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1. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

   https://doi.org/10.1073/pnas.2113561119  

   Cramer, Estee Y. ; Ray, Evan L. ; Lopez, Velma K. ; Bracher, Johannes ; Brennen, Andrea ; Castro Rivadeneira, Alvaro J. ; Gerding, Aaron ; Gneiting, Tilmann ; House, Katie H. ; Huang, Yuxin ; et al ( April 2022 , Proceedings of the National Academy of Sciences)

   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 atmore »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.« less
2. A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave

   https://doi.org/10.1038/s41467-021-25207-0  

   Bracher, J. ; Wolffram, D. ; Deuschel, J. ; Görgen, K. ; Ketterer, J. L. ; Ullrich, A. ; Abbott, S. ; Barbarossa, M. V. ; Bertsimas, D. ; Bhatia, S. ; et al ( December 2021 , Nature Communications)

   Abstract Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.
3. Predicting near-term variability in ocean carbon uptake

   https://doi.org/10.5194/esd-10-45-2019  

   Lovenduski, Nicole S. ; Yeager, Stephen G. ; Lindsay, Keith ; Long, Matthew C. ( January 2019 , Earth System Dynamics)

   Abstract. Interannual variations in air–sea fluxes of carbon dioxide (CO₂) impactthe global carbon cycle and climate system, and previous studies suggest thatthese variations may be predictable in the near term (from a year to a decadein advance). Here, we quantify and understand the sources of near-termpredictability and predictive skill in air–sea CO₂ flux on global andregional scales by analyzing output from a novel set of retrospective decadalforecasts of an Earth system model. These forecasts exhibit the potential topredict year-to-year variations in the globally integrated air–sea CO₂flux several years in advance, as indicated by the high correlation of theforecasts with a model reconstruction of past CO₂ flux evolution. Thispotential predictability exceeds that obtained solely from foreknowledge ofvariations in external forcing or a simple persistence forecast, with thelongest-lasting forecast enhancement in the subantarctic Southern Ocean andthe northern North Atlantic. Potential predictability in CO₂ fluxvariations is largely driven by predictability in the surface ocean partialpressure of CO₂, which itself is a function of predictability in surfaceocean dissolved inorganic carbon and alkalinity. The potentialpredictability, however, is not realized as predictive skill, as indicated bythe moderate to low correlation of the forecasts with anobservationally based CO₂ flux product. Nevertheless,more »our results suggestthat year-to-year variations in ocean carbon uptake have the potential to bepredicted well in advance and establish a precedent for forecasting air–seaCO₂ flux in the near future.

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4. Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March-December 2020

   https://doi.org/10.1371/journal.pone.0254826  

   Tariq, Amna ; Banda, Juan M. ; Skums, Pavel ; Dahal, Sushma ; Castillo-Garsow, Carlos ; Espinoza, Baltazar ; Brizuela, Noel G. ; Saenz, Roberto A. ; Kirpich, Alexander ; Luo, Ruiyan ; et al ( July 2021 , PLOS ONE)

   Adrish, Muhammad (Ed.)

   Mexico has experienced one of the highest COVID-19 mortality rates in the world. A delayed implementation of social distancing interventions in late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. In this study we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on the methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatiotemporal transmission patterns of SARS-CoV-2. The early estimates of the reproduction number for Mexico were estimated between R t ~1.1–1.3 from the genomic and case incidence data. Moreover, the mean estimate of R t has fluctuated around ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories based on epidemic growth rates. Our results show that the sequential mortality forecasts from themore »GLM and Richards model predict a downward trend in the number of deaths for all thirteen forecast periods for Mexico and Mexico City. However, the sub-epidemic and IHME models perform better predicting a more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21, 09/28-10/27, 09/28-10/27) for Mexico and Mexico City. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.« less
5. Short-term solar irradiance forecasting using convolutional neural networks and cloud imagery

   https://doi.org/10.1088/1748-9326/abe06d  

   Choi, Minsoo ; Rachunok, Benjamin ; Nateghi, Roshanak ( April 2021 , Environmental Research Letters)

   Access to accurate, generalizable and scalable solar irradiance prediction is critical for smooth solar-grid integration, especially in the light of the accelerated global adoption of solar energy production. Both physical and statistical prediction models of solar irradiance have been proposed in the literature. Physical models require meteorological forecasts—generated by computationally expensive models—to predict solar irradiance, with limited accuracy in sub-daily predictions. Statistical models leveragein-situmeasurements which require expensive equipment and do not account for meso-scale atmospheric dynamics. We address these fundamental gaps by developing a convolutional global horizontal irradiance prediction model, using convolutional neural networks and publicly accessible satellite cloud images. Our proposed model predicts solar irradiance in 12 different locations in the US for various prediction time horizons. Our model yields up to 24% improvement in an hour-ahead predictions and 26% in a day-ahead predictions compared to a persistence forecast. Moreover, using saliency maps and target-location-focused cropping, we demonstrate the benefits of incorporating meso-scale atmospheric dynamics for prediction performance. Our results are critical for energy systems planners, utility managers and electricity market participants to ensure efficient harvesting of the solar energy and reliable operation of the grid.