Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing.

Over the past few months, the outbreak of Coronavirus disease (COVID-19) has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and policy-makers to make better decisions. We collect the United States COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them. To obtain reliable data for further analysis, we first examine the cyclical pattern and the following anomalies, which frequently occur in the reported cases: (1) the order dependencies violation, (2) the point or period anomalies, and (3) the issue of reporting delay. To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. In addition, we integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environmental information, which are also essential for understanding the spread of the virus.

"Just right" combinations of adjuvants with nanoscale carriers activate aged dendritic cells without overt inflammation.

BACKGROUND: The loss in age-related immunological markers, known as immunosenescence, is caused by a combination of factors, one of which is inflammaging. Inflammaging is associated with the continuous basal generation of proinflammatory cytokines. Studies have demonstrated that inflammaging reduces the effectiveness of vaccines. Strategies aimed at modifying baseline inflammation are being developed to improve vaccination responses in older adults. Dendritic cells have attracted attention as an age-specific target because of their significance in immunization as antigen presenting cells that stimulate T lymphocytes. RESULTS: In this study, bone marrow derived dendritic cells (BMDCs) were generated from aged mice and used to investigate the effects of combinations of adjuvants, including Toll-like receptor, NOD2, and STING agonists with polyanhydride nanoparticles and pentablock copolymer micelles under in vitro conditions. Cellular stimulation was characterized via expression of costimulatory molecules, T cell-activating cytokines, proinflammatory cytokines, and chemokines. Our results indicate that multiple TLR agonists substantially increase costimulatory molecule expression and cytokines associated with T cell activation and inflammation in culture. In contrast, NOD2 and STING agonists had only a moderate effect on BMDC activation, while nanoparticles and micelles had no effect by themselves. However, when nanoparticles and micelles were combined with a TLR9 agonist, a reduction in the production of proinflammatory cytokines was observed while maintaining increased production of T cell activating cytokines and enhancing cell surface marker expression. Additionally, combining nanoparticles and micelles with a STING agonist resulted in a synergistic impact on the upregulation of costimulatory molecules and an increase in cytokine secretion from BMDCs linked with T cell activation without excessive secretion of proinflammatory cytokines. CONCLUSIONS: These studies provide new insights into rational adjuvant selection for vaccines for older adults. Combining appropriate adjuvants with nanoparticles and micelles may lead to balanced immune activation characterized by low inflammation, setting the stage for designing next generation vaccines that can induce mucosal immunity in older adults.