Evaluating the cost implications of integrating SARS-CoV-2 genome sequencing for infection prevention and control investigation of nosocomial transmission within hospitals.

BACKGROUND: The COG-UK hospital-onset COVID-19 infection (HOCI) trial evaluated the impact of SARS-CoV-2 whole-genome sequencing (WGS) on acute infection, prevention, and control (IPC) investigation of nosocomial transmission within hospitals. AIM: To estimate the cost implications of using the information from the sequencing reporting tool (SRT), used to determine likelihood of nosocomial infection in IPC practice. METHODS: A micro-costing approach for SARS-CoV-2 WGS was conducted. Data on IPC management resource use and costs were collected from interviews with IPC teams from 14 participating sites and used to assign cost estimates for IPC activities as collected in the trial. Activities included IPC-specific actions following a suspicion of healthcare-associated infection (HAI) or outbreak, as well as changes to practice following the return of data via SRT. FINDINGS: The mean per-sample costs of SARS-CoV-2 sequencing were estimated at £77.10 for rapid and £66.94 for longer turnaround phases. Over the three-month interventional phases, the total management costs of IPC-defined HAIs and outbreak events across the sites were estimated at £225,070 and £416,447, respectively. The main cost drivers were bed-days lost due to ward closures because of outbreaks, followed by outbreak meetings and bed-days lost due to cohorting contacts. Actioning SRTs, the cost of HAIs increased by £5,178 due to unidentified cases and the cost of outbreaks decreased by £11,246 as SRTs excluded hospital outbreaks. CONCLUSION: Although SARS-CoV-2 WGS adds to the total IPC management cost, additional information provided could balance out the additional cost, depending on identified design improvements and effective deployment.

Factors Influencing Covid-19 Risk: Insights from Molnupiravir Exposure-Response

Background: Exposure-response (E-R) models were developed for the primary endpoint of hospitalization or death in COVID-19 patients from the Phase 3 portion of the MOVe-OUT study (Clinicaltrials.gov NCT04577797). Beyond dose, these models can identify other determinants of response, highlight the relationship of virologic response with clinical outcomes, and provide a basis for differential efficacy across trials. Method(s): Logistic regression models were constructed using a multi-step process with influential covariates identified first using placebo arm data only. Subsequently the assessment of drug effect based on drug exposure was determined using placebo and molnupiravir (MOV) arm data. To validate the models, the rate of hospitalization/death was predicted for published studies of COVID-19 treatment. All work was performed using R Version 3.0 or later. Result(s): A total of 1313 participants were included in the E-R analysis, including subjects having received MOV (N=630) and placebo (N=683). Participants with missing baseline RNA or PK were excluded (79 from MOV and 16 from placebo arms). The covariates shown to be significant determinants of response were baseline viral load, baseline disease severity, age, weight, viral clade, and co-morbidities of active cancer and diabetes. Day 5 and Day 10 viral load were identified as strong on-treatment predictors of hospitalization/death, pointing to sustained high viral load as driving negative outcomes. Estimated AUC50 was 19900 nM*hr with bootstrapped 95% C.I. of (9270, 32700). In an external validation exercise based on baseline characteristics, the E-R model predicted the mean (95% CI) placebo hospitalization rates across trials of 9.3% (7.6%, 11.7%) for MOVe-OUT, 7.2% (5.3%, 9.8%) for the nirmatrelvir/ritonavir EPIC-HR trial, and 3.2% (1.9%, 5.5%) for generic MOV trials by Aurobindo and Hetero, consistent with the differing observed placebo rates in these trials. The relative reduction in hospitalization/death rate predicted with MOV treatment (relative to placebo) also varied with the above patient populations. Conclusion(s): Overall, the exposure-response results support the MOV dose of 800 mg Q12H for treatment of COVID-19. The results further support that many clinical characteristics impacted hospitalization rate beyond drug exposures which can vary widely across studies. These characteristics also influenced the magnitude of relative risk reduction achieved by MOV in the MOVe-OUT study.

Immune-Viral Dynamics Modeling of the Basis for Individual Variation in Covid-19

Background: Viral dynamics models provide mechanistic insights into viral disease and therapeutic interventions. A detailed, mechanistic model of COVID-19 was developed and fit to data from molnupiravir (MOV) trials to characterize the SARS-CoV-2 viral dynamics in MOV-treated and untreated participants and describe the basis for variation across individuals. Method(s): An Immune-Viral Dynamics Model (IVDM) incorporating mechanisms of viral infection, viral replication, and induced innate and adaptive immune response described the dynamics of viral load (VL) from pooled data from MOV Phase 2 and 3 trials (N=1958). Population approaches were incorporated to estimate variation across individuals and to conduct an extensive covariate analysis. Nineteen parameters in a system of five differential equations described SARS-CoV-2 viral dynamics in humans. Six population parameters were successfully informed through fitting to observed trial data while the remaining parameters were fixed based on literature values or calibrated via sensitivity analysis. Result(s): Final viral dynamics and immune response parameters were all estimated with high certainty and reasonable inter-individual variabilities. The model captured the viral load profiles across a wide range of subpopulations and predicted lymphocyte dynamics without using this data to inform the parameters, suggesting inferred immune response curves from this model were accurate. This mechanistic representation of COVID-19 disease indicated that the processes of cellular infection, viral production, and immune response are in a time-varying, non-equilibrium state throughout the course of infection. MOV mechanism of action was best described as an inhibitory process on the infectivity term with estimated AUC50 of 10.5 muM*hr. Covariates identified included baseline viral load on infectivity and age, baseline disease severity, viral clade, baseline viral load, and diabetes on immune response parameters. Greater variation was identified for immune parameters than viral kinetic parameters. Conclusion(s): These findings show that the variation in the human response (e.g., immune response) is more influential in COVID-19 disease than variations in the virus kinetics. The model indicates that immunocompromised patients (due to HIV, organ transplant, active cancer, immunosuppressive therapies) develop an immune response to SARS-CoV-2, albeit more slowly than in immunocompetent, and MOV is effective in further reducing viral loads in the immunocompromised.

Post-pandemic Economic Transformations in the United States of America

The COVID-19 pandemic has impacted economic activity not only in the United States, but across the globe. Lockdown and travel restrictions imposed by local authorities have led to change in customer preferences and thus transformation of economic activity from traditional areas to new regions. While most changes have been temporary and short term, some of them have been observed to be of permanent nature. Using large-scale aggregated and anonymized transaction data across various socio-economic groups, we analyse and discuss such temporary relocation of citizens' economic activities in metropolitan areas of 15 states in the US. The results of this study have extensive implications for urban planners and business owners, and can provide insights into the temporary relocation of economic activities resulting from an extreme exogenous shock like the COVID-19 pandemic. © 2022 IEEE.

Molnupiravir Exposure-Response Relationships for Clinical Outcomes, Virology, and Mechanism of Action Biomarkers Are Consistent in Treatment of Covid-19

BACKGROUND: Exposure-response (E-R) analysis supported molnupiravir phase 3 dose selection based on viral load (VL) and mechanism of action (MOA) markers from phase 2.1 This analysis evaluated how well these biomarkers predict the E-R for hospitalization or death in phase 3. METHOD(S): The following E-R models were developed and compared: (1) logistic regression of the primary outcome (hospitalization or death) from phase 3, (2) VL change from baseline (CFB) from phase 2 and 3, and (3) low frequency nucleotide substitutions (LNS), a measure of MOA, from phase 2. Individual estimates of exposure were derived from population PK modeling of sparse samples collected in all patients. All work was performed using R v3.0 or later. RESULT(S): All E-R relationships were best represented by an Emax model with AUC50 estimates of 19,900, 10,260, and 4,390 nM*hr for hospitalization, day 5 VL CFB, and LNS mutation rate, respectively. Normalized E-R relationships were overlaid, illustrating consistency in E-R shape (Figure). Plasma NHC AUC0-12 was identified as the PK driver. Patients at 800 mg achieved near maximal response. CONCLUSION(S): E-R results support the dose of 800 mg Q12H for treatment of COVID-19. E-R relationships for MOA and virology biomarkers were consistent with the clinical E-R. (Figure Presented).

Molnupiravir Exposure-Response of Clinical Outcomes in Treatment of Covid-19

BACKGROUND: The goal of this analysis was to investigate the relationship of molnupiravir pharmacokinetics (PK) and clinical outcomes (primary endpoint of hospitalization or death) in patients with COVID-19 in the phase 3 cohort of MOVe-OUT (clinicaltrials.gov NCT04577797).1 METHODS: Logistic regression models were constructed using a multi-step process with influential covariates identified first using placebo arm data only and subsequently assessment of drug effect as a function of exposures evaluated using placebo and MOV arm data. Individual estimates of exposure were derived from population PK modeling of sparse samples collected in all patients. All work was performed using R v3.0 or later. RESULT(S): A total of 1,313 participants were included in the exposure-response (E-R) analysis, including subjects on MOV (N = 630) and placebo (N = 683). Participants with missing PK or baseline RNA were excluded (79 from MOV and 16 from placebo arms). The covariates shown to be significant determinants of response were baseline viral load, baseline disease severity, age, weight, viral clade, active cancer, and diabetic risk factors. An additive AUC-based Emax model with a fixed hill coefficient of 1 best represented exposure-dependency in drug effect. Estimated AUC50 was 19,900 nM*hr with bootstrapped 95% confidence interval of (9,270, 32,700). Patients at 800 mg achieved near maximal response, which was larger than the response projected for 200 or 400 mg. CONCLUSION(S): Overall, the E-R results support the MOV dose of 800 mg Q12H for treatment of COVID-19. Many patient characteristics, beyond drug exposures, impacted the risk of hospitalization or death.

Post-COVID-19 elective surgery and transformation: exploring the role of ambulatory spinals in the high-risk patient population

At Whipps Cross Hospital, multi-morbid (high-risk) patients undergoing urological surgery are routinely listed on the surgical inpatient pathway. The 'Getting it right first time' [1] review of anaesthesia recommended day-case surgery as the default for suitable procedures, to help with waiting lists as well as to provide patients with a safe environment. To improve patient choice and postoperative outcomes, an ambulatory spinal pathway was piloted. Methods An earlier scoping exercise identified a pool of urology high-risk patients who could potentially benefit from an ambulatory spinal pathway. Based on this, prilocaine use for ambulatory spinal anaesthetic was provisionally approved by the drugs and therapeutic committee. A pilot ambulatory pathway was put in place, which helped identify suitable patients. The pilot pathway was limited to a select group of anaesthetists to minimise variations. Postoperatively, patients were followed up at 3 and 24 h and assessed for postoperative nausea, vomiting, pain, mobilisation, neurological symptoms and cognitive impairment. Results The total number of patients was 19. Mean ASA was 2.9. Average age was 74 years. The mean dose of hyperbaric prilocaine 2% used was 2.9 ml, 21% of cases utilised additional intrathecal additives. Regarding intra-operative analgesia, only paracetamol was used in 15% of cases. There were no conversions to general anaesthetic. The most common procedure was a cystoscopy with or without biopsy (42%). With comorbidities, diabetes mellitus was the most common (58%), followed by cardiac disease (53%) and respiratory disease (42%). At 3 h, 100% of patients were eating and all sensation had returned, 0% had cognitive impairment, 47% were sitting out and 42% mobilising. Sixteen per cent had hypotension and 5% had pain at rest. At 24 h, 0% had cognitive impairment, 50% had required analgesia and 84% were mobilising. All patients reported they would have a spinal anaesthetic again in the future. Discussion With an ageing population, who have multiple comorbidities, there is huge benefit regarding providing the choice of a spinal anaesthetic rather than general anaesthetic, which allows patients to go home the same day. This will not only provide financial savings to the service provider but also help clear the backlog of surgeries due to the COVID-19 pandemic and enhance patient recovery.

Glycemic Control Metrics using Flash Glucose Monitoring in People with Type 2 Diabetes: Three Year Trends in Real Life Clinical Setting

Objective: Flash Continuous Glucose Monitoring (flash CGM) has been rapidly accepted in real life clinical setting. Methods: We conducted a cross sectional study across two centres, delivering the similar standard of care, over three years (n=362), in patients who utilised FreeStyle Libre Pro CGM to understand glycemic metrics and variability. The key glycemic metrics;TIR, Time Below Range (TBR), Time Above Range (TAR), estimated HbA1c, average glucose was analysed. Descriptive statistics, Pearson r and ANOVA were utilised for analysis. Results: Overall, in total 24.8% (90/362) were in TIR >70%, with 14.7% (18/122) patients in 2018, 17.6% (30/170) in 2019 and 60% (42/70) in 2020. In total 37% (134/362) were in TAR < 25%, 29.5% (36/122) in 2018, 28.2% (48/170) in 2019 and 71.4% (50/70) in 2020. In total 45.3% (164/362) were in TBR < 4%, 44.2% (54/122) in 2018, 46.4% (79/170) in 2019 and 44.2% (31/70) in 2020. Overall, 9.3% (34/362) achieved all three metrices (TIR >70%, TAR < 25%, TBR < 4%), with 4.9% (6/122) in 2018, 7.6% (13/170) in 2019, 24.2 (17/70) in 2020. There was a significant negative correlation between the eHbA1c and TIR (Pearson r – 0.74, 95% CI -0.79 to -0.69, p < 0.0001). There was significant improvement in TIR and TAR over three years. The eHbA1c (6.5%) and average glucose (139.7mg/dl) were lowest in the year 2020, which were comparable with values in previous years. Lesser hypoglycaemic events were noticed in CGM. (figure). [Formula presented] Discussion/Conclusion: There was a significant change in the glycemic metrics. We attribute the remarkable improvement, over three years, to the better awareness in the patients to manage diabetes, greater adoption of guideline directed, contemporary therapeutics including SGLT2 inhibitors, advanced insulins. This coincided with the COVID-19 induced fear of mortality and lockdown led better metabolic health, that resulted in better self-care of diabetes.

Facemask Detection to Prevent COVID-19 Using YOLOv4 Deep Learning Model

The on-going global Covid-19 pandemic has impacted everyone's life. World Health organization (WHO) and Governments all over world have found that social distancing and donning a mask in public places has been instrumental in reducing the rate of COVID-19 transmission. Stepping out of homes in a face mask is a social obligation and a law mandate that is often violated by people and hence a face mask detection model that is accessible and efficient will aid in curbing the spread of disease. Detecting and identifying a face mask on an individual in real time can be a daunting and challenging task but using deep learning and computer vision, establish tech-based solutions that can help combat COVID-19 pandemic. In this paper, YOLOv4 deep learning model is designed and applied deep transfer learning approach to create a face mask detector which can be used in real time. GPU used was Google Collab to run the simulations and to draw inferences. Proposed implementation considered three types of data as input such as image dataset, video dataset and real time data for face mask detection. Performance parameters are tabulated and obtained mean average precision of 0.86, F1 score 0.77 for image dataset, 90 % accuracy for video dataset. And real time face mask detector with accuracy of 95%, it is successfully able to identify a person with and without facemask and report if they are wearing a face mask or not. © 2022 IEEE.

Cross-Species Translation of Correlates of Protection for COVID-19 Vaccine Candidates Using Quantitative Tools

Background. Several COVID-19 vaccines have been authorized, and the need for rapid, further modification is anticipated. This work uses a Model-Based Meta-Analysis (MBMA) to relate, across species, immunogenicity to peak viral load (VL) after challenge and to clinical efficacy. Together with non-clinical and/or early clinical immunogenicity data (ECID), this enables prediction of a candidate vaccine's clinical efficacy. The goal of this work was to enable the accelerated development of vaccine candidates by supporting Go/No-Go and study design decisions, and the resulting MBMA can be instrumental in decisions not to progress candidates to late stage development. Methods. A literature review with pre-specified inclusion/exclusion criteria enabled creation of a database including nonclinical serum neutralizing titers (SN), peak VL after challenge with SARS-CoV-2 (VL), along with data from several clinical vaccine candidates. Rhesus Macaque (RM) and golden hamster (GH) were selected (due to availability and consistency of data) for MBMA modeling. For both RM and GH, peak post-challenge VL in lung and nasal tissues were used as surrogates for clinical disease and were related to pre-challenge SN via the MBMA. The VL predictions from the RM MBMA were scaled to incidence rates in humans, with a scaling factor between RM and human SN estimated using early Phase 3 efficacy data. This enabled clinical efficacy predictions based on ECID. To qualify the model's predictive power, efficacies of COVID-19 vaccine candidates were compared to those predicted from the MBMA and their respective Ph1/2 SN data. More recently available clinical data enable building a clinical MBMA;comparing this to the RM MBMA further supports SN as predictive. Results. The MBMA analyses identified a sigmoidal decrease in VL (increasing protection) with increase in SN in all three species, with more SN needed (in both RM and GH) for protection in nasal swabs than in BAL (see figure). The comparison between predicted and reported clinical efficacies demonstrated the model's predictive power across vaccine platforms. RM and GH MBMA Protection Models and Translational Prediction with Observed Efficacies Sizes of circles indicate relative weight of the data in the respective quantitative model. Model and data visualizations have been harmonized (across tissue-types) separately for each of RM and GH using VACHER (Lommerse, et al., CPT:PSP, in press). Conclusion. By quantifying adjustments needed between species and assays, translational MBMA can inform development decisions by using nonclinical SN and VL, and ECID to predict protection from COVID-19.

Sentiment Analysis on COVID-19 Twitter Data

Corona Virus or COVID-19 first appeared in December, 2019 in Wuhan, China. People tweeted aggressively on twitter at that time. This paper analysed the tweets regarding COVID-19 from November, 2019 to May, 2020 in India and its affect. All tweets are categorized into 3 categories(Positive, Negative and Neutral). Multiple datasets are created State-wise, Month-wise, combined of all states to analyze the people's reactions towards Lockdown in June, 2020 and about everything related to COVID-19. Most people started having Negative tweets but with increasing time people shifted towards positive and neutral comments. In April, 2020 most comments were Positive and about winning against Corona virus. © 2020 IEEE.