Some insights on the COVID-19 pandemic from Fisher information
Ontology highlight
ABSTRACT:
SUBMITTER: Cabezas H
PROVIDER: S-EPMC10906310 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
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Project description: Not available
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Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.
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Project description:Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R0 (of approximately 2-3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response. †Unless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.
| S-EPMC7305515 | biostudies-literature
Project description:IntroductionMiller Fisher syndrome (MFS) is a rare variant of Guillain-Barre syndrome characterized by ataxia, areflexia, and ophthalmoplegia. We present a case of MFS following Pfizer COVID-19 vaccine.Case presentationA previously healthy 24-year-old female presented with binocular horizontal diplopia 18 days after receiving the first dose of Pfizer COVID-19 vaccine (Comirnaty®). Anti-ganglioside testing revealed positive anti-GQ1b antibodies. Intravenous immunoglobulins were administered, in a dose of 2 g per kg of body weight over 5 days. On a follow-up exam 3 weeks after the treatment, clinical improvement was noted with normal bulbomotor examination.ConclusionPatients with acute ophthalmoplegia occurring after COVID-19 vaccination should be screened for the presence of anti-GQ1b antibody. If the antibody is present, intravenous immunoglobulin should be administered as it may hasten clinical improvement.
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Project description:Primary outcome(s): The incidence of obstructive colorectal cancer
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Project description:The COVID-19 pandemic has impacted all aspects of our lives, including the information spread on social media. Prior literature has found that information diffusion dynamics on social networks mirror that of a virus, but applying the epidemic Susceptible-Infected-Removed model (SIR) model to examine how information spread is not sufficient to claim that information spreads like a virus. In this study, we explore whether there are similarities in the simulated SIR model (SIRsim), observed SIR model based on actual COVID-19 cases (SIRemp), and observed information cascades on Twitter about the virus (INFOcas) by using network analysis and diffusion modeling. We propose three primary research questions: (a) What are the diffusion patterns of COVID-19 virus spread, based on SIRsim and SIRemp? (b) What are the diffusion patterns of information cascades on Twitter (INFOcas), with respect to retweets, quote tweets, and replies? and (c) What are the major differences in diffusion patterns between SIRsim, SIRemp, and INFOcas? Our study makes a contribution to the information sciences community by showing how epidemic modeling of virus and information diffusion analysis of online social media are distinct but interrelated concepts.
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been identified as the cause of the Coronavirus disease 19 (COVID-19), which was initially reported in December 2019 in China and has since rapidly spread worldwide.
Since then, the COVID-19 pandemic has caused a detrimental effect of the national health care system, causing a drastic reduction of the screening programs for colorectal cancer and requiring the redistribution of the hospital resources from elective surgery to the care of patients with SARS-Cov_2 infection requiring admission.
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Project description:The COVID-19 pandemic has stimulated important changes in online information access as digital engagement became necessary to meet the demand for health, economic, and educational resources. Our analysis of 55 billion everyday web search interactions during the pandemic across 25,150 US ZIP codes reveals that the extent to which different communities of internet users enlist digital resources varies based on socioeconomic and environmental factors. For example, we find that ZIP codes with lower income intensified their access to health information to a smaller extent than ZIP codes with higher income. We show that ZIP codes with higher proportions of Black or Hispanic residents intensified their access to unemployment resources to a greater extent, while revealing patterns of unemployment site visits unseen by the claims data. Such differences frame important questions on the relationship between differential information search behaviors and the downstream real-world implications on more and less advantaged populations.
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Project description:Increased mental-health symptoms as a reaction to stressful life events, such as the Covid-19 pandemic, are common. Critically, successful adaptation helps to reduce such symptoms to baseline, preventing long-term psychiatric disorders. It is thus important to understand whether and which psychiatric symptoms show transient elevations, and which persist long-term and become chronically heightened. At particular risk for the latter trajectory are symptom dimensions directly affected by the pandemic, such as obsessive-compulsive (OC) symptoms. In this longitudinal large-scale study (N = 406), we assessed how OC, anxiety and depression symptoms changed throughout the first pandemic wave in a sample of the general UK public. We further examined how these symptoms affected pandemic-related information seeking and adherence to governmental guidelines. We show that scores in all psychiatric domains were initially elevated, but showed distinct longitudinal change patterns. Depression scores decreased, and anxiety plateaued during the first pandemic wave, while OC symptoms further increased, even after the ease of Covid-19 restrictions. These OC symptoms were directly linked to Covid-related information seeking, which gave rise to higher adherence to government guidelines. This increase of OC symptoms in this non-clinical sample shows that the domain is disproportionately affected by the pandemic. We discuss the long-term impact of the Covid-19 pandemic on public mental health, which calls for continued close observation of symptom development.
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