The COVID-19 pandemic severely affected blood collection activities due to social distancing requirements, deferral of blood drives, and other measures to prevent spread of infection. Medical facilities also experienced changes in blood usage patterns with the cancellation of elective surgeries. To evaluate fluctuating blood inventory levels, the University of Maryland Medical Center (UMMC) built an R-based workflow to facilitate rapid and repeatable analysis and visualization of blood usage data extracted from the Cerner Laboratory Information System (LIS). The generation of daily reports by blood product and hospital unit promoted informed decision-making with regard to changing ordering practices to avoid wastage. This dataset consists of 5 R Markdown workflow files and one README file. The workflows will continue to be updated in the GitHub repository as additional R Markdown documents are written to assist in identifying large users, optimize data cleaning and product identification, establish safe inventory levels based on historical and recent usage, and establish web‐based functionality via the shiny pack.

This study uses a computing platform to analyze and estimate the correlation between human mobility and COVID-19 spreading. The infection data are collected from various county, state and national sources and mobile device location data are procured from multiple third-party data providers. To capture the time-varying relationship between the number of infection and mobility inflow, the authors developed a simultaneous equations model with time-varying coefficients. Mobility data can be found in the University of Maryland COVID-19 Impact Analysis Platform. The analysis platform displays the data in a user-friendly interface utilizing a map of the United States and charts that show, under Mobility and Social Distancing, the % of in and out-of-county trips, % of in and out- of-state trips, and others. The information on the map can also be adjusted to show the mobility per state. Codebase, data of infection cases and computed metrics are shared on GitHub. The statistic modeling including data processing, prediction and visualization is written in Python and R.

This observational retrospective control study investigated the development of the humoral immune response to SARS-CoV-2 in convalescent plasma (CCP) recipients (n=34) and compared it to the humoral response in a group of patients not treated with CCP (n=68). Additionally, a separate comparison of clinical outcomes was performed between CCP recipients and a matched control group of untreated patients (n=34). Patients considered for enrollment in the study presented with severe COVID-19 and were hospitalized in the intensive care units (ICU) of 3 Maryland hospitals. Participants received a single unit of ABO compatible CCP of approximately 250mL. Blood samples for SARS-CoV-2 antibody titre measurements were collected immediately pre-transfusion (day 0) and on days 3, 7 and 14 post-transfusion. Non-transfused patients were used for comparison of antibody titres. Sample draws from this cohort ranged from 0 to 48 days after the onset of symptoms, which varied in severity. Non-transfused patients used for the clinical outcome analysis were matched to CCP recipients based on sex, age, and on three levels of respiratory support requirement (non-ventilated, mechanically ventilated and ventilated with extracorporeal membrane oxygenation (ECMO)) and were admitted in the same hospital. This dataset includes clinical variables from all transfused and non-transfused participants including: symptoms at presentation, level of respiratory support (mechanical ventilation/ECMO status), comorbidities, other SARS-CoV-2 directed therapies, 30-days in-hospital mortality, number of days on mechanical ventilation, number of days on ECMO support, ICU length of stay (LOS) and hospital LOS. Clinical improvement was assessed primarily on survival at 30 days. Secondary outcomes included the number of days on ventilatory and/or ECMO respiratory support, LOS in the hospital and LOS in the ICU.

An institutional-based cross-sectional study was conducted to assess the prevalence of self-reported symptoms of depression, anxiety, and stress during the COVID-19 pandemic among 846 healthcare workers (HCWs) in central Ethiopia. From June 25, 2020, to July 25, 2020, an online survey was administered to collect self-reported data using the Depression, Anxiety, and Stress Scale (DASS-21). Data were cleaned, coded, and analysed using SPSS Version 23. A multivariable logistic regression analysis was done to identify the associated factors for mental health outcomes at a p-value of less than 0.05.

An age-stratified agent-based model of COVID-19 was used to simulate outbreaks in states within two U. S. regions. The northeastern region consisted of Connecticut, Massachusetts, Maine, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island and Vermont. The southern region consisted of Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia and West Virginia. The model was calibrated using reported incidence of COVID-19 in each state from October 1, 2020 to August 31, 2021. It then projected the number of infections, hospitalizations, and deaths that would be averted between September 2021 and the end of March 2022, if states increased their daily vaccination rate.

An adapted version of the Brighton Collaboration priority list was used to evaluate serious adverse events (SAE) of special interest observed in mRNA Covid-19 vaccine trials. In December of 2020, reviewers searched journal publications and trial data on the FDA’s and Health Canada’s websites to locate SAE results tables for these trials. For each trial, blinded SAE tables were prepared. Using these blinded SAE tables, two clinician reviewers judged whether each SAE type was an adverse event of special interest (AESI). Risk ratios and risk differences between vaccine and placebo groups were calculated for the incidence of AESIs and SAEs.

"The GISAID Initiative promotes the rapid sharing of data from all influenza viruses and the coronavirus causing COVID-19. This includes genetic sequence and related clinical and epidemiological data associated with human viruses, and geographical as well as species-specific data associated with avian and other animal viruses, to help researchers understand how viruses evolve and spread during epidemics and pandemics. GISAID does so by overcoming disincentive hurdles and restrictions, which discourage or prevented sharing of virological data prior to formal publication." (From "Mission")

This dataset was created to understand the mechanisms through which social capital shaped the community response to the coronavirus pandemic on the island of Menorca, Spain. Data was collected through qualitative interviews of 25 permanent residents of the island between April and June of 2020. The age range of the participants was between 26 to 89 years old. The semi-structured interview guide had questions about pre-pandemic and pandemic emergency response and recovery events. Questions were developed to capture information on different indicators, including emergent forms of social capital, dark sides of social capital and trust.

This study investigates multiple vaccine strategies to enhance immunogenicity and protection against SARS-CoV-2 in aged mice. The study aims to determine the effect of a booster dose, with an emphasis on older age. Female three and eleven-month-old BALB/c mice were studied over the course of 38 weeks. Mice received BNT162b2 SARS-CoV-2 spike mRNA vaccine series, primary vaccination series, and mock Phosphate-buffered saline (PBS) injections. Single mouse experiments aimed to include 20 mice per group. Sample size and age criteria were chosen empirically based on the results of previous studies. Mice were randomly assigned to different treatment groups.

Due to constraints in vaccine supply, this agent-based model of COVID-19 transmission was developed to compare the impact of two vaccination strategies: 1) vaccinate more individuals with the first dose of available vaccines and delay the second dose or 2) to continue with the recommended 2-dose series as tested in clinical trials. Model population was stratified into 6 age groups of 0 to 4, 5 to 19, 20 to 49, 50 to 64, 65 to 79, and 80+ years based on United States census data.