Modeling the first wave of Covid-19 pandemic in the Republic of Cyprus.
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ABSTRACT:
SUBMITTER: Agapiou S
PROVIDER: S-EPMC8017012 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionImaging is essential for the initial diagnosis and monitoring of the novel coronavirus, which emerged in Wuhan, China. This study aims to assess the insight of radiographers on how the COVID-19 pandemic has affected their work routine and if protective measures are applied.MethodA prospective observational study was conducted among radiographers registered in the Cyprus Society of Registered Radiologic Technologists & Radiation Therapy Technologists. A questionnaire composed of 28 multiple choice questions was utilised, and the data analysis was performed using SPSS software with the statistical significance assumed as p-value < 0.05.ResultsOut of 350 registered radiographers, 101 responses were received. The results showed that there are statistically significant differences regarding the working hours, the feeling of stress, the work effectiveness, the average examination time, the presence of a protocol used among the different workplaces of the participants; a private radiology centre, a private hospital or a public hospital, with a p-value 0.0022, 0.015, 0.027, 0.001, 0.0001 respectively. Also, statistically significant differences were observed in the decontamination methods used for equipment (p-value 0.007), for air (p-value 0.04) and when decontamination takes place (p-value 0.00032) among the different workplaces of the participants. Nonetheless, the majority of radiographers believe that their workplace is sufficiently provided with PPE, cleaning supplies, equipment, and with cleaning personnel and are optimistic regarding the adequacy of these provisions in the next three months.ConclusionThis study showed that in the Republic of Cyprus, there are protocols regarding protective measures against COVID-19, and the radiographers are adequately trained on how to face an infectious disease outbreak. However, work is needed in order to develop protocols that reassure the safety of patients and medical personnel while managing the excess workload effectively.Implications for practiceThis study indicates the importance of applying protective measures and protocols in the radiology departments in order to minimise the spread of the virus.
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Project description:Proteomic Ig-SEQ identification of SARS-COV-2 specific antibodies from serum of two convalescent patients during the first wave of the COVID-19 pandemic. Total IgG was isolated from serum, digested with IdeS protease to generate F(ab)2 fragments, and subjected to antigen affinity chromatography using agarose beads coupled with SARS-COV-2 spike protein. Purified F(ab)2 fragments were reduced, alkylated, and digested with trypsin prior to LC-MSMS analysis on an Orbitrap Fusion Lumos mass spectrometer. Resulting peptide IDs were used to identify IgG clonotypes in antigen-specific and flow-through fractions.
2024-01-26 | PXD046829 | Pride
Project description:To evaluate the gene expression profiling of peripheral leukocytes in different outcomes of SARS-CoV-2 infections, whole blood samples were collected from individuals with positive SARS-CoV-2 nasopharyngeal swab by RT-PCR (54 patients) and healthy uninfected individuals (12 volunteers). Infected patients were classified into mild, moderate, severe and critical groups according to a modified statement in the Novel Coronavirus Pneumonia Diagnosis and Treatment Guideline. Blood were collected into EDTA tubes and the buffy coat samples were stored in TRIzol reagent at -80 °C until use for RNA extraction. Affymetrix Clariom S array was used to perform the high-throughput gene expression profiling. Microarray analyses were performed using APT Affymetrix software, R packages and Bioconductor libraries. This systemic view of SARS-CoV-2 infections through blood transcriptomics will foster the understanding about molecular mechanisms and immunopathological processes involved in COVID-19 disease and its different outcomes.
2022-04-29 | E-MTAB-11240 | biostudies-arrayexpress
Project description:Running across the globe for nearly 2 years, the Covid-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with epidemiological and sociological data for the first Covid-19 wave in the Czech Republic and found that (1) starting the spring 2020 lockdown 4 days earlier might prevent half of the confirmed cases by the end of lockdown period, (2) personal protective measures such as face masks appear more effective than just a realized reduction in social contacts, (3) the strategy of sheltering just the elderly is not at all effective, and (4) leaving schools open is a risky strategy. Despite vaccination programs, evidence-based choice and timing of non-pharmaceutical interventions remains an effective weapon against the Covid-19 pandemic.
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Project description:BackgroundThe novel coronavirus disease 2019 (COVID-19) pandemic has spread from China to the rest of the world. Africa seems less impacted with lower number of cases and deaths than other continents. Senegal recorded its first case on March 2, 2020. We present here data collected from March 2 to October 31, 2020 in Senegal.MethodsSocio-demographic, epidemiological, clinical and virological information were collected on suspected cases. To determine factors associated with diagnosed infection, symptomatic disease and death, multivariable binary logistic regression and log binomial models were used. Epidemiological parameters such as the reproduction number and growth rate were estimated.Results67,608 suspected cases were tested by the IPD laboratories (13,031 positive and 54,577 negative). All age categories were associated with SARS-CoV-2 infection, but also patients having diabetes or hypertension or other cardiovascular diseases. With diagnosed infection, patients over 65 years and those with hypertension and cardiovascular disease and diabetes were highly associated with death. Patients with co-morbidities were associated with symptomatic disease, but only the under 15 years were not associated with. Among infected, 27.67% were asymptomatic (40.9% when contacts were systematically tested; 12.11% when only symptomatic or high-risk contacts were tested). Less than 15 years-old were mostly asymptomatic (63.2%). Dakar accounted for 81.4% of confirmed cases. The estimated mean serial interval was 5.57 (± 5.14) days. The average reproduction number was estimated at 1.161 (95%CI: 1.159-1.162), the growth rate was 0.031 (95%CI: 0.028-0.034) per day.ConclusionsOur findings indicated that factors associated with symptomatic COVID-19 and death are advanced age (over 65 years-old) and comorbidities such as diabetes and hypertension and cardiovascular disease.
| S-EPMC9488827 | biostudies-literature
Project description:Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region, predicts COVID-19 statistics (the daily number of cases, hospitalizations, and deaths), and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity), different variants of concern, vaccination rate, and mitigation measures. Using Switzerland as a case study, COVIDHunter estimates that we are experiencing a deadly new wave that will peak on 26 January 2022, which is very similar in numbers to the wave we had in February 2020. The policy-makers have only one choice that is to increase the strength of the currently applied mitigation measures for 30 days. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures. We release the source code of the COVIDHunter implementation at https://github.com/CMU-SAFARI/COVIDHunter and show how to flexibly configure our model for any scenario and easily extend it for different measures and conditions than we account for.
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Project description:Background: The first wave of COVID-19 pandemic may have significantly impacted antimicrobial consumption in hospitals. The objective of this study was to assess the evolution of antimicrobial consumption during this period.Methods: A retrospective quasi-experimental before-after study was conducted in a Spanish tertiary care hospital. The study compared two periods: pre-pandemic, from January 2018 to February 2020, and during the COVID-19 pandemic from March to June 2020. Antimicrobial consumption was analyzed monthly as defined daily doses (DDD)/100 bed-days and overall hospital and ICU consumption were evaluated. Results: An increase in the hospital consumption was noticed. Although only ceftaroline achieved statistical significance (p = 0.014), a rise was observed in most of the studied antimicrobials. A clear temporal pattern was detected. While an increase in ceftriaxone and azithromycin was observed during March, an increment in the consumption of daptomycin, carbapenems, linezolid, ceftaroline, novel cephalosporin/β-lactamase inhibitors or triazoles during April-May was noticed. In the ICU, these findings were more evident, namely ceftriaxone (p = 0.029), carbapenems (p = 0.002), daptomycin (p = 0.002), azithromycin (p = 0.030), and linezolid (p = 0.011) but followed a similar temporal pattern. Conclusion: An increase in the antimicrobial consumption during the first wave of COVID-19 pandemic was noticed, especially in the ICU. Availability of updated protocols and antimicrobial stewardship programs are essential to optimize these outcomes.
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Project description:With the COVID-19 pandemic's outbreak, millions flocked to Wikipedia for updated information. Amid growing concerns regarding an "infodemic," ensuring the quality of information is a crucial vector of public health. Investigating whether and how Wikipedia remained up to date and in line with science is key to formulating strategies to counter misinformation. Using citation analyses, we asked which sources informed Wikipedia's COVID-19-related articles before and during the pandemic's first wave (January-May 2020). We found that coronavirus-related articles referenced trusted media outlets and high-quality academic sources. Regarding academic sources, Wikipedia was found to be highly selective in terms of what science was cited. Moreover, despite a surge in COVID-19 preprints, Wikipedia had a clear preference for open-access studies published in respected journals and made little use of preprints. Building a timeline of English-language COVID-19 articles from 2001-2020 revealed a nuanced trade-off between quality and timeliness. It further showed how pre-existing articles on key topics related to the virus created a framework for integrating new knowledge. Supported by a rigid sourcing policy, this "scientific infrastructure" facilitated contextualization and regulated the influx of new information. Last, we constructed a network of DOI-Wikipedia articles, which showed the landscape of pandemic-related knowledge on Wikipedia and how academic citations create a web of shared knowledge supporting topics like COVID-19 drug development. Understanding how scientific research interacts with the digital knowledge-sphere during the pandemic provides insight into how Wikipedia can facilitate access to science. It also reveals how, aided by what we term its "citizen encyclopedists," it successfully fended off COVID-19 disinformation and how this unique model may be deployed in other contexts.
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Project description:The feeling of loneliness is a major public health concern associated with multiple somatic and psychiatric illnesses. Studies have shown increasing incidence of loneliness in the general population during the first wave of the COVID-19 pandemic. Homeless individuals are a particularly vulnerable group; however, little is known about loneliness among homeless individuals. We therefore aimed to examine the prevalence of loneliness among homeless individuals during the pandemic. Furthermore, we estimated the association between loneliness and sociodemographic and lifestyle factors, as well as the self-perceived risk of contracting COVID-19. Data from the Hamburg survey of homeless individuals were used, including 151 homeless individuals that were recruited in spring of 2020. Loneliness was measured by the 3- item version of the UCLA-3 Loneliness Scale. To summarize, 48.5% of the participants experienced loneliness. Multiple linear regressions showed increased loneliness to be associated with male gender (β = 1.07, p = 0.01), being single (β = 1.33, p = 0.00), originating from Germany (β = 1.48, p = 0.00), high frequency of sharing a sleeping space with more than three people (β = 0.42, p = 0.02) and a higher self-perceived risk of contracting COVID-19 (β = 0.41, p = 0.02). On the contrary, there was no association of loneliness with age, educational level, chronic alcohol consumption or frequently sharing a sleeping space. In conclusion, the magnitude of loneliness among homeless individuals during the pandemic was highlighted. Description of factors determining loneliness may help to identify homeless individuals at risk.
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Project description:The COVID-19 pandemic has kept the world in suspense for the past year. In most federal countries such as Germany, locally varying conditions demand for state- or county-level decisions to adapt to the disease dynamics. However, this requires a deep understanding of the mesoscale outbreak dynamics between microscale agent models and macroscale global models. Here, we use a reparameterized SIQRD network model that accounts for local political decisions to predict the spatiotemporal evolution of the pandemic in Germany at county resolution. Our optimized model reproduces state-wise cumulative infections and deaths as reported by the Robert Koch Institute and predicts the development for individual counties at convincing accuracy during both waves in spring and fall of 2020. We demonstrate the dominating effect of local infection seeds and identify effective measures to attenuate the rapid spread. Our model has great potential to support decision makers on a state and community politics level to individually strategize their best way forward during the months to come.
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