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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

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).

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Case Study: Analysis of Autonomous Center line Tracking Neural Networks

Deep neural networks have gained widespread usage in a number of applications. However, limitations such as lack of explainability and robustness inhibit building trust in their behavior, which is crucial in safety critical applications such as autonomous driving. Therefore, techniques which aid in understanding and providing guarantees for neural network behavior are the need of the hour. In this paper, we present a case study applying a recently proposed technique, Prophecy, to analyze the behavior of a neural network model, provided by our industry partner and used for autonomous guiding of airplanes on taxi runways. This regression model takes as input an image of the runway and produces two outputs, cross-track error and heading error, which represent the position of the plane relative to the center line. We use the Prophecy tool to extract neuron activation patterns for the correctness and safety properties of the model. We show the use of these patterns to identify features of the input that explain correct and incorrect behavior. We also use the patterns to provide guarantees of consistent behavior. We explore a novel idea of using sequences of images (instead of single images) to obtain good explanations and identify regions of consistent behavior.

Deep Neural Networks↗
DOE OSTI2025

Exploring prokaryotic diversity in permafrost-affected soils of Ladakh’s Changthang region and its geochemical drivers

Global warming due to climate change has substantial impact on high-altitude permafrost affected soils. This raises a serious concern that the microbial degradation of sequestered carbon can result in alteration of the biogeochemical cycles. Therefore, the characterization of permafrost affected soil microbiomes, especially of unexplored high-altitude, low oxygen arid region, is important for predicting their response to climate change. This study presents the first report of the bacterial diversity of permafrost-affected soils in the Changthang region of Ladakh. The relationship between soil pH, organic carbon, electrical conductivity, and available micronutrients with the microbial diversity was investigated. Amplicon sequencing of permafrost affected soil samples from Jukti and Tsokar showed that Proteobacteria and Actinobacteria were the dominant phyla in all samples. The genera Brevitalea, Chthoniobacter, Sphingomonas, Hydrogenispora, Clostridium, Gaiella, Gemmatimonas were relatively abundant in the Jukti samples whereas the genera Thiocapsa, Actinotalea, Syntrophotalea, Antracticibcterium, Luteolibacter, Nitrospirillum dominated the Tsokar sample. Correlation analyses highlighted the influence of soil geochemical parameters on the bacterial community structure. PCoA analyses showed that the bacterial beta diversity varied significantly between the sampling locations (PERMANOVA test (F-value: 2.3316; R 2 = 0.466, p = 0.001) and similar results were also obtained while comparing genus abundance data using the ANOSIM test (R = 0.345, p = 0.007).

16S rRNA↗
DOE OSTI2025

Blue Carbon Stocks Along the Pacific Coast of North America Are Mainly Driven by Local Rather Than Regional Factors

Coastal wetlands, including seagrass meadows, emergent marshes, mangroves, and temperate tidal swamps, can efficiently sequester and store large quantities of sediment organic carbon (SOC). However, SOC stocks may vary by ecosystem type and along environmental or climate gradients at different scales. Quantifying such variability is needed to improve blue carbon accounting, conservation effectiveness, and restoration planning. We analyzed SOC stocks in 1,284 sediment cores along >6,500 km of the Pacific coast of North America that included large environmental gradients and multiple ecosystem types. Tidal wetlands with woody vegetation (mangroves and swamps) had the highest mean stocks to 1 m depth (357 and 355 Mg ha −1 , respectively), 45% higher than marshes (245 Mg ha −1 ), and more than 500% higher than seagrass (68 Mg ha −1 ). Unvegetated tideflats, though not often considered a blue carbon ecosystem, had noteworthy stocks (148 Mg ha −1 ). Stocks increased with tidal elevation and with fine (<63 μm) sediment content in several ecosystems. Stocks also varied by dominant plant species within individual ecosystem types. At larger scales, marsh stocks were lowest in the Sonoran Desert region of Mexico, and swamp stocks differed among climate zones; otherwise stocks showed little correlation with ecoregion or latitude. More variability in SOC occurred among ecosystem types, and at smaller spatial scales (such as individual estuaries), than across regional climate gradients. These patterns can inform coastal conservation and restoration priorities across scales where preserving stored carbon and enhancing sequestration helps avert greenhouse gas emissions and maintains other vital ecosystem services.

emergent marsh↗
DOE OSTI2025

Unique genetic signatures in HIV-1 subtype A1 and A1D recombinant envelope glycoprotein distinguish contemporary transmitted/founder viruses from historical strains in East Africa

Introduction: The envelope glycoprotein (Env) of HIV-1 Transmitted/Founder (T/F) viruses in subtypes B and C carries distinct genetic signatures that enhance transmission fitness, augment infectivity and immune evasion. However, there is limited data on such signatures in T/F subtypes A1, D and A1D recombinants that predominate East Africa’s HIV epidemic.Methods: We used phylogenetically corrected approaches to detect distinct genetic signatures by comparing 44 contemporary HIV-1 T/F Envs with 229 historical Envs of the same subtype in East Africa.Results and Discussion: Subtype analysis based on the full-length Env gene of contemporary T/F viruses revealed a high proportion of subtype A1, followed by A1D recombinants, and fewer subtype D. Signature analysis revealed that the contemporary subtype A1 T/Fs were more likely to select distinct amino acids, including M22 in the signal peptide, R82 in gp120, A172 in the V2 loop, E230 in the glycosite 230, K275 in the D loop, Y317 in the V3 loop, K476 and N477 in the CD4 contact site, when compared with the historical Envs (q-value < 0.2). Conversely, the contemporary subtype A1 T/F Envs were less likely to carry the amino acids Q432 in the CD4 contact site, and the L784 signature within the LLP-2 (q-value < 0.2). The A1D recombinant T/Fs were more likely to select the D620 in the C-helix, but under selected the L34 in gp120, P299 in the V3 loop and Y643 in the Heptad repeat-2, compared to the historical Envs (q-value < 0.2). The distinct signature sites reported in this study may contribute to the successful establishment of acute infection as well as the persistence of long-term infection. Therefore, effective therapeutics and vaccines may target these distinct amino acid signatures especially for the East African region as it may be necessary to employ subtype-specific vaccines according to the subtype distribution.

59 BASIC BIOLOGICAL SCIENCES↗