There is an urgent need to answer questions related to COVID-19's clinical course and associations with underlying conditions and health outcomes. Multi-center data are necessary to generate reliable answers, but centralizing data in a single repository is not always possible. Using a privacy-protecting strategy, we launched a public Questions & Answers web portal (https//covid19questions.org) with analyses of comorbidities, medications and laboratory tests using data from 202 hospitals (59,074 COVID-19 patients) in the USA and Germany. We find, for example, that 8.6% of hospitalizations in which the patient was not admitted to the ICU resulted in the patient returning to the hospital within seven days from discharge and that, when adjusted for age, mortality for hospitalized patients was not significantly different by gender or ethnicity. Mail-order prescriptions are popular in the U.S., but the recent mail delays due to operational changes at the United States Postal Services (USPS) may postpone the delivery of vital medications. Despite growing recognition of the health and economic effects of a postal crisis on mail-order pharmacy consumers, little is known about the extent of mail-order prescription use, and most importantly, the population groups and types of medications that will likely be most affected by these postal delays. The prevalence of mail-order prescription use was assessed using a nationally representative repeated cross-sectional survey (the Medical Expenditure Panel Survey) carried out among adults aged 18 and older in each year from 1996 to 2018. We stratified use of mail-order prescription by socio-demographic and health characteristics. Additionally, we calculated which prescription medications were most prevalent among all mailed medications, and for which medications users were most likely to opt for mail-order prenavirus disease 2019 epidemic.Multiple promising COVID-19 vaccines are under rapid development, with deployment of the initial supply expected by 2021. Careful design of a vaccine prioritization strategy across socio-demographic groups is an imminent and crucial public policy challenge given that (1) the eventual vaccine supply will be highly constrained for at least the first several months of the vaccination campaign, and (2) there are stark differences in transmission and severity of impacts from SARS-CoV-2 across groups. https://www.selleckchem.com/MEK.html Previous experience with vaccine development mid-pandemic offers limited insights for SARS-CoV-2 prioritization SARS and Zika vaccine development was incomplete when those outbreaks ended and the epidemiology of endemic human influenza viruses differ substantially from that of SARS-CoV-2. We assess the optimal allocation of a limited and dynamic COVID-19 vaccine supply in the U.S. across socio-demographic groups differentiated by age and essential worker status. The transmission dynamics are modeled using a compartmend vaccination strategies by up to 18% depending on the outcome optimized. For example, in our baseline model, cumulative mortality decreased on average by 17% (25,000 deaths in the U.S. population) over the course of the outbreak. To conduct a systematic review and meta-analysis to assess the prevalence of various clinical symptoms and laboratory findings of COVID-19 in children. PubMed, MEDLINE, and SCOPUS databases were searched to include studies conducted between January 1, 2020, and July 15, 2020 which reported data about clinical characteristics and laboratory findings in laboratory-confirmed diagnosis of COVID-19 in pediatric patients. Random effects meta-analysis using generalized linear mixed models was used to estimate the pooled prevalence. The most prevalent symptom of COVID-19 in children was 46.17% (95%CI 39.18-53.33%), followed by cough (40.15%, 95%CI 34.56-46.02%). Less common symptoms were found to be dyspnea, vomiting, nasal congestion/rhinorrhea, diarrhea, sore throat/pharyngeal congestion, headache, and fatigue. The prevalence of asymptomatic children was 17.19% (95%CI 11.02-25.82%). The most prevalent laboratory findings in COVID-19 children were elevated Creatinine Kinase (26.86%, 95%CI 16.15-41.19%) and neutropenia (25.76%, 95%CI 13.96-42.58%). These were followed by elevated LDH, thrombocytosis, lymphocytosis, neutrophilia, elevated D Dimer, Elevated CRP, elevated ESR, leukocytosis, elevated AST and leukopenia. There was a low prevalence of elevated ALT and lymphopenia in children with COVID- 19. This study provides estimates of the pooled prevalence of various symptoms and laboratory findings of COVID-19 in the pediatric population. This study provides estimates of the pooled prevalence of various symptoms and laboratory findings of COVID-19 in the pediatric population.Rapid diagnostic testing for COVID-19 is key to guiding social distancing orders and containing emerging disease clusters by contact tracing and isolation. However, communities throughout the US do not yet have adequate access to tests. Pharmacies are already engaged in testing, but there is capacity to greatly increase coverage. Using a facility location optimization model and willingness-to-travel estimates from US National Household Travel Survey data, we find that if COVID-19 testing became available in all US pharmacies, an estimated 94% of the US population would be willing to travel to obtain a test, if warranted. Whereas the largest chain provides high coverage in densely populated states, like Massachusetts, Rhode Island, New Jersey, and Connecticut, independent pharmacies would be required for sufficient coverage in Montana, South Dakota, and Wyoming. If only 1,000 pharmacies in the US are selected to provide testing, judicious selection, using our optimization model, provides estimated access to 29 million more people than selecting pharmacies simply based on population density. COVID-19 testing through pharmacies can improve access across the US. Even if only few pharmacies offer testing, judicious selection of specific sites can simplify logistics and improve access. There is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes. Identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. Retrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020. Ensemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes.