A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior. Videos and related metadata were downloaded using a third-party TikTok Scraper using the search term #coronavirus. Videos were reviewed for content and data were entered on a spreadsheet.

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 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.