Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts is a research paper published in Proceedings of the AAAI Conference on Artificial Intelligence (2023). On theSindex it has a DataRank of 0.360. It has been cited 10 times.
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Base Score Contribution
0.360
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Science Foundation
Grant: 1917819
Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology
National Science Foundation
Grant: 1916805
Collaborative Research: Framework: Software: CINES: A Scalable Cyberinfrastructure for Sustained Innovation in Network Engineering and Science
National Science Foundation
Grant: 2027541
RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks
National Science Foundation
Grant: 1918656
Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology
National Science Foundation
Grant: 2028004
RAPID: Collaborative: Transfer Learning Techniques for Better Response to COVID-19 in the US
National Institutes of Health
Grant: 3R01GM109718-05S1
Systems Analysis of Social Pathways of Epidemics to Reduce Health Disparities
National Science Foundation
Grant: 2142997
RAPID: Modeling and Analytics for COVID-19 Outbreak Response in India: A multi-institutional, US-India joint collaborative effort
FWCI
10.20
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals