and D. Alfandari was supported by grants from the USPHS (RO1DE016289 and R24OD021485).
 UMass Faculty Fund (A Lover); UMass Faculty Discretionary Funds (N Reich); UMass Institute for Applied Life Science "Midigrant" (#169076; A. Lover); and D. Alfandari was supported by grants from the USPHS (RO1DE016289 and R24OD021485).
  Characterizing the kinetics of the antibody response to SARS□CoV□2 is of critical importance to developing strategies that may mitigate the public health burden of COVID-19. We sought to determine how circulating antibody levels change over time following natural infection.
 
  We conducted a prospective, longitudinal analysis of COVID-19 convalescent plasma (CCP) donors at multiple time points over a 9-month period. At each study visit, subjects either donated plasma or only had study samples drawn. In all cases, anti-SARS-CoV-2 donor testing was performed using semi-quantitative chemiluminescent immunoassays (ChLIA) targeting subunit 1 (S1) of the SARS-CoV-2 spike (S) protein, and an in-house fluorescence reduction neutralization assay (FRNA).
 
  From April to November 2020 we enrolled 202 donors, mean age 47.3 ±14.7 years, 55% female, 75% Caucasian. Most donors reported a mild clinical course (91%, n=171) without hospitalization. One hundred and five (105) (52%) donors presented for repeat visits with a medss demographics, with the exception of age, BMI and clinical severity.
  COVID-19 symptoms are increasingly recognized to persist among a subset of individual following acute infection, but features associated with this persistence are not well-understood.
 
  We aimed to identify individual features that predicted persistence of symptoms over at least 2 months at the time of survey completion.Design Non-probability internet survey. Participants were asked to identify features of acute illness as well as persistence of symptoms at time of study completion. We used logistic regression models to examine association between sociodemographic and clinical features and persistence of symptoms at or beyond 2 months.
 
  Ten waves of a fifty-state survey between June 13, 2020 and January 13, 2021.
 
  6,211 individuals who reported symptomatic COVID-19 illness confirmed by positive test or clinician diagnosis.
 
  symptomatic COVID-19 illness.
 
  Among 6,211 survey respondents reporting COVID-19 illness, with a mean age of 37.8 (SD 12.2) years and 45.1% female, 73.9% white, 10.0% Black, 9.9% Hisvelopment of targeted interventions.
  As the coronavirus disease 2019 (COVID-19) pandemic continues and millions remain vulnerable to infection with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), attention has turned to characterizing post-acute sequelae of SARS-CoV-2 infection (PASC).
 
  From April 21 to December 31, 2020, we assembled a cohort of consecutive volunteers who a) had documented history of SARS-CoV-2 RNA-positivity; b) were ≥ 2 weeks past onset of COVID-19 symptoms or, if asymptomatic, first test for SARS-CoV-2; and c) were able to travel to our site in San Francisco. Participants learned about the study by being identified on medical center-based registries and being notified or by responding to advertisements. At 4-month intervals, we asked participants about physical symptoms that were new or worse compared to the period prior to COVID-19, mental health symptoms and quality of life. We described 4 time periods 1) acute illness (0-3 weeks), 2) early recovery (3-10 weeks), 3) late recovery 1 (12-20 weeks), and 4) lamong a cohort of participants enrolled in the post-acute phase of SARS-CoV-2 infection, we found many with persistent physical symptoms through 8 months following onset of COVID-19 with an impact on self-rated overall health.  https://www.selleckchem.com/products/l-arginine-l-glutamate.html  The presence of participants with and without symptoms and ample biological specimens will facilitate study of PASC pathogenesis. Similar evaluations in a population-representative sample will be needed to estimate the population-level prevalence of PASC.
 Among a cohort of participants enrolled in the post-acute phase of SARS-CoV-2 infection, we found many with persistent physical symptoms through 8 months following onset of COVID-19 with an impact on self-rated overall health. The presence of participants with and without symptoms and ample biological specimens will facilitate study of PASC pathogenesis. Similar evaluations in a population-representative sample will be needed to estimate the population-level prevalence of PASC.We analyzed the plasma levels of interferons and cytokines, and the expression of interferon-stimulated genes in peripheral blood mononuclear cells in COVID-19 patients with different disease severity. Mild patients exhibited transient type I interferon responses, while ICU patients had prolonged type I interferon responses with hyper-inflammation mediated by interferon regulatory factor 1. Type II interferon responses were compromised in ICU patients. Type III interferon responses were induced in the early phase of SARS-CoV-2 infection, even in convalescent patients. These results highlight the importance of type I and III interferon responses during the early phase of infection in controlling COVID-19 progression.Timely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model's performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons.