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1.
Commun Med (Lond) ; 2: 54, 2022.
Article in English | MEDLINE | ID: covidwho-1947549

ABSTRACT

Background: The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion. Methods: We built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries. Results: We demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49-2.53%. Conclusion: We developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics.

2.
Front Immunol ; 13: 891524, 2022.
Article in English | MEDLINE | ID: covidwho-1933678

ABSTRACT

Since the start of the COVID-19 pandemic, mutations have led to the emergence of new SARS-CoV-2 variants, and some of these have become prominent or dominant variants of concern. This natural course of development can have an impact on how protective the previously naturally or vaccine induced immunity is. Therefore, it is crucial to understand whether and how variant specific mutations influence host immunity. To address this, we have investigated how mutations in the recent SARS-CoV-2 variants of interest and concern influence epitope sequence similarity, predicted binding affinity to HLA, and immunogenicity of previously reported SARS-CoV-2 CD8 T cell epitopes. Our data suggests that the vast majority of SARS-CoV-2 CD8 T cell recognized epitopes are not altered by variant specific mutations. Interestingly, for the CD8 T cell epitopes that are altered due to variant specific mutations, our analyses show there is a high degree of sequence similarity between mutated and reference SARS-CoV-2 CD8 T cell epitopes. However, mutated epitopes, primarily derived from the spike protein, in SARS-CoV-2 variants Delta, AY.4.2 and Mu display reduced predicted binding affinity to their restriction element. These findings indicate that the recent SARS-CoV-2 variants of interest and concern have limited ability to escape memory CD8 T cell responses raised by vaccination or prior infection with SARS-CoV-2 early in the pandemic. The overall low impact of the mutations on CD8 T cell cross-recognition is in accordance with the notion that mutations in SARS-CoV-2 are primarily the result of receptor binding affinity and antibody selection pressures exerted on the spike protein, unrelated to T cell immunity.


Subject(s)
COVID-19 , SARS-CoV-2 , CD8-Positive T-Lymphocytes , Epitopes, T-Lymphocyte/genetics , Humans , Pandemics , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics
3.
Stud Health Technol Inform ; 290: 1092-1093, 2022 Jun 06.
Article in English | MEDLINE | ID: covidwho-1933595

ABSTRACT

Since the emergence of SARS-CoV-2 in November 2019, there has been an exponential production of literature due to worldwide efforts to understand the interactions between the virus and the human body. Using an "in-house" developed script we retrieved gene annotations and identified phenotype enrichments. Human Phenotype Ontology terms were retrieved from the literature using the Onassis R package. This produced both disease-gene and disease-phenotype data as well as data for gene-phenotype interactions. Overall, we retrieved 181 human phenotypes that were identified by both approaches. Further in-depth analysis of these relationships could provide further insights in the molecular mechanisms related with the observed phenotypes, answers and hypotheses for key concepts within COVID-19 research.


Subject(s)
COVID-19 , Humans , Phenotype , SARS-CoV-2/genetics
4.
Bioinformation ; 18(3):170-179, 2022.
Article in English | Web of Science | ID: covidwho-1918055

ABSTRACT

It is of interest to document the Molecular Dynamics Simulation and docking analysis of NF-kappa B target with sulindac sodium in combating COVID-19 for further consideration. Sulindac is a nonsteroidal anti-inflammatory drug (NSAID) of the arylalkanoic acid class that is marketed by Merck under the brand name Clinoril. We show the binding features of sulindac sodium with NF-kappa B that can be useful in drug repurposing in COVID-19 therapy.

5.
Mathematical Biosciences and Engineering ; 19(9):9060-9078, 2022.
Article in English | Web of Science | ID: covidwho-1917919

ABSTRACT

Several outbreaks of COVID-19 caused by imported cases have occurred in China following the successful control of the outbreak in early 2020. In order to avoid recurrences of such local outbreaks, it is important to devise an efficient control and prevention strategy. In this paper, we developed a stochastic discrete model of the COVID-19 epidemic in Guangzhou in 2021 to compare the effectiveness of centralized quarantine and compulsory home quarantine measures. The model was calibrated by using the daily reported cases and newly centralized quarantined cases. The estimated results showed that the home quarantine measure increased the accuracy of contact tracing. The estimated basic reproduction number was lower than that in 2020, even with a much more transmissible variant, demonstrating the effectiveness of the vaccines and normalized control interventions. Sensitivity analysis indicated that a sufficiently implemented contact tracing and centralized quarantine strategy in the initial stage would contain the epidemic faster with less infections even with a weakly implemented compulsory home quarantine measure. However, if the accuracy of the contact tracing was insufficient, then early implementation of the compulsory home quarantine with strict contact tracing, screening and testing interventions on the key individuals would shorten the epidemic duration and reduce the total number of infected cases. Particularly, 94 infections would have been avoided if the home quarantine measure had been implemented 3 days earlier and an extra 190 infections would have arisen if the home quarantine measure was implemented 3 days later. The study suggested that more attention should be paid to the precise control strategy during the initial stage of the epidemic, otherwise the key group-based control measure should be implemented strictly.

6.
Commun Med (Lond) ; 2: 74, 2022.
Article in English | MEDLINE | ID: covidwho-1915299

ABSTRACT

Background: The reduction in SARS-CoV-2 transmission facilitated by mobile contact tracing applications (apps) depends both on the proportion of relevant contacts notified and on the probability that those contacts quarantine after notification. The proportion of relevant contacts notified depends upon the number of days preceding an infector's positive test that their contacts are notified, which we refer to as an app's notification window. Methods: We use an epidemiological model of SARS-CoV-2 transmission that captures the profile of infection to consider the trade-off between notification window length and active app use. We focus on 5-day and 2-day windows, the notification windows of the NHS COVID-19 app in England and Wales before and after 2nd August 2021, respectively. Results: Our analyses show that at the same level of active app use, 5-day windows result in larger reductions in transmission than 2-day windows. However, short notification windows can be more effective at reducing transmission if they are associated with higher levels of active app use and adherence to isolation upon notification. Conclusions: Our results demonstrate the importance of understanding adherence to interventions when setting notification windows for COVID-19 contact tracing apps.


After submitting a positive SARS-CoV-2 test result, mobile contact-tracing apps identify 'recent' high-risk encounters with other app users, who are then notified of potential exposure. An app's success at limiting further transmission depends on the proportion of infected contacts notified. This depends on what counts as 'recent', e.g. notifying contacts from 5 days prior to the positive test can capture more infections than notifying contacts from 2 days prior. We call this number of days an app's notification window. However, an app's effectiveness also depends on whether or not exposed contacts use the app and adhere to isolation if notified. If shorter windows are associated with higher levels of active app use, they can be more effective at reducing transmission than longer windows, demonstrating the importance of considering the potential impact on active app use when setting an app's notification window length.

7.
ESC Heart Fail ; 2022 Jun 21.
Article in English | MEDLINE | ID: covidwho-1894590

ABSTRACT

AIMS: The co-morbidities contribute to the inferior prognosis of COVID-19 patients. Recent reports suggested that the higher co-morbidity rate between COVID-19 and heart failure (HF) leads to increased mortality. However, the common pathogenic mechanism between them remained elusive. Here, we aimed to reveal underlying molecule mechanisms and genetic correlation between COVID-19 and HF, providing a new perspective on current clinical management for patients with co-morbidity. METHODS: The gene expression profiles of HF (GSE26887) and COVID-19 (GSE147507) were retrieved from the GEO database. After identifying the common differentially expressed genes (|log2FC| > 1 and adjusted P < 0.05), integrated analyses were performed, namely, enrichment analyses, protein-protein interaction network, module construction, critical gene identification, and functional co-expression analysis. The performance of critical genes was validation combining hierarchical clustering, correlation, and principal component analysis in external datasets (GSE164805 and GSE9128). Potential transcription factors and miRNAs were obtained from the JASPER and RegNetwork repository used to construct co-regulatory networks. The candidate drug compounds in potential genetic link targets were further identified using the DSigDB database. RESULTS: The alteration of 12 genes was identified as a shared transcriptional signature, with the role of immune inflammatory pathway, especially Toll-like receptor, NF-kappa B, chemokine, and interleukin-related pathways that primarily emphasized in response to SARS-CoV-2 complicated with HF. Top 10 critical genes (TLR4, TLR2, CXCL8, IL10, STAT3, IL1B, TLR1, TP53, CCL20, and CXCL10) were identified from protein-protein interaction with topological algorithms. The unhealthy microbiota status and gut-heart axis in co-morbidity were identified as potential disease roads in bridging pathogenic mechanism, and lipopolysaccharide acts as a potential marker for monitoring HF during COVID-19. For transcriptional and post-transcriptional levels, regulation networks tightly coupling with both disorders were constructed, and significant regulator signatures with high interaction degree, especially FOXC1, STAT3, NF-κB1, miR-181, and miR-520, were detected to regulate common differentially expressed genes. According to genetic links targets, glutathione-based antioxidant strategy combined with muramyl dipeptide-based microbe-derived immunostimulatory therapies was identified as promising anti-COVID-19 and anti-HF therapeutics. CONCLUSIONS: This study identified shared transcriptomic and corresponding regulatory signatures as emerging therapeutic targets and detected a set of pharmacologic agents targeting genetic links. Our findings provided new insights for underlying pathogenic mechanisms between COVID-19 and HF.

8.
Ann Transl Med ; 10(11): 635, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1884865

ABSTRACT

Background: Patients with rheumatoid arthritis (RA) may be more susceptible to infection by coronavirus disease-19 (COVID-19) due to immune system dysfunction. However, there are still insufficient treatment strategies for patients with RA and COVID-19. Since Jingulian is a traditional Chinese medicine (TCM) with anti-viral and immune regulatory functions, our study aims to explore the detailed mechanisms of Jingulian in treating patients with RA and COVID-19. Methods: All the components of Jingulian were retrieved from pharmacology databases. Then, a series of network pharmacology-based analyses and molecular docking were used to understand the molecular functions, core targets, related pathways, and potential therapeutic targets of Jingulian in patients with RA/COVID-19. Results: A total of 93 genes were identified according to the disease-compound-target network. We investigated that the main targets, signaling pathways, and biological functions of Jingulian in RA and COVID-19. Our results indicated that Jingulian may treat patients with RA/COVID-19 through immune processes and viral processes. Moreover, the results of molecular docking revealed that tormentic acid was one of the top compounds of Jingulian, which had high affinity with Janus kinase 1 (JAK1), signal transducer and activator of transcription 3 (STAT3), and epidermal growth factor receptor (EGFR) in patients with RA/COVID-19. Furthermore, 5 core targets of Jingulian were also identified, including JAK1, Janus kinase 2 (JAK2), STAT3, lymphocyte specific protein tyrosine kinase (LCK), and EGFR. Conclusions: Tormentic acid in Jingulian may regulate JAK1, STAT3, and EGFR, and might play a critical role in RA/COVID-19.

9.
Viruses ; 14(6)2022 05 26.
Article in English | MEDLINE | ID: covidwho-1869814

ABSTRACT

In silico methods for immune epitope prediction have become essential for vaccine and therapeutic design, but manual intra-species comparison of putative epitopes remains challenging and subject to human error. Created initially for analyzing SARS-CoV-2 variants of concern, comparative analysis of variant epitope sequences (CAVES) is a novel tool designed to carry out rapid comparative analyses of epitopes amongst closely related pathogens, substantially reducing the required time and user workload. CAVES applies a two-level analysis approach. The Level-one (L1) analysis compares two epitope prediction files, and the Level-two (L2) analysis incorporates search results from the IEDB database of experimentally confirmed epitopes. Both L1 and L2 analyses sort epitopes into categories of exact matches, partial matches, or novel epitopes based on the degree to which they match with peptides from the compared file. Furthermore, CAVES uses positional sequence data to improve its accuracy and speed, taking only a fraction of the time required by manual analyses and minimizing human error. CAVES is widely applicable for evolutionary analyses and antigenic comparisons of any closely related pathogen species. CAVES is open-source software that runs through a graphical user interface on Windows operating systems, making it widely accessible regardless of coding expertise. The CAVES source code and test dataset presented here are publicly available on the CAVES GitHub page.


Subject(s)
COVID-19 , SARS-CoV-2 , Computational Biology/methods , Epitopes/genetics , Epitopes, T-Lymphocyte , Humans , SARS-CoV-2/genetics , Software
10.
International Journal of Biomathematics ; 15(05):26, 2022.
Article in English | Web of Science | ID: covidwho-1868782

ABSTRACT

To define a vaccination policy and antiviral treatment against the spreading of viral infections a nonlinear optimal (H-infinity) control approach is proposed. Actually, because of the scarcity of the resources for treating infectious diseases in terms of vaccines, antiviral drugs and other medical facilities, there is need to implement optimal control against the epidemics deployment. In this approach, the state-space model of the epidemics dynamics undergoes first approximate linearization around a temporary operating point which is recomputed at each time-step of the control method. The linearization is based on Taylor series expansion and on the computation of the associated Jacobian matrices. Next, an optimal (H-infinity) feedback controller is developed for the approximately linearized model of the epidemics. To compute the controller's feedback gains an algebraic Riccati equation is solved at each iteration of the control algorithm. Furthermore, the global asymptotic stability properties of the control scheme are proven through Lyapunov stability analysis. This paper's results confirm that optimal control of the infectious disease dynamics allows for eliminating its spreading while also keeping moderate the consumption of the related medication, that is vaccines and antiviral drugs.

11.
Mathematical Modelling of Natural Phenomena ; 17:20, 2022.
Article in English | Web of Science | ID: covidwho-1868030

ABSTRACT

We develop a new data-driven immuno-epidemiological model with distributed infectivity, recovery and death rates determined from the epidemiological, clinical and experimental data. Immunity in the population is taken into account through the time-dependent number of vaccinated people with different numbers of doses and through the acquired immunity for recovered individuals. The model is validated with the available data. We show that for the first time from the beginning of pandemic COVID-19 some countries reached collective immunity. However, the epidemic continues because of the emergence of new variant BA.2 with a larger immunity escape or disease transmission rate than the previous BA.l variant. Large epidemic outbreaks can be expected several months later due to immunity waning. These outbreaks can be restrained by an intensive booster vaccination.

12.
Mathematical Modelling of Natural Phenomena ; 17:17, 2022.
Article in English | Web of Science | ID: covidwho-1868029

ABSTRACT

The use of epidemic modelling in connection with spread of diseases plays an important role in understanding dynamics and providing forecasts for informed analysis and decision-making. In this regard, it is crucial to quantify the effects of uncertainty in the modelling and in model-based predictions to trustfully communicate results and limitations. We propose to do efficient uncertainty quantification in compartmental epidemic models using the generalized Polynomial Chaos (gPC) framework. This framework uses a suitable polynomial basis that can be tailored to the underlying distribution for the parameter uncertainty to do forward propagation through efficient sampling via a mathematical model to quantify the effect on the output. By evaluating the model in a small number of selected points, gPC provides illuminating statistics and sensitivity analysis at a low computational cost. Through two particular case studies based on Danish data for the spread of Covid-19, we demonstrate the applicability of the technique. The test cases consider epidemic peak time estimation and the dynamics between superspreading and partial lockdown measures. The computational results show the efficiency and feasibility of the uncertainty quantification techniques based on gPC, and highlight the relevance of computational uncertainty quantification in epidemic modelling.

13.
Stud Health Technol Inform ; 294: 711-712, 2022 May 25.
Article in English | MEDLINE | ID: covidwho-1865437

ABSTRACT

CovidGraph, developed by the HealthECCO community, is a platform designed to foster research and data exploration to fight COVID-19. It is built on a graph database and encompasses data sources from different biomedical data domains including publications, clinical trials, patents, case statistics, molecular data and systems biology models. The tool provides multiple interfaces for data exploration and thus serves as a single point of entry for data driven COVID-19 research. Availability and Implementation: CovidGraph is available from the project website: https://healthecco.org/covidgraph/. The source code and documentation are provided on GitHub: https://github.com/covidgraph.


Subject(s)
COVID-19 , Databases, Factual , Documentation , Humans , Information Storage and Retrieval , Software
14.
Commun Med (Lond) ; 1: 57, 2021.
Article in English | MEDLINE | ID: covidwho-1860423

ABSTRACT

Background: After one year of stop-and-go COVID-19 mitigation, in the spring of 2021 European countries still experienced sustained viral circulation due to the Alpha variant. As the prospect of entering a new pandemic phase through vaccination was drawing closer, a key challenge remained on how to balance the efficacy of long-lasting interventions and their impact on the quality of life. Methods: Focusing on the third wave in France during spring 2021, we simulate intervention scenarios of varying intensity and duration, with potential waning of adherence over time, based on past mobility data and modeling estimates. We identify optimal strategies by balancing efficacy of interventions with a data-driven "distress" index, integrating intensity and duration of social distancing. Results: We show that moderate interventions would require a much longer time to achieve the same result as high intensity lockdowns, with the additional risk of deteriorating control as adherence wanes. Shorter strict lockdowns are largely more effective than longer moderate lockdowns, for similar intermediate distress and infringement on individual freedom. Conclusions: Our study shows that favoring milder interventions over more stringent short approaches on the basis of perceived acceptability could be detrimental in the long term, especially with waning adherence.

15.
Elife ; 112022 05 17.
Article in English | MEDLINE | ID: covidwho-1847655

ABSTRACT

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.


While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.


Subject(s)
COVID-19 , SARS-CoV-2 , Hospitalization , Hospitals , Humans , Machine Learning , Retrospective Studies
16.
17.
Comput Biol Med ; 146: 105443, 2022 07.
Article in English | MEDLINE | ID: covidwho-1803805

ABSTRACT

STATEMENT: Enrichment analysis of cell transcriptional responses to SARS-CoV-2 infection from biclustering solutions yields broader coverage and superior enrichment of GO terms and KEGG pathways against alternative state-of-the-art machine learning solutions, thus aiding knowledge extraction. MOTIVATION AND METHODS: The comprehensive understanding of the impacts of SARS-CoV-2 virus on infected cells is still incomplete. This work aims at comparing the role of state-of-the-art machine learning approaches in the study of cell regulatory processes affected and induced by the SARS-CoV-2 virus using transcriptomic data from both infectable cell lines available in public databases and in vivo samples. In particular, we assess the relevance of clustering, biclustering and predictive modeling methods for functional enrichment. Statistical principles to handle scarcity of observations, high data dimensionality, and complex gene interactions are further discussed. In particular, and without loos of generalization ability, the proposed methods are applied to study the differential regulatory response of lung cell lines to SARS-CoV-2 (α-variant) against RSV, IAV (H1N1), and HPIV3 viruses. RESULTS: Gathered results show that, although clustering and predictive algorithms aid classic stances to functional enrichment analysis, more recent pattern-based biclustering algorithms significantly improve the number and quality of enriched GO terms and KEGG pathways with controlled false positive risks. Additionally, a comparative analysis of these results is performed to identify potential pathophysiological characteristics of COVID-19. These are further compared to those identified by other authors for the same virus as well as related ones such as SARS-CoV-1. The findings are particularly relevant given the lack of other works utilizing more complex machine learning algorithms within this context.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Cluster Analysis , Humans , Machine Learning , SARS-CoV-2
18.
Math. Model. Nat. Phenom. ; 17:24, 2022.
Article in English | Web of Science | ID: covidwho-1795645

ABSTRACT

The Covid-19 pandemic outbreak was followed by a huge amount of modelling studies in order to rapidly gain insights to implement the best public health policies. Most of these compartmental models involved ordinary differential equations (ODEs) systems. Such a formalism implicitly assumes that the time spent in each compartment does not depend on the time already spent in it, which is at odds with the clinical data. To overcome this "memoryless" issue, a widely used solution is to increase and chain the number of compartments of a unique reality (e.g. have infected individual move between several compartments). This allows for greater heterogeneity and thus be closer to the observed situation, but also tends to make the whole model more difficult to apprehend and parameterize. We develop a non-Markovian alternative formalism based on partial differential equations (PDEs) instead of ODEs, which, by construction, provides a memory structure for each compartment thereby allowing us to limit the number of compartments. We apply our model to the French 2021 SARS-CoV-2 epidemic and, while accounting for vaccine-induced and natural immunity, we analyse and determine the major components that contributed to the Covid-19 hospital admissions. The results indicate that the observed vaccination rate alone is not enough to control the epidemic, and a global sensitivity analysis highlights a huge uncertainty attributable to the age-structured contact matrix. Our study shows the flexibility and robustness of PDE formalism to capture national COVID-19 dynamics and opens perspectives to study medium or long-term scenarios involving immune waning or virus evolution.

19.
Commun Med (Lond) ; 1(1): 32, 2021.
Article in English | MEDLINE | ID: covidwho-1768865

ABSTRACT

BACKGROUND: Persistence of antibodies to SARS-CoV-2 viral infection may depend on several factors and may be related to the severity of disease or to the different symptoms. METHODS: We evaluated the antibody response to SARS-CoV-2 in personnel from 9 healthcare facilities and an international medical school and its association with individuals' characteristics and COVID-19 symptoms in an observational cohort study. We enrolled 4735 subjects (corresponding to 80% of all personnel) for three time points over a period of 8-10 months. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach. RESULTS: Here we show in individuals positive for IgG (≥12 AU/mL) at the beginning of the study an increase [p = 0.0002] in antibody response in paucisymptomatic or symptomatic subjects, particularly with loss of taste or smell (anosmia/dysgeusia: OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis in the first three months. The antibody response persists for at least 8-10 months. CONCLUSIONS: SARS-CoV-2 infection induces a long lasting antibody response that increases in the first months, particularly in individuals with anosmia/dysgeusia. This may be linked to the lingering of SARS-CoV-2 in the olfactory bulb.

20.
Elife ; 112022 03 16.
Article in English | MEDLINE | ID: covidwho-1766127

ABSTRACT

Publications are essential for a successful academic career, and there is evidence that the COVID-19 pandemic has amplified existing gender disparities in the publishing process. We used longitudinal publication data on 431,207 authors in four disciplines - basic medicine, biology, chemistry and clinical medicine - to quantify the differential impact of COVID-19 on the annual publishing rates of men and women. In a difference-in-differences analysis, we estimated that the average gender difference in publication productivity increased from -0.26 in 2019 to -0.35 in 2020; this corresponds to the output of women being 17% lower than the output of men in 2109, and 24% lower in 2020. An age-group comparison showed a widening gender gap for both early-career and mid-career scientists. The increasing gender gap was most pronounced among highly productive authors and in biology and clinical medicine. Our study demonstrates the importance of reinforcing institutional commitments to diversity through policies that support the inclusion and retention of women in research.


Subject(s)
COVID-19 , Efficiency , Female , Humans , Male , Pandemics , Publishing , Sex Factors
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