Practical considerations for measuring the effective reproductive number, Rt.

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

A comparative study of p53 expression in hyperplastic, dysplastic epithelium and oral squamous cell carcinoma

Aim: To study oral hyperplastic epithelium, dysplastic epithelium and squamous cell carcinoma to determine (1) the prevalence of p53 protein immunoreactivity, (2) number of p53 positive cells, and (3) the area of localization of p53 protein immunoreactivity. Methods: Two contiguous sections from 30 tissue specimens (10 each from oral hyperplastic epithelium, dysplastic epithelium and squamous cell carcinoma) were subjected to hematoxylin and eosin (H/E) staining for histopathological diagnosis and immunohistochemical (IHC) staining for demonstration of p53. p53 positivity was looked for in each IHC stained slide and the number of positive cells amongst 1,000 epithelial cells were recorded. The localization of these p53 positive cells within the strata (i.e. basal/suprabasal, spinous and superficial layers) of epithelium between 3 groups, and also within each group according to histological grades was recorded. Results: Higher p53 positive cell counts were demonstrated in oral squamous cell carcinoma compared to hyperplastic and dysplastic tissues. The expression of p53 in epithelial hyperkeratosis was mainly localized to basal epithelial cells whereas in epithelial dysplasia, it was predominantly localized to spinous epithelial cells. Conclusions: Qualitatively p53 is not a specific marker for malignancy of oral epithelium. However the quantitative analysis of p53 positive cells and their localization in oral epithelium is of importance as a marker for oral squamous cell carcinoma

A comparative study of p53 expression in hyperplastic , dysplastic epithelium and oral squamous cell carcinoma

study oral hyperplastic epithelium, dysplastic epithelium and squamous cell carcinoma to determine (1) the prevalence of p53 protein immunoreactivity, (2) number of p53 positive cells, and (3) the area of localization of p53 protein immunoreactivity. Methods: Two contiguous sections from 30 tissue specimens (10 each from oral hyperplastic epithelium, dysplastic epithelium and squamous cell carcinoma) were subjected to hematoxylin and eosin (H/E) staining for histopathological diagnosis and immunohistochemical (IHC) staining for demonstration of p53. p53 positivity was looked for in each IHC stained slide and the number of positive cells amongst 1,000 epithelial cells were recorded. The localization of these p53 positive cells within the strata (i.e. basal/suprabasal, spinous and superficial layers) of epithelium between 3 groups, and also within each group according to histological grades was recorded. Results: Higher p53 positive cell counts were demonstrated in oral squamous cell carcinoma compared to hyperplastic and dysplastic tissues. The expression of p53 in epithelial hyperkeratosis was mainly localized to basal epithelial cells whereas in epithelial dysplasia, it was predominantly localized to spinous epithelial cells. Conclusions: Qualitatively p53 is not a specific marker for malignancy of oral epithelium. However the quantitative analysis of p53 positive cells and their localization in oral epithelium is of importance as a marker for oral squamous cell carcinoma

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19

Funding: National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award number T32AI007535), the National Institute of General Medical Sciences of the National Institutes of Health (award number U54GM088558), the Morris-Singer Fund, and the National Institutes of Health (cooperative agreement U01 CA261277).In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.Publisher PDFPeer reviewe

Gaussian Accelerated Molecular Dynamics in OpenMM

Gaussian accelerated molecular dynamics (GaMD) is a computational technique that provides both unconstrained enhanced sampling and free energy calculations of biomolecules. Here, we present the implementation of GaMD in the OpenMM simulation package and validate it on model systems of alanine dipeptide and RNA folding. For alanine dipeptide, 30 ns GaMD production simulations reproduced free energy profiles of 1000 ns conventional molecular dynamics (cMD) simulations. In addition, GaMD simulations captured the folding pathways of three hyperstable RNA tetraloops (UUCG, GCAA, and CUUG) and binding of the rbt203 ligand to the HIV-1 Tar RNA, both of which involved critical electrostatic interactions such as hydrogen bonding and base stacking. Together with previous implementations, GaMD in OpenMM will allow for wider applications in simulations of proteins, RNA, and other biomolecules

The Potential Economic Impact of the Updated COVID-19 mRNA Fall 2023 Vaccines in Japan

This analysis estimates the economic and clinical impact of a Moderna updated COVID-19 mRNA Fall 2023 vaccine for adults ≥18 years in Japan. A previously developed Susceptible-Exposed-Infected-Recovered (SEIR) model with a one-year analytic time horizon (September 2023–August 2024) and consequences decision tree were used to estimate symptomatic infections, COVID-19 related hospitalizations, deaths, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER) for a Moderna updated Fall 2023 vaccine versus no additional vaccination, and versus a Pfizer–BioNTech updated mRNA Fall 2023 vaccine. The Moderna vaccine is predicted to prevent 7.2 million symptomatic infections, 272,100 hospitalizations and 25,600 COVID-19 related deaths versus no vaccine. In the base case (healthcare perspective), the ICER was ¥1,300,000/QALY gained ($9400 USD/QALY gained). Sensitivity analyses suggest results are most affected by COVID-19 incidence, initial vaccine effectiveness (VE), and VE waning against infection. Assuming the relative VE between both bivalent vaccines apply to updated Fall 2023 vaccines, the base case suggests the Moderna version will prevent an additional 1,100,000 symptomatic infections, 27,100 hospitalizations, and 2600 deaths compared to the Pfizer–BioNTech vaccine. The updated Moderna vaccine is expected to be highly cost-effective at a ¥5 million willingness-to-pay threshold across a wide range of scenarios

How to detect and reduce potential sources of biases in epidemiologic studies of SARS-CoV-2

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility