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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. 

Background: Although microalgal biofuels have potential advantages over conventional fossil fuels, high production costs limit their application in the market. We developed bio-flocculation and incubation methods for the marine alga Nannochloropsis oceanica CCMP1779 and the oleaginous fungus Mortierella elongata AG77 resulting in increased oil productivity. Results: Grown separately and then combining the cells the M. elongata mycelium could efficiently capture N. oceanica due to an intricate cellular interaction between the two species leading to bio-flocculation. Use of a high-salt culture medium induced accumulation of triacylglycerol (TAG) and enhanced the content of polyunsaturated fatty acids (PUFAs) including arachidonic acid (ARA) and docosahexaenoic acid (DHA) in M. elongata. To increase TAG productivity in the alga, we developed an effective reduced nitrogen supply regime based on ammonium in environmental photobioreactors (ePBRs). Under optimized conditions, N. oceanica produced high levels of TAG that could be indirectly monitored by following chlorophyll content. Combining N. oceanica and M. elongata to initiate bio-flocculation yielded high levels of TAG and total fatty acids, ~15% and 22% of total dry weight (DW), respectively, as well as high levels of PUFAs. Genetic engineering N. oceanica for higher TAG content in nutrient-replete medium was accomplished by overexpressing DGTT5, a gene encoding the type II acyl-CoA:diacylglycerol acyltransferase 5. Combined with bioflocculation this approach led to increased production of TAG under nutrient replete conditions (~10% of DW) compared to the wild type (~6% of DW). Conclusions: The combined use of M. elongata and N. oceanica with available genomes and genetic engineering tools for both species opens up new avenues to improve bio-fuel productivity and allows the engineering of polyunsaturated fatty acids. Keywords: Microalgae, Filamentous fungi, Flocculation, Cell wall interaction, Biofuel, Nitrogen starvation, Polyunsaturated fatty acid, Triacylglycerol 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.