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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.

Abstract As billions of nocturnal avian migrants traverse North America, twice a year they must contend with landscape changes driven by natural and anthropogenic forces, including the rapid growth of the artificial glow of the night sky. While airspaces facilitate migrant passage, terrestrial landscapes serve as essential areas to restore energy reserves and often act as refugia—making it critical to holistically identify stopover locations and understand drivers of use. Here, we leverage over 10 million remote sensing observations to develop seasonal contiguous United States layers of bird migrant stopover density. In over 70% of our models, we identify skyglow as a highly influential and consistently positive predictor of bird migration stopover density across the United States. This finding points to the potential of an expanding threat to avian migrants: peri-urban illuminated areas may act as ecological traps at macroscales that increase the mortality of birds during migration.

Abstract In this study, we combined a machine learning pipeline and human supervision to identify and label swallow and martin roost locations on data captured from 2000 to 2020 by 12 Weather Surveillance Radars in the Great Lakes region of the US. We employed radar theory to extract the number of birds in each roost detected by our technique. With these data, we set out to investigate whether roosts formed consistently in the same geographic area over two decades and whether consistency was also predictive of roost size. We used a clustering algorithm to group individual roost locations into 104 high‐density regions and extracted the number of years when each of these regions was used by birds to roost. In addition, we calculated the overall population size and analyzed the daily roost size distributions. Our results support the hypothesis that more persistent roosts are also gathering more birds, but we found that on average, most individuals congregate in roosts of smaller size. Given the concentrations and consistency of roosting of swallows and martins in specific areas throughout the Great Lakes, future changes in these patterns should be monitored because they may have important ecosystem and conservation implications.

Abstract Tracking technologies have widely expanded our understanding of bird migration routes, destinations and underlying strategies. However, determining the entire trajectory of small birds equipped with lightweight geolocators remains a challenge. We develop a highly optimized hidden Markov model (HMM) for reconstructing bird trajectories. The observation model is defined by pressure and, optionally, light measurements, while the movement model incorporates wind data to constrain consecutive positions based on realistic airspeeds. To reduce the computational costs associated with a large state space, we prune the HMM states and transitions based on flight and observation constraints to efficiently model the entire trajectory. The approach presented is based on a mathematically exact procedure and is fast to compute. We demonstrate how to compute (1) the most likely trajectory, (2) the marginal probability map of each stationary period, (3) simulated trajectories and (4) the wind conditions (wind support/drift) encountered by the bird during each migratory flight. We construct a version of an HMM optimized for reconstructing a bird's migration trajectory based on lightweight geolocator data. To render this approach easily accessible to researchers, we designed a dedicated R package GeoPressureR (https://raphaelnussbaumer.com/GeoPressureR/).

Abstract Large‐scale monitoring of seasonal animal movement is integral to science, conservation and outreach. However, gathering representative movement data across entire species ranges is frequently intractable. Citizen science databases collect millions of animal observations throughout the year, but it is challenging to infer individual movement behaviour solely from observational data. We present BirdFlow, a probabilistic modelling framework that draws on citizen science data from the eBird database to model the population flows of migratory birds. We apply the model to 11 species of North American birds, using GPS and satellite tracking data to tune and evaluate model performance. We show that BirdFlow models can accurately infer individual seasonal movement behaviour directly from eBird relative abundance estimates. Supplementing the model with a sample of tracking data from wild birds improves performance. Researchers can extract a number of behavioural inferences from model results, including migration routes, timing, connectivity and forecasts. The BirdFlow framework has the potential to advance migration ecology research, boost insights gained from direct tracking studies and serve a number of applied functions in conservation, disease surveillance, aviation and public outreach.

Abstract Limited research exists examining how resistance training to failure affects applied outcomes and single motor unit characteristics in previously trained individuals. Herein, resistance‐trained adults (24 ± 3 years old, self‐reported resistance training experience was 6 ± 4 years, 11 men and 8 women) were randomly assigned to either a low‐repetitions‐in‐reserve (RIR; i.e., training near failure, n = 10) or high‐RIR (i.e., not training near failure, n = 9) group. All participants implemented progressive overload during 5 weeks where low‐RIR performed squat, bench press, and deadlift twice weekly and were instructed to end each training set with 0–1 RIR. high‐RIR performed identical training except for being instructed to maintain 4–6 RIR after each set. During week 6, participants performed a reduced volume‐load. The following were assessed prior to and following the intervention: (i) vastus lateralis (VL) muscle cross‐sectional area (mCSA) at multiple sites; (ii) squat, bench press, and deadlift one‐repetition maximums (1RMs); and (iii) maximal isometric knee extensor torque and VL motor unit firing rates during an 80% maximal voluntary contraction. Although RIR was lower in the low‐ versus high‐RIR group during the intervention (p

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

We propose a general approach for differentially private synthetic data generation, that consists of three steps: (1) select a collection of low-dimensional marginals, (2) measure those marginals with a noise addition mechanism, and (3) generate synthetic data that preserves the measured marginals well. Central to this approach is Private-PGM, a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals. We present two mechanisms, NIST-MST and MST, that are instances of this general approach. NIST-MST was the winning mechanism in the 2018 NIST differential privacy synthetic data competition, and MST is a new mechanism that can work in more general settings, while still performing comparably to NIST-MST. We believe our general approach should be of broad interest, and can be adopted in future mechanisms for synthetic data generation.

Abstract Mid‐latitude climate extremes are projected to increase in frequency under global climate change. How this may affect migratory bird populations is not well understood. The mid‐latitudes of North America experienced an extreme warming event during March 2012 that advanced the spring phenology of ecological productivity, resulting in lower levels of productivity during the summer. Here, we test the predictions that: (1) short‐distance migratory birds, due to geographic proximity and more flexible migratory behavior, should advance their spring migration phenology; and (2) breeding populations, due to lower summer productivity, should have reduced occurrences. We used occurrence data for 353 bird species from the eBird database to calculate weekly occurrence anomalies for 2012 relative to the 2010–2014 average. We identified species having unusually large positive occurrence anomalies during March 2012 and species having unusually large negative occurrence anomalies during July–August 2012. For each category, we summarized migration strategies, geographic distributions, and annual associations with temperature and ecological productivity. Short‐distance migrants whose winter and breeding ranges intersect the mid‐latitudes advanced their spring migration phenology during March (n = 21). Long‐distance migrants whose winter and breeding distributions were weakly associated with the mid‐latitudes had lower occurrences during the summer (n = 32). Five species were shared between the two categories. Within species’ winter ranges, temperature and ecological productivity were higher than expected during March; within species’ breeding ranges, ecological productivity was lower than expected during the summer. These differences were strongest for the 21 short‐distance migrants. Following our expectations, mid‐latitude climate extremes and associated ecological consequences broadly affected avian migration and breeding activities within the region. Our findings suggest short‐distance migrants are more flexible and resilient, whereas populations of long‐distance migrants are at a distinct disadvantage, which may intensify if the frequency of these events increases.

Globally, billions of flying animals undergo seasonal migrations, many of which occur at night. The temporal and spatial scales at which migrations occur and our inability to directly observe these nocturnal movements makes monitoring and characterizing this critical period in migratory animals' life cycles difficult. Remote sensing, therefore, has played an important role in our understanding of large-scale nocturnal bird migrations. Weather surveillance radar networks in Europe and North America have great potential for long-term low-cost monitoring of bird migration at scales that have previously been impossible to achieve. Such long-term monitoring, however, poses a number of challenges for the ornithological and ecological communities: how does one take advantage of this vast data resource, integrate information across multiple sensors and large spatial and temporal scales, and visually represent the data for interpretation and dissemination, considering the dynamic nature of migration? We assembled an interdisciplinary team of ecologists, meteorologists, computer scientists, and graphic designers to develop two different flow visualizations, which are interactive and open source, in order to create novel representations of broad-front nocturnal bird migration to address a primary impediment to long-term, large-scale nocturnal migration monitoring. We have applied these visualization techniques to mass bird migration events recorded by two different weather surveillance radar networks covering regions in Europe and North America. These applications show the flexibility and portability of such an approach. The visualizations provide an intuitive representation of the scale and dynamics of these complex systems, are easily accessible for a broad interest group, and are biologically insightful. Additionally, they facilitate fundamental ecological research, conservation, mitigation of human-wildlife conflicts, improvement of meteorological products, and public outreach, education, and engagement.

Wind plays a significant role in the flight altitudes selected by nocturnally migrating birds. At mid-latitudes in the Northern Hemisphere, atmospheric conditions are dictated by the polar-front jet stream, whose amplitude increases in the autumn. One consequence for migratory birds is that the region’s prevailing westerly winds become progressively stronger at higher migration altitudes. We expect this seasonality in wind speed to result in migrants occupying progressively lower flight altitudes, which we test using density estimates of nocturnal migrants at 100 m altitudinal intervals from 12 weather surveillance radar stations located in the northeastern USA. Contrary to our expectations, median migration altitudes deviated little across the season, and the variance was lower during the middle of the season and higher during the beginning and especially the end of the season. Early-season migrants included small- to intermediate-sized long-distance migrants in the orders Charadriiformes and Passeriformes, and late-season migrants included large-bodied and intermediate-distance migrants in the order Anseriformes. Therefore, seasonality in the composition of migratory species, and related variation in migration strategies and behaviours, resulted in a convex–concave bounded distribution of migration altitudes. Our results provide a basis for assessing the implications for migratory bird populations of changes in mid-latitude atmospheric conditions probably occurring under global climate change.