Covid-19 Vaccination in India: Prophecy of Time Period to Immune 18+ Population

In the present paper, we have prophesied how much time will be required to vaccinate 18+ population of India with at least one dose of COVID-19 vaccines. We have used non-linear extrapolation technique to prophecy, for this polynomial function is used for extrapolation. We have Fitted a non-linear polynomial of degree six to the cumulative vaccination data from 16 January 2021 to 24 July 2021 to estimate the required time period. Non-linear extrapolation results are depicted through the graphs, shows that the entire 18+ population will be vaccinated with at least 1 dose by mid of December of this year and 25% population will be fully vaccinated. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Error Detection and Error Concealment of Medical Images Using Frequency Selective Extrapolation (FSE) Algorithm

The fundamental problem of any transmission procedure is the introduction of unwanted discrepancies at the output obtained. These discrepancies cause a hindrance to the further processing of vital data present in the transmitted image. As a result, some mechanisms through which error can be identified and rectified is needed. Error is prone to occur during transmission and error detection becomes extremely important. The method for error detection through correlation function and spatial error concealment of lost image information through frequency selective extrapolation (FSE) algorithm in erroneous medical image transmission on a network is illustrated in this paper. X-ray image is used here as a medical image. This can be used for testing whether COVID, normal, or pneumonia. In this COVID scenario, the traveling is restricted and even in rural areas they cannot travel frequently. So, diagnosis can be done through the transmission of medical image taking into account the availability of a suitable transmission medium for the same. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study.

BACKGROUND: In the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important. OBJECTIVE: We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. METHODS: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. RESULTS: We estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states. CONCLUSIONS: Our novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.

On getting it right by being wrong: A case study of how flawed research may become self-fulfilling at last.

Scientists prominently argue that the COVID-19 pandemic stems not least from people's inability to understand exponential growth. They increasingly cite evidence from a classic psychological experiment published some 45 years prior to the first case of COVID-19. Despite­or precisely because of­becoming such a canonical study (more often cited than read), its critical design flaws went completely unnoticed. They are discussed here as a cautionary tale against uncritically enshrining unsound research in the "lore" of a field of research. In hindsight, this is a unique case study of researchers falling prey to just the cognitive bias they set out to study­undermining an experiment's methodology while, ironically, still supporting its conclusion.

Nucleoside analog GS-441524: pharmacokinetics in different species, safety, and potential effectiveness against Covid-19.

GS-441524, the parent nucleoside of remdesivir, has been proposed to be effective against Covid-19 based on in vitro studies and studies in animals. However, randomized clinical trials of the agent to treat Covid-19 have not been conducted. Here, we evaluated GS-441524 for Covid-19 treatment based on studies reporting pharmacokinetic parameters of the agent in mice, rats, cats, dogs, monkeys, and the single individual in the first-in-human trial supplemented with information about its activity against severe acute respiratory syndrome coronavirus 2 and safety. A dosing interval of 8 h was considered clinically relevant and used to calculate steady-state plasma concentrations of GS-441524. These ranged from 0.27 to 234.41 µM, reflecting differences in species, doses, and administration routes. Fifty percent maximal inhibitory concentrations of GS-441524 against severe acute respiratory syndrome coronavirus 2 ranged from 0.08 µM to above 10 µM with a median of 0.87 µM whereas concentrations required to produce 90% of the maximal inhibition of the virus varied from 0.18 µM to more than 20 µM with a median of 1.42 µM in the collected data. Most of these concentrations were substantially lower than the calculated steady-state plasma concentrations of the agent. Plasma exposures to orally administered GS-441524, calculated after normalization of doses, were larger for dogs, mice, and rats than cynomolgus monkeys and humans, probably reflecting interspecies differences in oral uptake with reported oral bioavailabilities below 8.0% in cynomolgus monkeys and values as high as 92% in dogs. Reported oral bioavailabilities in rodents ranged from 12% to 57%. Using different presumptions, we estimated human oral bioavailability of GS-441524 at 13% and 20%. Importantly, doses of GS-441524 lower than the 13 mg/kg dose used in the first-in-human trial may be effective against Covid-19. Also, GS-441524 appears to be well-tolerated. In conclusion, GS-441524 has potential for oral treatment of Covid-19.

Covid-19 Predictions Using a Gauss Model, Based on Data from April 2

We study a Gauss model (GM), a map from time to the bell-shaped Gaussian function to model the deaths per day and country, as a simple, analytically tractable model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e., initial exponential spread to eventual saturation, and an agent-based model, we apply the GM to existing data, as of 2 April 2020, from 25 countries during first corona pandemic wave and study the model’s predictions. We find that logarithmic daily fatalities caused by the coronavirus disease 2019 (Covid-19) are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical χ2-fit with 95% confidence, we are able to obtain the characteristic parameters of the GM, i.e., a width, peak height, and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Quaternary Ammonium Compounds: Bioaccumulation Potentials in Humans and Levels in Blood before and during the Covid-19 Pandemic.

Quaternary ammonium compounds (QACs) are commonly used in a variety of consumer, pharmaceutical, and medical products. In this study, bioaccumulation potentials of 18 QACs with alkyl chain lengths of C8-C18 were determined in the in vitro-in vivo extrapolation (IVIVE) model using the results of human hepatic metabolism and serum protein binding experiments. The slowest in vivo clearance rates were estimated for C12-QACs, suggesting that these compounds may preferentially build up in blood. The bioaccumulation of QACs was further confirmed by the analysis of human blood (sera) samples (n = 222). Fifteen out of the 18 targeted QACs were detected in blood with the ΣQAC concentrations reaching up to 68.6 ng/mL. The blood samples were collected during two distinct time periods: before the outbreak of the COVID-19 pandemic (2019; n = 111) and during the pandemic (2020, n = 111). The ΣQAC concentrations were significantly higher in samples collected during the pandemic (median 6.04 ng/mL) than in those collected before (median 3.41 ng/mL). This is the first comprehensive study on the bioaccumulation and biomonitoring of the three major QAC groups and our results provide valuable information for future epidemiological, toxicological, and risk assessment studies targeting these chemicals.

Verification of the accuracy of the SIR model in forecasting based on the improved SIR model with a constant ratio of recovery to infection rate by comparing with monitored second wave data.

The temporal evolution of second and subsequent waves of epidemics such as Covid-19 is investigated. Analytic expressions for the peak time and asymptotic behaviours, early doubling time, late half decay time, and a half-early peak law, characterizing the dynamical evolution of number of cases and fatalities, are derived, where the pandemic evolution exhibiting multiple waves is described by the semi-time SIR model. The asymmetry of the epidemic wave and its exponential tail are affected by the initial conditions, a feature that has no analogue in the all-time SIR model. Our analysis reveals that the immunity is very strongly increasing in several countries during the second Covid-19 wave. Wave-specific SIR parameters describing infection and recovery rates we find to behave in a similar fashion. Still, an apparently moderate change of their ratio can have significant consequences. As we show, the probability of an additional wave is however low in several countries due to the fraction of immune inhabitants at the end of the second wave, irrespective of the ongoing vaccination efforts. We compare with alternate approaches and data available at the time of submission. Most recent data serves to demonstrate the successful forecast and high accuracy of the SIR model in predicting the evolution of pandemic outbreaks as long as the assumption underlying our analysis, an unchanged situation of the distribution of variants of concern and the fatality fraction, do not change dramatically during a wave. With the rise of the α variant at the time of submission the second wave did not terminate in some countries, giving rise to a superposition of waves that is not treated by the present contribution.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia.

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

Coronavirus Disease 2019: Considerations for Health Technology Assessment From the National Centre for Pharmacoeconomics Review Group.

It is expected that the coronavirus disease 2019 (COVID-19) pandemic will leave large deficits in the budgets of many jurisdictions. Funding for other treatments, in particular new treatments, may become more constrained than previously expected. Therefore, a robust health technology assessment (HTA) system is vital. Many clinical trials carried out during the pandemic may have been temporarily halted, while others may have had to change their protocols. Even trials that continue as normal may experience external changes as other aspects of the healthcare service may not be available to the patients in the trial, or the patients themselves may contract COVID-19. Consequently, many limitations are likely to arise in the provision of robust HTAs, which could have profound consequences on the availability of new treatments. Therefore, the National Centre for Pharmacoeconomics Review Group wishes to discuss these issues and make recommendations for applicants submitting to HTA agencies, in ample time for these HTAs to be prepared and assessed. We discuss how the pandemic may affect the estimation of the treatment effect, costs, life-years, utilities, discontinuation rates, and methods of evidence synthesis and extrapolation. In particular, we note that trials conducted during the pandemic will be subject to a higher degree of uncertainty than before. It is vital that applicants clearly identify any parameters that may be affected by the pandemic. These parameters will require considerably more scenario and sensitivity analyses to account for this increase in uncertainty.