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On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2

Proceedings of the National Academy of Sciences(2024)10.1073/pnas.2318989121Source: DataRank Database

On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2 is a research paper published in Proceedings of the National Academy of Sciences (2024). On theSindex it has a DataRank of 0. It has been cited 6 times.

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Scored on demand from live citation data

Open Access6 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (13 grants)Impact (FWCI 2.31)OA: hybridTopics (44)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Nathan Glatt-Holtz ORCID,Andrew J. Holbrook ORCID,Andrew F. Magee ORCID,Marc A. Suchard ORCID,Gustavo Didier ORCID

Live enrichment

PubMed:38215186 OpenAlex:https://openalex.org/W4390817180

National Science Foundation

Grant: DMS 2108790

National Science Foundation

Grant: DMS 2152774

National Science Foundation

Grant: DMS 2236854

Simons Foundation

Grant: 714014

HHS | NIH | NIAID | Division of Intramural Research, National Institute of Allergy and Infectious Diseases

Grant: K25 AI153816

HHS | NIH | NIAID | Division of Intramural Research, National Institute of Allergy and Infectious Diseases

Grant: R01 AI153044

HHS | NIH | National Institute of Allergy and Infectious Diseases

Grant: R01 AI162611

National Science Foundation

Grant: 2108790

Collaborative Research: Bayesian Inversion Approaches to Partial Differential Equations: Theory, Algorithm Development, and Applications

National Science Foundation

Grant: 2152774

Accelerating Bayesian Dimension Reduction for Dynamic Network Data with Many Observations

National Institutes of Health

Grant: 5R01AI162611-05

Fast and flexible Bayesian phylogenetics via modern machine learning

National Institutes of Health

Grant: 5R01AI153044-04

Statistical innovation to integrate sequences and phenotypes for scalable phylodynamic inference

National Science Foundation

Grant: 2236854

CAREER: Data-Centric Evolutionary Contagion Models with Parallel and Quantum Parallel Computing

National Institutes of Health

Grant: 5K25AI153816-04

Big Data Predictive Phylogenetics with Bayesian Learning

FWCI

2.31

Citation Percentile

0.9%

Citation Trend

2024

2025

hybridLicense: cc-by-nc-nd

journalhttps://www.pnas.org/doi/pdf/10.1073/pnas.2318989121

publisherhttps://www.pnas.org/doi/pdf/10.1073/pnas.2318989121

publisherhttps://pnas.org/doi/pdf/10.1073/pnas.2318989121

journalhttps://doi.org/10.1073/pnas.2318989121

repositoryhttps://pubmed.ncbi.nlm.nih.gov/38215186

Fields of Study

Evolution and Genetic DynamicsProtein Structure and DynamicsGene Regulatory Network Analysis0301 basic medicine01 natural sciences03 medical and health sciences0101 mathematicsHumansSARS-CoV-2AlgorithmsCOVID-19Markov Chains

MeSH Terms

COVID-19SARS-CoV-2AlgorithmsHumansMarkov Chains

Keywords

Matrix exponentialCurse of dimensionalityApplied mathematicsMarkov chainMathematicsComputer scienceMathematical optimizationArtificial intelligenceMachine learningFOS: Computer and information sciencesEpidemiologySARS-CoV-2Probability (math.PR)COVID-19Statistics - ComputationMarkov ChainsPhysical SciencesFOS: MathematicsHumansApplications of continuous-time Markov processes on discrete state spacesMathematics - ProbabilityAlgorithmsComputation (stat.CO)Hamiltonian Monte Carlocontinuous-time Markov chainsmolecular epidemiologyrandom matrix theory