In recent years, the use of multi-model ensemble projections in infectious disease modeling has become an established methodological approach to account for and integrate across uncertainties and structural differences present in individual models. However, the creation of long-term ensemble projections through these coordinated efforts is resource-intensive, demanding the input of multiple research teams and substantial computational power. This typically limits the ability to refine projections, update the selection of plausible epidemic trajectories, or expand the number of scenarios that can be assessed, even as new empirical data become available. To address this challenge, we define an adaptive ensemble approach that, analogously to a multi-model particle filtering method, dynamically selects individual model trajectories based on observed data throughout the epidemic projection period. We demonstrate the effectiveness of this methodology using the U.S. Flu Scenario Modeling Hub (SMH) projections for influenza hospitalizations in the United States during the 2023-2024 and 2024-2025 winter seasons. Our findings show that the adaptive ensemble yields improved predictive accuracy with respect to the original SMH ensemble projections across several scoring rules and geographical resolutions. Furthermore, the adaptive ensemble approach offers two additional applications: i) the dynamic assignment of posterior probabilities to epidemic scenarios, identifying the most plausible scenario, and representing how reality is captured by a combination of scenarios, and ii) the potential use for short-term forecasting. The adaptive ensemble approach is able to identify the most likely scenarios for the 2023-2024 and 2024-2025 U.S. influenza seasons, even in the early stages of the epidemic. It outperforms, retrospectively, a baseline model in short-term forecasting of influenza hospitalizations in the United States during the two seasons across various horizons and scoring rules, showing potential to contribute to real-time collaborative forecasting challenges such as CDC's FluSight. The proposed approach offers an efficient or low-resource strategy to increase the impact of multi-model epidemic projections by providing real-time support to modeling teams, public health authorities, and decision-makers.

Hospital admission data for respiratory illnesses such as influenza, COVID-19, and RSV are critical for real-time public health surveillance and short-term forecasting. The National Healthcare Safety Network (NHSN) which provides a comprehensive dataset for all 52 jurisdictions in United States changed its reporting cadence in November 2024. It began releasing preliminary weekly data on Wednesdays and updated versions on Fridays. Because Wednesday reports are often incomplete, especially for the most recent week, users of this dataset must contend with backfill, that is, subsequent (predominantly) increases in reported values as hospitals complete their submissions. In this study, we evaluate four simple nowcasting models to estimate the magnitude of backfill to the last data point: (1) a NULL model that assumes no backfill; (2) a linear-scaling model that assumes backfill scales linearly with the fraction of hospitals yet to report; (3) a prospective-scaling model used operationally during the season, which estimates backfill as a compromise between the two; and (4) a retrospective-scaling model that uses a fitted regression based on the full season’s data. Using 26 weeks of data from 52 U.S. jurisdictions, we compared model estimates to the final reported values and calculated absolute and relative errors. The linear-scaling model consistently overestimated backfill and underperformed the NULL model in the majority of cases for all three pathogens. In contrast, the prospective-scaling and retrospective-scaling models outperformed the NULL model for influenza and COVID-19 in most locations, but not for RSV. Despite the assumption that backfill should be non-negative, we observed negative backfill in 21 jurisdictions (often early in the season), which reduced model accuracy. Our results suggest that the intuitive linear-scaling approach is unreliable for this dataset, likely due to heterogeneity in reporting-facility catchment areas. While the retrospective-scaling model yielded the most accurate estimates, its reliance on complete-season data limits operational use. The prospective-scaling model, which requires only current data, provided the most reliable real-time estimates for influenza and COVID-19. These findings support the use of prospective-scaling for operational forecasting and highlight the need for probabilistic nowcasting methods.

Six years after its emergence, SARS-CoV-2 continues to have a substantial burden, however, the impact of vaccination and the optimal timing of its rollout remain uncertain. To explore these uncertainties, the US Scenario Modeling Hub convened its 19th round of ensemble projections for COVID-19 hospitalizations and deaths in the United States. Eight teams provided outcomes for each US state and nationally from April 2025 to April 2026 under five scenarios regarding vaccine recommendations and timing. We assessed recommendations with two eligibility scenarios (high-risk individuals only and all-eligible) and two timing scenarios (classic start: mid-August, earlier start: late June). These were crossed to create four scenarios and were compared against a counterfactual scenario with no vaccination. We found that compared to no vaccination, our ensemble projections estimated 90,000 (95% PI 53,000-126,000) hospitalizations averted in the high-risk and classic timing scenario across the US. Expanding coverage averted an additional 26,000 (95% PI 14,000-39,000) hospitalizations, which when coupled with earlier vaccination timing further reduced national hospitalizations by 15,000 (95% PI -3,000-33,000). These findings estimate significant benefits from a broad all-eligible vaccination recommendation, and suggest an additional benefit is likely to be gained from an earlier vaccination campaign.

Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making. ### Competing Interest Statement J.S. and Columbia University disclose partial ownership of SK Analytics. N.G.R. discloses paid consulting for Google Inc. J.Lemaitre discloses paid consulting for Pfizer Inc. The remaining authors declare no competing interests. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The forecast data for each model are publicly accessible from the FluSight Forecast Hub GitHub repository(https://github.com/cdcepi/FluSight-forecast-hub). The target hospitalization data are also available as weekly counts for each jurisdiction from U.S. Department of Health & Human Services. Weekly Hospital Respiratory Data (HRD) Metrics by Jurisdiction, National Healthcare Safety Network (NHSN), https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/ua7e-t2fy/about_data All data analyzed were publicly available prior to the initiation of this study. The influenza hospital admission data are aggregated weekly counts reported at the jurisdiction level and contain no individually identifiable information. The forecast data are model outputs submitted by participating teams to a public repository. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The forecast data for each model are publicly accessible from the FluSight Forecast Hub GitHub repository(https://github.com/cdcepi/FluSight-forecast-hub; https://doi.org/10.5281/zenodo.22101290). The target data are also available as weekly counts for each jurisdiction from HHS CDC's Center for Forecasting and Outbreak Analytics, CDC-RFA-FT-23-0069 CSTE/CDC, NU38OT000297, NU38PW000005, NU38OT000297 Centers for Disease Control and Prevention, U01IP001121, 75D30123C15907, U01IP001122, 6NU50CK000555-03-01 Natural Sciences and Engineering Research Council of Canada, ALLRP 581756-23 National Institute of General Medical Sciences, R35GM119582, R24GM153920, R35GM156799 National Institute of Allergy and Infectious Diseases, R01AI163023 National Institutes of Health / National Institute of General Medical Sciences, R35GM156799, DBI-2412389 National Institutes of Health / National Institute of General Medical Sciences, R01GM111510 National Institutes of Health, 5R01AI102939

Importance:COVID-19 remains a disease with high burden in the US, prompting continued debate about optimal targets for annual vaccination. Objective:To project COVID-19 burden in the US for April 2024 to April 2025 under 6 scenarios of immune escape (20% and 50% per year) and levels of vaccine recommendation (no recommendation, vaccination for individuals at high risk only, vaccination for all eligible groups) and to assess the potential benefit of vaccine recommendations in reducing disease burden. Design, Setting, and Participants:For this decision analytical model, the US Scenario Modeling Hub, a collaborative modeling effort, convened 9 teams to provide scenario projections of US COVID-19 hospitalizations and deaths for April 2024 to April 2025, under 6 scenarios combining levels of immune escape and possible vaccine recommendations. Exposure:Annually reformulated vaccines were assumed to be 75% effective against hospitalization for variants circulating on June 15, 2024, and available on September 1, 2024. Age- and state-specific coverage was assumed to be as reported in September 2023 to April 2024. Main Outcomes and Measures:Ensemble estimates were made for weekly COVID-19 hospitalizations and deaths. Projections are presented for relative and absolute prevented hospitalizations and deaths averted due to vaccination over the April 2024 to April 2025 period. Results:For the US population (332 million, with an estimated 58 million aged ≥65 years), COVID-19 was expected to cause 814 000 (95% projection interval [PI], 400 000-1.2 million) hospitalizations and 54 000 (95% PI, 17 000-98 000) deaths for April 2024 to April 2025, comparable in magnitude to the prior year. Vaccination of high-risk groups only was projected to reduce hospitalizations (compared to no vaccination recommendation) by 76 000 (95% CI, 34 000-118 000) and deaths by 7000 (95% CI, 3000-11 000) across both immune escape scenarios. Compared with vaccinating high-risk groups only, a universal vaccine recommendation was projected to provide direct and indirect benefits, further preventing 11 000 hospitalizations and 1000 deaths in those aged 65 years and older. Conclusions and Relevance:In this decision analytical modeling study of COVID-19 burden in the US in 2024 to 2025, ensemble projections suggested that although vaccinating high-risk groups had substantial benefits in reducing disease burden, maintaining the vaccine recommendation for all individuals had the potential to save thousands more lives. Despite divergence of projections from observed disease trends in 2024 to 2025-possibly driven by variant emergence patterns and immune escape-averted COVID-19 burden due to vaccination was robust across immune escape scenarios, emphasizing the substantial benefit of broader vaccine availability for all individuals.

The Sun's corona is its tenuous outer atmosphere of hot plasma, which is difficult to observe. Most models of the corona extrapolate its magnetic field from that measured on the photosphere (the Sun's optical surface) over a full 27-day solar rotational period, providing a time-stationary approximation. We present a model of the corona that evolves continuously in time, by assimilating photospheric magnetic field observations as they become available. This approach reproduces dynamical features that do not appear in time-stationary models. We used the model to predict coronal structure during the total solar eclipse of 8 April 2024 near the maximum of the solar activity cycle. There is better agreement between the model predictions and eclipse observations in coronal regions located above recently assimilated photospheric data.

Total solar eclipses offer an unparalleled opportunity to observe the low and middle corona. As is our tradition, the solar physics team at Predictive Science is predicting the structure of the solar corona for the April 8, 2024 total solar eclipse, using a magnetohydrodynamic (MHD) model of the corona. The model incorporates thermodynamic transport terms and employs a wave-turbulence-driven (WTD) description of coronal heating and solar wind acceleration. Our previous coronal predictions employed relaxed MHD solutions corresponding to a boundary condition based on a single photospheric magnetic map, incorporating data that at best was measured 10 to 14 days prior to the eclipse.This year, we introduce a new paradigm: A continuously updated prediction based on a time-evolving model. To accomplish this near-real time description, we have incorporated 3 new elements: (1) a time-evolving MHD model driven by evolution of the photospheric magnetic field, (2) an automated method for energizing the non-potential corona near polarity inversion lines that evolve in time, and (3) The Open-source Flux Transport (OFT) model, that assimilates near-real time surface magnetic flux observations from SDO HMI as well as low-latency observations from the Solar Orbiter PHI instrument made away from the Sun–-Earth line.This presentation will give an overview of the entire prediction effort and describe the time-dependent coronal dynamical features that appear in the solutions.Research Supported by NASA and NSF. Computational resources provided by the NSF ACCESS program and the NASA Advanced Supercomputing division at Ames.

Coronal holes are recognized as the primary sources of heliospheric open magnetic flux (OMF). However, a noticeable gap exists between in-situ measured OMF and that derived from remote sensing observations of the Sun. In this study, we investigate the OMF evolution and its connection to solar structures throughout 2014, with special emphasis on the period from September to October, where a sudden and significant OMF increase was reported. By deriving the OMF evolution at 1au, modeling it at the source surface, and analyzing solar photospheric data, we provide a comprehensive analysis of the observed phenomenon. First, we establish a strong correlation between the OMF increase and the solar magnetic field derived from a Potential Field Source Surface (PFSS) model ($cc_{\mathrm{Pearson}}=0.94$). Moreover, we find a good correlation between the OMF and the open flux derived from solar coronal holes ($cc_{\mathrm{Pearson}}=0.88$), although the coronal holes only contain $14-32\%$ of the Sun's total open flux. However, we note that while the OMF evolution correlates with coronal hole open flux, there is no correlation with the coronal hole area evolution ($cc_{\mathrm{Pearson}}=0.0$). The temporal increase in OMF correlates with the vanishing remnant magnetic field at the southern pole, caused by poleward flux circulations from the decay of numerous active regions months earlier. Additionally, our analysis suggests a potential link between the OMF enhancement and the concurrent emergence of the largest active region in solar cycle 24. In conclusion, our study provides insights into the strong increase in OMF observed during September to October 2014.

Total solar eclipses offer an unparalleled opportunity to observe the low and middle corona.  As is our tradition, the solar physics team at Predictive Science is predicting the structure of the solar corona for the April 8, 2024 total solar eclipse, using a magnetohydrodynamic (MHD) model of the corona.  The model incorporates thermodynamic transport terms and employs a wave-turbulence-driven (WTD) description of coronal heating and solar wind acceleration.  Our previous coronal predictions employed relaxed MHD solutions corresponding to a boundary condition based on a single photospheric magnetic map, incorporating data that at best was measured 10 to 14 days prior to the eclipse. This year, we introduce a new paradigm:  A continuously updated prediction based on a time-evolving model.  To accomplish this near-real time description, we have incorporated 3 new elements:  (1) a time-evolving MHD model driven by evolution of the photospheric magnetic field,  (2) an automated method for energizing the non-potential corona near polarity inversion lines that evolve in time, and (3) The Open-source Flux Transport (OFT) model, that assimilates near-real time surface magnetic flux observations from SDO HMI as well as low-latency observations from the Solar Orbiter PHI instrument made away from the Sun–-Earth line. This presentation will give an overview of the entire prediction effort and describe the time-dependent coronal dynamical features that appear in the solutions. Research Supported by NASA and NSF.  Computational resources provided by the NSF ACCESS program and the NASA Advanced Supercomputing division at Ames.

The Wang–Sheeley–Arge (WSA) model has been in use for decades and remains a popular, economical approach to modeling the solar coronal magnetic field and forecasting conditions in the inner heliosphere. Given its usefulness, it is unsurprising that a number of WSA implementations have been developed by various groups with different computational approaches. While the WSA magnetic field model has traditionally been calculated using a spherical harmonic expansion of the solar magnetic field, finite-difference potential field solutions can offer speed and/or accuracy advantages. However, the creation of new versions of WSA requires that we ensure the solutions from these new models are consistent with established versions and that we quantify for the user community to what degree and in what ways they differ. In this paper, we present side-by-side comparisons of WSA models produced using the traditional, spherical harmonic–based implementation developed by Wang, Sheeley, and Arge with WSA models produced using a recently open-sourced finite-difference code from the CORHEL modeling suite called POT3D. We present comparisons of the terminal solar wind speed and magnetic field at the outer boundaries of the models, weighing these against the variation of the WSA model in the presence of small perturbations in the computational procedure, parameters, and inputs. We also compare the footpoints of magnetic field lines traced from the outer boundaries and the locations of open field in the models. We find that the traced field-line footpoints show remarkable agreement, with the greatest differences near the magnetic neutral line and in the polar regions.

In temperate regions, annual preparation by public health officials for seasonal influenza requires early-season long-term projections. These projections are different from short-term (e.g., 1-4 weeks ahead) forecasts that are typically updated weekly. Whereas short-term forecasts estimate what “will” likely happen in the near term, the goal of scenario projections is to guide long-term decision-making using “what if” scenarios. We developed a mechanistic metapopulation model and used it to provide long-term influenza projections to the Flu Scenario Modeling Hub. The scenarios differed in their assumptions about influenza vaccine effectiveness and prior immunity. The parameters of the model were inferred from early season hospitalization data and then simulated forward in time until June 3, 2023. We submitted two rounds of projections (mid-November and early December), with the second round being a repeat of the first with three more weeks of data (and consequently different model parameters). In this study, we describe the model, its calibration, and projections targets. The scenario projection outcomes for two rounds are compared with each other at state and national level reported daily hospitalizations. We show that although Rounds 2 and 3 were identical in definition, the addition of three weeks of data produced an improvement to model fits. These changes resulted in earlier projections for peak incidence, lower projections for peak magnitude and relatively small changes to cumulative projections. In both rounds, all four scenarios presented conceivable outcomes, with some scenarios agreeing well with observations. We discuss how to interpret this agreement, emphasizing that this does not imply that one scenario or another provides the ground truth. Our model's performance suggests that its underlying assumptions provided plausible bounds for what could happen during an influenza season following two seasons of low circulation. We suggest that such projections would provide actionable estimates for public health officials.

To address Objective II of the National Space Weather Strategy and Action Plan "Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts" and US Congress PROSWIFT Act 116-181, our team is developing a new set of open-source software that would ensure substantial improvements of Space Weather (SWx) predictions. On the one hand, our focus is on the development of data-driven solar wind models. On the other hand, each individual component of our software is designed to have accuracy higher than any existing SWx prediction tools with a dramatically improved performance. This is done by the application of new computational technologies and enhanced data sources. The development of such software paves way for improved SWx predictions accompanied with an appropriate uncertainty quantification. This makes it possible to forecast hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models include (1) a new, open-source solar magnetic flux model (OFT), which evolves information to the back side of the Sun and its poles, and updates the model flux with new observations using data assimilation methods; (2) a new potential field solver (POT3D) associated with the Wang-Sheeley-Arge coronal model, and (3) a new adaptive, 4-th order of accuracy solver (HelioCubed) for the Reynolds-averaged MHD equations implemented on mapped multiblock grids (cubed spheres). We describe the software and results obtained with it, including the application of machine learning to modeling coronal mass ejections, which makes it possible to improve SWx predictions by decreasing the time-of-arrival mismatch. The tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions.

Automated detection schemes are nowadays the standard approach for locating coronal holes in extreme-UV images from the Solar Dynamics Observatory (SDO). However, factors such as the noisy nature of solar imagery, instrumental effects, and others make it challenging to identify coronal holes using these automated schemes. While discrepancies between detection schemes have been noted in the literature, a comprehensive assessment of these discrepancies is still lacking. The contribution of the Coronal Hole Boundary Working Team in the COSPAR ISWAT initiative to close this gap is threefold. First, we present the first community data set for comparing automated coronal hole detection schemes. This data set consists of 29 SDO images, all of which were selected by experienced observers to challenge automated schemes. Second, we use this community data set as input to 14 widely applied automated schemes to study coronal holes and collect their detection results. Third, we study three SDO images from the data set that exemplify the most important lessons learned from this effort. Our findings show that the choice of the automated detection scheme can have a significant effect on the physical properties of coronal holes, and we discuss the implications of these findings for open questions in solar and heliospheric physics. We envision that this community data set will serve the scientific community as a benchmark data set for future developments in the field.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Context. Our understanding of the solar magnetic coronal structure is tightly linked to the shape of open field regions, specifically coronal holes. A dynamically evolving coronal hole coincides with the local restructuring of open to closed magnetic field, which leads to changes in the interplanetary solar wind structure. Aims. By investigating the dynamic evolution of a fast-tilting coronal hole, we strive to uncover clues about what processes may drive its morphological changes, which are clearly visible in extreme ultraviolet (EUV) filtergrams. Methods. Using combined 193 Å and 195 Å EUV observations by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and the Extreme UltraViolet Imager on board the Solar Terrestrial Relations Observatory-Ahead, in conjunction with line-of-sight magnetograms taken by the Helioseismic and Magnetic Imager, also on board the Solar Dynamics Observatory, we tracked and analyzed a coronal hole over 12 days to derive changes in morphology, area, and magnetic field. We complemented this analysis by potential field source surface modeling to compute the open field structure of the coronal hole. Results. We find that the coronal hole exhibits an apparent tilting motion over time that cannot solely be explained by solar differential rotation. It tilts at a mean rate of ∼3.2° day−1 that accelerates up to ∼5.4° day−1. At the beginning of May the area of the coronal hole decreased by more than a factor of three over four days (from ∼13 × 109 km2 to ∼4 × 109 km2), but its open flux remained constant (∼2 × 1020 Mx). Furthermore, the observed evolution is not reproduced by modeling that assumes the coronal magnetic field to be potential. Conclusions. In this study we present a solar coronal hole that tilts at a rate that has yet to be reported in literature. The rate exceeds the effect of the coronal hole being advected by either photospheric or coronal differential rotation. Based on the analysis we find it likely that this is due to morphological changes in the coronal hole boundary caused by ongoing interchange reconnection and the interaction with a newly emerging ephemeral region in its vicinity.

To address Objective II of the National Space Weather Strategy and Action Plan 'Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts' and US Congress PROSWIFT Act 116–181, our team is developing a new set of open-source software that would ensure substantial improvements of Space Weather (SWx) predictions. On the one hand, the focus is on the development of data-driven models. On the other hand, each individual component of our software will have higher accuracy with a dramatically improved performance. This is done by the application of new computational technologies and enhanced data sources. The development of such software paves way for improved SWx predictions accompanied with an appropriate uncertainty quantification. This will make it possible to forecast hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models involve (1) a new, open-source solar magnetic flux model (OFT), which evolves information to the back side of the Sun and its poles, and updates the model flux with new observations using data assimilation methods; (2) a new potential field solver (POT3D) associated with the Wang-Sheeley-Arge coronal model, and (3) a new adaptive, 4-th order of accuracy solver (HelioCubed) for the Reynolds-averaged MHD equations implemented on mapped multiblock grids (cubed spheres). We describe the software and results obtained with it, including the appication of machine learning to modeling coronal mass ejections, which makes it possible to improve SWx predictions by decreasing the time-of-arrival mismatch. The test show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions.

ABSTRACT Introduction The CoronaVirus Disease 2019 (COVID-19) pandemic remains a formidable threat to populations around the world. The U.S. Military, in particular, represents a unique and distinguishable subset of the population, primarily due to the age and gender of active duty personnel. Current investigations have focused on health outcome forecasts for civilian populations, making them of limited value for military planning. Materials and Methods We have developed and applied an age-structured susceptible, exposed, infectious, recovered, or dead compartmental model for both civilian and military populations, driven by estimates of the time-dependent reproduction number, R(t), which can be both fit to available data and also forecast future cases, intensive care unit (ICU) patients, and deaths. Results We show that the expected health outcomes for active duty military populations are substantially different than for civilian populations of the same size. Specifically, while the number of cases is not expected to differ dramatically, severity, both in terms of ICU burdens and deaths, is substantially lower. Conclusions Our results confirm that the burden placed on military health centers will be substantially lower than that for equivalent-sized civilian populations. More practically, the tool we have developed to investigate this (https://q.predsci.com/covid19/) can be used by military health planners to estimate the resources needed in particular locations based on current estimates of the transmission profiles of COVID-19 within the surrounding civilian population in which the military installation is embedded. As this tool continues to be developed, it can be used to assess the likely impact of different intervention strategies, as well as vaccine policies; both for the current pandemic as well as future ones.

More than a year since the appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), many questions about the disease COVID-19 have been answered; however, many more remain poorly understood. Although the situation continues to evolve, it is crucial to understand what factors may be driving transmission through different populations, both for potential future waves, as well as the implications for future pandemics. In this report, we compiled a database of more than 28 potentially explanatory variables for each of the 50 U.S. states through early May 2020. Using a combination of traditional statistical and modern machine learning approaches, we identified those variables that were the most statistically significant, and, those that were the most important. These variables were chosen to be fiduciaries of a range of possible drivers for COVID-19 deaths in the USA. We found that population-weighted population density (PWPD), some "stay at home" metrics, monthly temperature and precipitation, race/ethnicity, and chronic low-respiratory death rate, were all statistically significant. Of these, PWPD and mobility metrics dominated. This suggests that the biggest impact on COVID-19 deaths was, at least initially, a function of where you lived, and not what you did. However, clearly, increasing social distancing has the net effect of (at least temporarily) reducing the effective PWPD. Our results strongly support the idea that the loosening of "lock-down" orders should be tailored to the local PWPD. In contrast to these variables, while still statistically significant, race/ethnicity, health, and climate effects could only account for a few percent of the variability in deaths. Where associations were anticipated but were not found, we discuss how limitations in the parameters chosen may mask a contribution that might otherwise be present.

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.