicron and future variants of SARS-CoV-2.

Environmental factors and mobility predict COVID-19 seasonality in the Netherlands

BACKGROUND

We recently showed that seasonal patterns of COVID-19 incidence and Influenza-Like Illnesses incidence are highly similar, in a country in the temperate climate zone, such as the Netherlands. We hypothesize that in The Netherlands the same environmental factors and mobility trends that are associated with the seasonality of flu-like illnesses are predictors of COVID-19 seasonality as well.

METHODS

We used meteorological, pollen/hay fever and mobility data from the Netherlands. For the reproduction number of COVID-19 (R_t), we used daily estimates from the Dutch State Institute for Public Health. For all datasets, we selected the overlapping period of COVID-19 and the first allergy season: from February 17, 2020 till September 21, 2020 (n = 218). Backward stepwise multiple linear regression was used to develop an environmental prediction model of the R_t of COVID-19. Next, we studied whether adding mobility trends to an environmental model improved the predictive power.

RESULTS

Through stepwise backward multiple linear regression four highly significant (p < 0.01) predictive factors are selected in our combined model: temperature, solar radiation, hay fever incidence, and mobility to indoor recreation locations. Our combined model explains 87.5% of the variance of R_t of COVID-19 and has a good and highly significant fit: F(4, 213) = 374.2, p < 0.00001. This model had a better overall predictive performance than a solely environmental model, which explains 77.3% of the variance of R_t (F(4, 213) = 181.3, p < 0.00001).

CONCLUSIONS

We conclude that the combined mobility and environmental model can adequately predict the seasonality of COVID-19 in a country with a temperate climate like the Netherlands. In this model higher solar radiation, higher temperature and hay fever are related to lower COVID-19 reproduction, and higher mobility to indoor recreation locations is related to an increased COVID-19 spread.

Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions

Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%-53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.

Safety and immunogenicity of intramuscular, single-dose V590 (rVSV-SARS-CoV-2 Vaccine) in healthy adults: Results from a phase 1 randomised, double-blind, placebo-controlled, dose-ranging trial

BACKGROUND

Vaccines against COVID-19 are needed to overcome challenges associated with mitigating the global pandemic. We report the safety and immunogenicity of V590, a live recombinant vesicular stomatitis virus-based COVID-19 vaccine candidate.

METHODS

In this placebo-controlled, double-blind, three-part phase 1 study, healthy adults were randomised to receive a single intramuscular dose of vaccine or placebo. In Part 1, younger (18-54 years) and, in Part 2, older (≥55 years) adults seronegative for SARS-CoV-2 nucleocapsid received one of four V590 dose levels (5.00 × 105; 2.40 × 106; 1.15 × 107; or 5.55 × 107 plaque-forming units [pfu]) or placebo. In Part 3, a single V590 dose level (5.55 × 10⁷ pfu) or placebo was administered to younger SARS-CoV-2 seropositive adults. Primary endpoints included adverse events (AEs) and for Parts 1 and 2 anti-SARS-CoV-2 serum neutralising antibody responses measured by 50% plaque reduction neutralisation (PRNT50) assay at Day 28. Registration NCT04569786 [P001-02].

FINDINGS

232 participants were randomised and 219 completed the study. In seronegative participants, anti-SARS-CoV-2 spike-specific antibody responses to V590 were low and comparable to placebo across the lower dose levels. At the highest dose level (5.55 × 107 pfu), anti-SARS-CoV-2 spike-specific PRNT50 was 2.3-fold higher than placebo. The most frequently reported AEs were injection-site pain (38.4%), headache (15.1%) and fatigue (13.4%).

INTERPRETATION

V590 was generally well-tolerated. However, Day 28 anti-SARS-Cov-2 spike-specific antibody responses in seronegative participants following a single intramuscular administration of V590 were not sufficient to warrant continued development.

FUNDING

The study was funded by Merck Sharp & Dohme LLC., a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions

Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%-53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.