Associations between Serum Levels of C3, C4, and Total Classical Complement Activity in Covid-19 Patients at the Time of Admission and Clinical Outcome

In the present study, we investigated the association between complement system status at the time of admission and clinical outcomes in COVID-19 patients. This single-center study was carried out with sixty-one adult patients with COVID-19 who were hospitalized at Imam Hassan Hospital of North Khorasan University of Medical Sciences (Bojnurd, Iran) with less than three days passage since onset of COVID-19 symptoms. Twenty-three healthy volunteers with demographic features similar to the patient group (matched by age and gender) were included in the study as a control group. Patient information including demographic information, demographic data, clinical characteristics, and clinical outcomes were obtained from electronic medical records. Of 61 hospitalized patients with COVID-19, 28 (47.54%) were female, and the average age was 48.7+/-8.8 years. The healthy control group included 23 cases (11 (47.8%) female, 12 (52.1%) males, mean age 46.4+/-4.4 years). Twenty-one of the 61 patients (34.4%) were admitted to the ICU, and sixteen of them (26.2%) died. Thirty-three (54.10%) patients with COVID-19 were hospitalized for less than 7 days, and 28 (45.90%) of them were hospitalized for >= 7 days. Our results show that length of hospital stay in the no-ICU group was significantly lower than the ICU admission or death groups (6.49+/-0.24 vs. 8.85+/-1.59 and 10.53+/-1.80, p = 0.0002). The levels of C3, C4, and CH50 were determined through the immunoturbidimetric method and single-radial-haemolysis plates, respectively, on serum samples obtained from patients at the time of admission or those in the control group. Our results indicate that C3, C4 and CH50 levels were markedly lower in COVID-19 patients than in the control group. We also found that complement parameter levels in COVID-19 patients who died or were admitted to ICU were significantly lower than in non-ICU COVID-19 patients. In general, it seems that serum level of C3, C4, and CH50 at admission may predict disease progression or adverse clinical outcome in COVID-19 patients. Copyright © 2022 Saint Petersburg Pasteur Institute. All rights reserved.

Social media during the covid-19 pandemic: A public health crisis or a political battle?

Since the start of coronavirus disease 2019 (COVID-19) pandemic, social media platforms have been filled with discussions about the global health crisis. Meanwhile, the World Health Organization (WHO) has highlighted the importance of seeking credible sources of information on social media regarding COVID-19. In this study, we conducted an in-depth analysis of Twitter posts about COVID-19 during the early days of the COVID-19 pandemic to identify influential sources of COVID-19 information and understand the characteristics of these sources. We identified influential accounts based on an information diffusion network representing the interactions of Twitter users who discussed COVID-19 in the United States over a 24-h period. The network analysis revealed 11 influential accounts that we categorized as: 1) political authorities (elected government officials), 2) news organizations, and 3) personal accounts. Our findings showed that while verified accounts with a large following tended to be the most influential users, smaller personal accounts also emerged as influencers. Our analysis revealed that other users often interacted with influential accounts in response to news about COVID-19 cases and strongly contested political arguments received the most interactions overall. These findings suggest that political polarization was a major factor in COVID-19 information diffusion. We discussed the implications of political polarization on social media for COVID-19 communication. © Springer Nature Switzerland AG 2021.

Testing and tracking in the UK: A dynamic causal modelling study [version 1;peer review: 1 approved with reservations, 1 not approved]

By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies. © 2020. Friston KJ et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Second waves, social distancing, and the spread of COVID-19 across America

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity-and the exchange of people between regions-and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.