Networks of SARS-CoV-2 transmission

Molecular evolutionary characteristics of severe acute respiratory syndrome coronavirus 2 and the relatedness of epidemiological and socio-environmental factors

After the first outbreak, SARS-CoV-2 infection continues to occur due to the emergence of new variants. There is limited information available on the comparative evaluation of evolutionary characteristics of SARS-CoV-2 among different countries over time, and its relatedness to epidemiological and socio-environmental factors within those countries. We assessed comparative Bayesian evolutionary characteristics for SARS-CoV-2 in eight countries from 2020 to 2022 using BEAST version 2.6.7. Additionally, the relatedness between virus evolution factors and both epidemiological and socio-environmental factors was analyzed using Pearson's correlation coefficient. The estimated substitution rates in the gene encoding S protein of SARS-CoV-2 exhibited a continuous increase from 2020 to 2022 and were divided into two distinct groups in 2022 (p value < 0.05). Effective population size (Ne) generally showed decreased patterns by time. Notably, the change rates of the substitution rates were negatively correlated with the cumulative vaccination rates in 2021. A strict and rapid vaccination policy in the United Arab Emirates dramatically reduced the evolution of the virus, compared to other countries. Also, the average yearly temperature in countries were negatively correlated with the substitution rates. The changes of six epitopes in SARS-CoV-2 were related to various socio-environmental factors. We figured out comparative virus evolutionary traits and the association of epidemiological and socio-environmental factors especially cumulative vaccination rates and average temperature.

Relationship of close contact settings with transmission and infection during the SARS-CoV-2 Omicron BA.2 epidemic in Shanghai

INTRODUCTION

We analysed case-contact clusters during the Omicron BA.2 epidemic in Shanghai to assess the risk of infection of contacts in different settings and to evaluate the effect of demographic factors on the association of infectivity and susceptibility to the Omicron variant.

METHODS

Data on the settings and frequency of contact, demographic characteristics and comorbidities of index cases, contacts and secondary cases were analysed. Independent effect of multiple variables on the risk for transmission and infection was evaluated using generalised estimating equations.

RESULTS

From 1 March to 1 June 2022, we identified 450 770 close contacts of 90 885 index cases. The risk for infection was greater for contacts in farmers' markets (fixed locations where farmers gather to sell products, adjusted OR (aOR): 3.62; 95% CI 2.87 to 4.55) and households (aOR: 2.68; 95% CI 2.15 to 3.35). Children (0-4 years) and elderly adults (60 years and above) had higher risk for infection and transmission. During the course of the epidemic, the risk for infection and transmission in different age groups initially increased, and then decreased on about 21 April (17th day of citywide home quarantine). Compared with medical workers (reference, aOR: 1.00), unemployed contacts (aOR: 1.77; 95% CI 1.53 to 2.04) and preschoolers (aOR: 1.61; 95% CI 1.26 to 2.05) had the highest risk for infection; delivery workers (aOR: 1.90, 95% CI 1.51 to 2.40) and public service workers (aOR: 1.85; 95% CI 1.64 to 2.10) had the highest risk for transmission. Contacts who had comorbidities (aOR: 1.10; 95% CI 1.09 to 1.12) had a higher risk for infection, particularly those with lung diseases or immune deficiency.

CONCLUSION

Farmers' markets and households were the main setting for transmission of Omicron. Children, the elderly, delivery workers and public service workers had the highest risk for transmission and infection. These findings should be considered when implementing targeted interventions.

Non-pharmaceutical interventions for COVID-19: a systematic review on environmental control measures

The purpose of this review was to identify the effectiveness of environmental control (EC) non-pharmaceutical interventions (NPIs) in reducing transmission of SARS-CoV-2 through conducting a systematic review. EC NPIs considered in this review are room ventilation, air filtration/cleaning, room occupancy, surface disinfection, barrier devices, [Formula: see text] monitoring and one-way-systems. Systematic searches of databases from Web of Science, Medline, EMBASE, preprint servers MedRxiv and BioRxiv were conducted in order to identify studies reported between 1 January 2020 and 1 December 2022. All articles reporting on the effectiveness of ventilation, air filtration/cleaning, room occupancy, surface disinfection, barrier devices, [Formula: see text] monitoring and one-way systems in reducing transmission of SARS-CoV-2 were retrieved and screened. In total, 13 971 articles were identified for screening. The initial title and abstract screening identified 1328 articles for full text review. Overall, 19 references provided evidence for the effectiveness of NPIs: 12 reported on ventilation, 4 on air cleaning devices, 5 on surface disinfection, 6 on room occupancy and 1 on screens/barriers. No studies were found that considered the effectiveness of [Formula: see text] monitoring or the implementation of one-way systems. Many of these studies were assessed to have critical risk of bias in at least one domain, largely due to confounding factors that could have affected the measured outcomes. As a result, there is low confidence in the findings. Evidence suggests that EC NPIs of ventilation, air cleaning devices and reduction in room-occupancy may have a role in reducing transmission in certain settings. However, the evidence was usually of low or very low quality and certainty, and hence the level of confidence ascribed to this conclusion is low. Based on the evidence found, it was not possible to draw any specific conclusions regarding the effectiveness of surface disinfection and the use of barrier devices. From these results, we further conclude that community agreed standards for well-designed epidemiological studies with low risk of bias are needed. Implementation of such standards would enable more confident assessment in the future of the effectiveness of EC NPIs in reducing transmission of SARS-CoV-2 and other pathogens in real-world settings. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9 to 62.8 ; the average shortest path length decreased from 1.53 to 1.14 ; the average betweenness reduced from 0.65 to 0.11 ; the average cluster size dropped from 4.05 to 2.72 ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

Quantifying the impact of SARS-CoV-2 temporal vaccination trends and disparities on disease control

SARS-CoV-2 vaccines have been distributed at unprecedented speed. Still, little is known about temporal vaccination trends, their association with socioeconomic inequality, and their consequences for disease control. Using data from 161 countries/territories and 58 states, we examined vaccination rates across high and low socioeconomic status (SES), showing that disparities in coverage exist at national and subnational levels. We also identified two distinct vaccination trends: a rapid initial rollout, quickly reaching a plateau, or sigmoidal and slow to begin. Informed by these patterns, we implemented an SES-stratified mechanistic model, finding profound differences in mortality and incidence across these two vaccination types. Timing of initial rollout affects disease outcomes more substantially than final coverage or degree of SES disparity. Unexpectedly, timing is not associated with wealth inequality or GDP per capita. While socioeconomic disparity should be addressed, accelerating initial rollout for all over focusing on increasing coverage is an accessible intervention that could minimize the burden of disease across socioeconomic groups.

Neutralization against SARS-CoV-2 Delta/Omicron variants and B cell response after inactivated vaccination among COVID-19 convalescents

Emerging SARS-CoV-2 variants have made COVID-19 convalescents susceptible to re-infection and have raised concern about the efficacy of inactivated vaccination in neutralization against emerging variants and antigen-specific B cell response. To this end, a study on a long-term cohort of 208 participants who have recovered from COVID-19 was conducted, and the participants were followed up at 3.3 (Visit 1), 9.2 (Visit 2), and 18.5 (Visit 3) months after SARS-CoV-2 infection. They were classified into three groups (no-vaccination (n = 54), one-dose (n = 62), and two-dose (n = 92) groups) on the basis of the administration of inactivated vaccination. The neutralizing antibody (NAb) titers against the wild-type virus continued to decrease in the no-vaccination group, but they rose significantly in the one-dose and two-dose groups, with the highest NAb titers being observed in the two-dose group at Visit 3. The NAb titers against the Delta variant for the no-vaccination, one-dose, and two-dose groups decreased by 3.3, 1.9, and 2.3 folds relative to the wild-type virus, respectively, and those against the Omicron variant decreased by 7.0, 4.0, and 3.8 folds, respectively. Similarly, the responses of SARS-CoV-2 RBD-specific B cells and memory B cells were boosted by the second vaccine dose. Results showed that the convalescents benefited from the administration of the inactivated vaccine (one or two doses), which enhanced neutralization against highly mutated SARS-CoV-2 variants and memory B cell responses. Two doses of inactivated vaccine among COVID-19 convalescents are therefore recommended for the prevention of the COVID-19 pandemic, and vaccination guidelines and policies need to be updated.

Differential Risk of SARS-CoV-2 Infection by Occupation: Evidence from the Virus Watch prospective cohort study in England and Wales

BACKGROUND

Workers across different occupations vary in their risk of SARS-CoV-2 infection, but the direct contribution of occupation to this relationship is unclear. This study aimed to investigate how infection risk differed across occupational groups in England and Wales up to April 2022, after adjustment for potential confounding and stratification by pandemic phase.

METHODS

Data from 15,190 employed/self-employed participants in the Virus Watch prospective cohort study were used to generate risk ratios for virologically- or serologically-confirmed SARS-CoV-2 infection using robust Poisson regression, adjusting for socio-demographic and health-related factors and non-work public activities. We calculated attributable fractions (AF) amongst the exposed for belonging to each occupational group based on adjusted risk ratios (aRR).

RESULTS

Increased risk was seen in nurses (aRR = 1.44, 1.25-1.65; AF = 30%, 20-39%), doctors (aRR = 1.33, 1.08-1.65; AF = 25%, 7-39%), carers (1.45, 1.19-1.76; AF = 31%, 16-43%), primary school teachers (aRR = 1.67, 1.42- 1.96; AF = 40%, 30-49%), secondary school teachers (aRR = 1.48, 1.26-1.72; AF = 32%, 21-42%), and teaching support occupations (aRR = 1.42, 1.23-1.64; AF = 29%, 18-39%) compared to office-based professional occupations. Differential risk was apparent in the earlier phases (Feb 2020-May 2021) and attenuated later (June-October 2021) for most groups, although teachers and teaching support workers demonstrated persistently elevated risk across waves.

CONCLUSIONS

Occupational differences in SARS-CoV-2 infection risk vary over time and are robust to adjustment for socio-demographic, health-related, and non-workplace activity-related potential confounders. Direct investigation into workplace factors underlying elevated risk and how these change over time is needed to inform occupational health interventions.

Reducing societal impacts of SARS-CoV-2 interventions through subnational implementation

To curb the initial spread of SARS-CoV-2, many countries relied on nation-wide implementation of non-pharmaceutical intervention measures, resulting in substantial socio-economic impacts. Potentially, subnational implementations might have had less of a societal impact, but comparable epidemiological impact. Here, using the first COVID-19 wave in the Netherlands as a case in point, we address this issue by developing a high-resolution analysis framework that uses a demographically stratified population and a spatially explicit, dynamic, individual contact-pattern based epidemiology, calibrated to hospital admissions data and mobility trends extracted from mobile phone signals and Google. We demonstrate how a subnational approach could achieve similar level of epidemiological control in terms of hospital admissions, while some parts of the country could stay open for a longer period. Our framework is exportable to other countries and settings, and may be used to develop policies on subnational approach as a better strategic choice for controlling future epidemics.

Cultural differences, intergenerational contacts, and the spread of Covid-19: Evidence from Swiss language regions

The Covid-19 pandemic displayed large variations between and within countries in the speed of contagion and in observed fatality rates. This work sheds light on the role of social ties in old age, exploiting the high cultural variation between German-speaking and Latin- (French- and Italian-) speaking regions in Switzerland. We show that older adults in Latin-speaking regions exhibit a larger social network and more intergenerational contacts than their German-speaking counterparts. These differences are consistent with the heterogeneous incidence of the disease across language regions. Even controlling for several determinants of the contagion, we find large differences in the incidence of Covid-19 among older adults, in both the first and second waves of the pandemic. These findings also hold when exploiting language variations within the three Swiss bilingual cantons. We rule out the possibility that our results are driven by differences in canton-specific policies or in citizens' compliance with containment measures.

The impacts of vaccination status and host factors during early infection on SARS-CoV-2 persistence:a retrospective single-center cohort study

BACKGROUND

Viral persistence is a crucial factor that influences the transmissibility of SARS-CoV-2. However, the impacts of vaccination and physiological variables on viral persistence have not been adequately clarified.

METHODS

We collected the clinical records of 377 COVID-19 patients, which contained unvaccinated patients and patients received two doses of an inactivated vaccine or an mRNA vaccine. The impacts of vaccination on disease severity and viral persistence and the correlations between 49 laboratory variables and viral persistence were analyzed separately. Finally, we established a multivariate regression model to predict the persistence of viral RNA.

RESULTS

Both inactivated and mRNA vaccines significantly reduced the rate of moderate cases, while the vaccine related shortening of viral RNA persistence was only observed in moderate patients. Correlation analysis showed that 10 significant laboratory variables were shared by the unvaccinated mild patients and mild patients inoculated with an inactivated vaccine, but not by the mild patients inoculated with an mRNA vaccine. A multivariate regression model established based on the variables correlating with viral persistence in unvaccinated mild patients could predict the persistence of viral RNA for all patients except three moderate patients inoculated with an mRNA vaccine.

CONCLUSION

Vaccination contributed limitedly to the clearance of viral RNA in COVID-19 patients. While, laboratory variables in early infection could predict the persistence of viral RNA.

Effects of infection fatality ratio and social contact matrices on vaccine prioritization strategies

Effective strategies of vaccine prioritization are essential to mitigate the impacts of severe infectious diseases. We investigate the role of infection fatality ratio (IFR) and social contact matrices on vaccination prioritization using a compartmental epidemic model fueled by real-world data of different diseases and countries. Our study confirms that massive and early vaccination is extremely effective to reduce the disease fatality if the contagion is mitigated, but the effectiveness is increasingly reduced as vaccination beginning delays in an uncontrolled epidemiological scenario. The optimal and least effective prioritization strategies depend non-linearly on epidemiological variables. Regions of the epidemiological parameter space, in which prioritizing the most vulnerable population is more effective than the most contagious individuals, depend strongly on the IFR age profile being, for example, substantially broader for COVID-19 in comparison with seasonal influenza. Demographics and social contact matrices deform the phase diagrams but do not alter their qualitative shapes.

A population framework for predicting the proportion of people infected by the far-field airborne transmission of SARS-CoV-2 indoors

The number of occupants in a space influences the risk of far-field airborne transmission of SARS-CoV-2 because the likelihood of having infectious and susceptible people both correlate with the number of occupants. This paper explores the relationship between occupancy and the probability of infection, and how this affects an individual person and a population of people. Mass-balance and dose-response models determine far-field transmission risks for an individual person and a population of people after sub-dividing a large reference space into 10 identical comparator spaces. For a single infected person, the dose received by an individual person in the comparator space is 10 times higher because the equivalent ventilation rate per infected person is lower when the per capita ventilation rate is preserved. However, accounting for population dispersion, such as the community prevalence of the virus, the probability of an infected person being present and uncertainty in their viral load, shows the transmission probability increases with occupancy and the reference space has a higher transmission risk. Also, far-field transmission is likely to be a rare event that requires a high emission rate, and there are a set of Goldilocks conditions that are just right when equivalent ventilation is effective at mitigating against transmission. These conditions depend on the viral load, because when they are very high or low, equivalent ventilation has little effect on transmission risk. Nevertheless, resilient buildings should deliver the equivalent ventilation rate required by standards as minimum.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

BACKGROUND

Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.

METHODS

We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.

FINDINGS

The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]).

INTERPRETATION

Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.

FUNDING

Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

SARS-CoV-2 Transmission and Prevention in the Era of the Delta Variant

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) delta variant transmits much more rapidly than prior SARS-CoV-2 viruses. The primary mode of transmission is via short range aerosols that are emitted from the respiratory tract of an index case. There is marked heterogeneity in the spread of this virus, with 10% to 20% of index cases contributing to 80% of secondary cases, while most index cases have no subsequent transmissions. Vaccination, ventilation, masking, eye protection, and rapid case identification with contact tracing and isolation can all decrease the transmission of this virus.

Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold

Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being crucial to protect vulnerable individuals from severe outcomes as the virus becomes endemic.

Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out

BACKGROUND

Many studies have examined the effectiveness of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 transmission worldwide. However, less attention has been devoted to understanding the limits of NPIs across the course of the pandemic and along a continuum of their stringency. In this study, we explore the relationship between the growth of SARS-CoV-2 cases and an NPI stringency index across Canada before the accelerated vaccine roll-out.

METHODS

We conducted an ecological time-series study of daily SARS-CoV-2 case growth in Canada from February 2020 to February 2021. Our outcome was a back-projected version of the daily growth ratio in a stringency period (i.e., a 10-point range of the stringency index) relative to the last day of the previous period. We examined the trends in case growth using a linear mixed-effects model accounting for stringency period, province, and mobility in public domains.

RESULTS

Case growth declined rapidly by 20-60% and plateaued within the first month of the first wave, irrespective of the starting values of the stringency index. When stringency periods increased, changes in case growth were not immediate and were faster in the first wave than in the second. In the first wave, the largest decreasing trends from our mixed effects model occurred in both early and late stringency periods, depending on the province, at a geometric mean index value of 30⋅1 out of 100. When compared with the first wave, the stringency periods in the second wave possessed little association with case growth.

CONCLUSIONS

The minimal association in the first wave, and the lack thereof in the second, is compatible with the hypothesis that NPIs do not, per se, lead to a decline in case growth. Instead, the correlations we observed might be better explained by a combination of underlying behaviors of the populations in each province and the natural dynamics of SARS-CoV-2. Although there exist alternative explanations for the equivocal relationship between NPIs and case growth, the onus of providing evidence shifts to demonstrating how NPIs can consistently have flat association, despite incrementally high stringency.

Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold

Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being critical to protect vulnerable individuals from severe outcomes as the virus becomes endemic.

Structure and Mutations of SARS-CoV-2 Spike Protein: A Focused Overview

The spike protein (S-protein) of SARS-CoV-2, the protein that enables the virus to infect human cells, is the basis for many vaccines and a hotspot of concerning virus evolution. Here, we discuss the outstanding progress in structural characterization of the S-protein and how these structures facilitate analysis of virus function and evolution. We emphasize the differences in reported structures and that analysis of structure-function relationships is sensitive to the structure used. We show that the average residue solvent exposure in nearly complete structures is a good descriptor of open vs closed conformation states. Because of structural heterogeneity of functionally important surface-exposed residues, we recommend using averages of a group of high-quality protein structures rather than a single structure before reaching conclusions on specific structure-function relationships. To illustrate these points, we analyze some significant chemical tendencies of prominent S-protein mutations in the context of the available structures. In the discussion of new variants, we emphasize the selectivity of binding to ACE2 vs prominent antibodies rather than simply the antibody escape or ACE2 affinity separately. We note that larger chemical changes, in particular increased electrostatic charge or side-chain volume of exposed surface residues, are recurring in mutations of concern, plausibly related to adaptation to the negative surface potential of human ACE2. We also find indications that the fixated mutations of the S-protein in the main variants are less destabilizing than would be expected on average, possibly pointing toward a selection pressure on the S-protein. The richness of available structures for all of these situations provides an enormously valuable basis for future research into these structure-function relationships.

Graphene oxide-graphene Van der Waals heterostructure transistor biosensor for SARS-CoV-2 protein detection

The current outbreaking of the coronavirus SARS-CoV-2 pandemic threatens global health and has caused serious concern. Currently there is no specific drug against SARS-CoV-2, therefore, a fast and accurate diagnosis method is an urgent need for the diagnosis, timely treatment and infection control of COVID-19 pandemic. In this work, we developed a field effect transistor (FET) biosensor based on graphene oxide-graphene (GO/Gr) van der Waals heterostructure for selective and ultrasensitive SARS-CoV-2 proteins detection. The GO/Gr van der Waals heterostructure was in-situ formed in the microfluidic channel through π-π stacking. The developed biosensor is capable of SARS-CoV-2 proteins detection within 20 min in the large dynamic range from 10 fg/mL to 100 pg/mL with the limit of detection of as low as ∼8 fg/mL, which shows ∼3 × sensitivity enhancement compared with Gr-FET biosensor. The performance enhancement mechanism was studied based on the transistor-based biosensing theory and experimental results, which is mainly attributed to the enhanced SARS-CoV-2 capture antibody immobilization density due to the introduction of the GO layer on the graphene surface. The spiked SARS-CoV-2 protein samples in throat swab buffer solution were tested to confirm the practical application of the biosensor for SARS-CoV-2 proteins detection. The results indicated that the developed GO/Gr van der Waals heterostructure FET biosensor has strong selectivity and high sensitivity, providing a potential method for SARS-CoV-2 fast and accurate detection.

U.S. frontline workers and COVID-19 inequities

We overcome a lack of frontline worker status information in most COVID-19 data repositories to document the extent to which occupation has contributed to COVID-19 disparities in the United States. Using national data from over a million U.S. respondents to a Facebook-Carnegie Mellon University survey administered from September 2020 to March 2021, we estimated the likelihoods of frontline workers, compared to non-frontline workers, 1) to ever test positive for SARs-Cov-2 and 2) to test positive for SARs-Cov-2 within the past two weeks. Net of other covariates including education level, county-level political environment, and rural residence, both healthcare and non-healthcare frontline workers had higher odds of having ever tested positive for SARs-Cov-2 across the study time period. Similarly, non-healthcare frontline workers were more likely to test positive in the previous 14 days. Conversely, healthcare frontline workers were less likely to have recently tested positive. Our findings suggest that occupational exposure has played an independent role in the uneven spread of the virus. In particular, non-healthcare frontline workers have experienced sustained higher risk of testing positive for SARs-Cov-2 compared to non-frontline workers. Alongside more worker protections, future COVID-19 and other highly infectious disease response strategies must be augmented by a more robust recognition of the role that structural factors, such as the highly stratified U.S. occupational landscape, have played in the uneven toll of the COVID-19 pandemic.

The SARS-CoV-2 Pandemic in High Income Countries Such as Canada: A Better Way Forward Without Lockdowns

The SARS-CoV-2 pandemic has caused tragic morbidity and mortality. In attempt to reduce this morbidity and mortality, most countries implemented population-wide lockdowns. Here we show that the lockdowns were based on several flawed assumptions, including "no one is protected until everyone is protected," "lockdowns are highly effective to reduce transmission," "lockdowns have a favorable cost-benefit balance," and "lockdowns are the only effective option." Focusing on the latter, we discuss that Emergency Management principles provide a better way forward to manage the public emergency of the pandemic. Specifically, there are three priorities including the following: first, protect those most at risk by separating them from the threat (mitigation); second, ensure critical infrastructure is ready for people who get sick (preparation and response); and third, shift the response from fear to confidence (recovery). We argue that, based on Emergency Management principles, the age-dependent risk from SARS-CoV-2, the minimal (at best) efficacy of lockdowns, and the terrible cost-benefit trade-offs of lockdowns, we need to reset the pandemic response. We can manage risk and save more lives from both COVID-19 and lockdowns, thus achieving far better outcomes in both the short- and long-term.

Ignoring spatial heterogeneity in drivers of SARS-CoV-2 transmission in the US will impede sustained elimination

To dissect the transmission dynamics of SARS-CoV-2 in the United States, we integrate parallel streams of high-resolution data on contact, mobility, seasonality, vaccination and seroprevalence within a metapopulation network. We find the COVID-19 pandemic in the US is characterized by a geographically localized mosaic of transmission along an urban-rural gradient, with many outbreaks sustained by between-county transmission. We detect a dynamic tension between the spatial scale of public health interventions and population susceptibility as pre-pandemic contact is resumed. Further, we identify regions rendered particularly at risk from invasion by variants of concern due to spatial connectivity. These findings emphasize the public health importance of accounting for the hierarchy of spatial scales in transmission and the heterogeneous impacts of mobility on the landscape of contagion risk.

Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study

BACKGROUND

Inequities in the burden of COVID-19 were observed early in Canada and around the world suggesting economically marginalized communities faced disproportionate risks. However, there has been limited systematic assessment of how heterogeneity in risks has evolved in large urban centers over time.

PURPOSE

To address this gap, we quantified the magnitude of risk heterogeneity in Toronto, Ontario from January-November, 2020 using a retrospective, population-based observational study using surveillance data.

METHODS

We generated epidemic curves by social determinants of health (SDOH) and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 and estimated Gini coefficients. We examined the correlation between SDOH using Pearson-correlation coefficients.

RESULTS

Gini coefficient of cumulative cases by population size was 0.41 (95% confidence interval [CI]:0.36-0.47) and estimated for: household income (0.20, 95%CI: 0.14-0.28); visible minority (0.21, 95%CI:0.16-0.28); recent immigration (0.12, 95%CI:0.09-0.16); suitable housing (0.21, 95%CI:0.14-0.30); multi-generational households (0.19, 95%CI:0.15-0.23); and essential workers (0.28, 95%CI:0.23-0.34).

CONCLUSIONS

There was rapid epidemiologic transition from higher to lower income neighbourhoods with Lorenz curve transitioning from below to above the line of equality across SDOH. Moving forward necessitates integrating programs and policies addressing socioeconomic inequities and structural racism into COVID-19 prevention and vaccination programs.