Research during covid-19 pandemic: Perspectives from the ethics committees of a lower middle income country

The pandemic of Coronavirus Disease 2019 (COVID-19) has created paradoxically a good opportunity globally to conduct research in the field of health and social science, and a Lower Middle-Income Country (LMIC) like Nepal is not an exception in this regard. During this ongoing pandemic, the Ethical Review Board (ERB) of Nepal Health Research Council (NHRC) has received numerous research proposals regarding COVID-19. As its main responsibility is to ensure participants' safety, at the same time maintaining the scientific standard of research, the ERB has meticulously gone through all the proposals received so far. During this situation of a health emergency, the ERB of NHRC has had a different experience compared to the usual time. Its strength, weakness, opportunities, and threats have been like never before. Copyright © 2020, Kathmandu University. All rights reserved.

COVID-19 HOSPITALIZATION PATTERNS THROUGHOUT THE PANDEMIC ACROSS 10 US DEPARTMENT OF HEALTH AND HUMAN SERVICES (HHS) REGIONS

SESSION TITLE: COVID-19 Infections: Issues During and After Hospitalization SESSION TYPE: Original Investigations PRESENTED ON: 10/17/2022 01:30 pm - 02:30 pm PURPOSE: COVID-19 pandemic is well studied, but it’s impact on hospitalization pattern is still unclear. We aim to study the hospitalizations pattern throughout the COVID-19 pandemic across 10 US Health and Human Services (HHS) regions. METHODS: This study was conducted using two publically and freely available databases;1. The COVID Tracking Project- manually aggregated data from available sources from official, public state government sites, and 2. The US Department of HHS – state wise patient impact and hospital capacity data. The state wise hospitalization data was extracted and collated by noting hospitalization for the complete time range (from March 1, 2020 to March 7th, 2021) for dataset-1 and data reported between the dates of March 7th, 2021, to March 12th, 2022, for dataset-2. The HHS wise regional hospitalization data was then calculated by adding the respective daily state statistics and scaled to per 100,000 population. A 7-day moving average filter was finally applied to the data before visualization and analysis, to account for repeated days of missing recordings in the data sources. No patient and hospital identifiers were utilized;thus, study was IRB exempted. RESULTS: Based on proximity of the spikes in each wave, data visualization tools grouped, HHS regions 1, 2, 3, 5 in group A;regions 4, 6, 9 in group B, and regions 7, 8. 10 in group C. The visualization of data determined total 6 spikes till date. The start and end of spikes were determined by placing a threshold (10 cases per day per 100,000 population) on the number of daily hospitalizations. The spikes were further divided when a given start/end date pair has multiple clear peaks. Maximum number of days difference observed between the occurrence of COVID-19 peaks in number of hospitalizations, were 47 days for spike-3 for HHS regions in group A (Compared to 2 and 12 days in group B and C, respectively). For Spike-5 it was highest in group C as 78 days (compared to 18 and 1 day in group A and B, respectively). CONCLUSIONS: In a latest COVID-19 hospitalization data analysis, after normalization of data, states in HHS regions, 4, 6, and 9 have the closest spikes throughout the pandemic. These regions included three most populous states of US (Florida, Texas, California) among others and consisted of 67M (region 4), 42M (region 6) and 51M (region 9) people, total of roughly 50% US population. CLINICAL IMPLICATIONS: The result of this study, first to be presented at CHEST conference will pave the way in adding to public health policy discussion in preparedness and resources allocations for hospitalized patients. A subset-analysis of ICU admission is underway, which will be included at CHEST meeting presentation. DISCLOSURES: No relevant relationships by Ramesh Adhikari no disclosure on file for Keerti Deepika;No relevant relationships by Taru Dutt No relevant relationships by Rahul Kashyap No relevant relationships by Arjun Rajasekar no disclosure on file for Shruti Srivnivasan;No relevant relationships by Salim Surani

Comparative pathology, molecular pathogenicity, immunological features, and genetic characterization of three highly pathogenic human coronaviruses (MERS-CoV, SARS-CoV, and SARS-CoV-2).

The last two decades have witnessed the emergence of three deadly coronaviruses (CoVs) in humans: severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There are still no reliable and efficient therapeutics to manage the devastating consequences of these CoVs. Of these, SARS-CoV-2, the cause of the currently ongoing coronavirus disease 2019 (COVID-19) pandemic, has posed great global health concerns. The COVID-19 pandemic has resulted in an unprecedented crisis with devastating socio-economic and health impacts worldwide. This highlights the fact that CoVs continue to evolve and have the genetic flexibility to become highly pathogenic in humans and other mammals. SARS-CoV-2 carries a high genetic homology to the previously identified CoV (SARS-CoV), and the immunological and pathogenic characteristics of SARS-CoV-2, SARS-CoV, and MERS contain key similarities and differences that can guide therapy and management. This review presents salient and updated information on comparative pathology, molecular pathogenicity, immunological features, and genetic characterization of SARS-CoV, MERS-CoV, and SARS-CoV-2; this can help in the design of more effective vaccines and therapeutics for countering these pathogenic CoVs.

Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19.

Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and the surveillance process through which data are acquired. We present a Bayesian inference methodology that quantifies these uncertainties, for epidemics that are modelled by (possibly) non-stationary, continuous-time, Markov population processes. The efficiency of the method derives from a functional central limit theorem approximation of the likelihood, valid for large populations. We demonstrate the methodology by analysing the early stages of the COVID-19 pandemic in the UK, based on age-structured data for the number of deaths. This includes maximum a posteriori estimates, Markov chain Monte Carlo sampling of the posterior, computation of the model evidence, and the determination of parameter sensitivities via the Fisher information matrix. Our methodology is implemented in PyRoss, an open-source platform for analysis of epidemiological compartment models.

Research during COVID-19 pandemic: perspectives from the ethics committees of a lower middle income country

The pandemic of Coronavirus Disease 2019 (COVID-19) has created paradoxically a good opportunity globally to conduct research in the field of health and social science, and a Lower Middle-Income Country (LMIC) like Nepal is not an exception in this regard. During this ongoing pandemic, the Ethical Review Board (ERB) of Nepal Health Research Council (NHRC) has received numerous research proposals regarding COVID-19. As its main responsibility is to ensure participants' safety, at the same time maintaining the scientific standard of research, the ERB has meticulously gone through all the proposals received so far. During this situation of a health emergency, the ERB of NHRC has had a different experience compared to the usual time. Its strength, weakness, opportunities, and threats have been like never before.

Binding of SARS-CoV-2/SARS-CoV spike protein with human ACE2 receptor

SARS-CoV-2 virus is the serious health concern throughout the world. A comprehensive investigation of binding of SARS-CoV-2 active site with host receptor protein hACE2 is important in designing effective drugs. In the present work, the major amino acid binding partners between the virus CTD and host receptor have been studied and are compared with SARS-CoV RBD binding with hACE2. Our investigation show that some unique hydrogen bond pairs which were not reported in previous work. Along with hydrogen bonding, salt-bridges, hydrophobic interactions and contributions of electrostatic and van der Waals contacts play significant role in binding mechanism. The binding affinity of SARS-CoV-2 CTD/hACE2 is greater than SARS-CoV RBD/hACE2. This outcome is also verified from the free energy estimation by using umbrella sampling.