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Toward optimal disease surveillance with graph-based active learning

Proceedings of the National Academy of Sciences(2024)10.1073/pnas.2412424121Source: DataRank Database

Toward optimal disease surveillance with graph-based active learning 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 7 times.

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Enrichment:Funding (16 grants)OA: hybridTopics (22)IDs (PubMed, PMC)Annotations (1)

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Authors (9)

Mengyan Zhang ORCID,Prathyush Sambaturu ORCID,Simon Busch‐Moreno ORCID,Marc A. Suchard ORCID,Oliver Pybus ORCID

Live enrichment

PubMed:39700149 PMC:PMC11670094

HHS | NIH

Grant: R01 AI153044

UKRI | Engineering and Physical Sciences Research Council

Grant: EP/V002910/2

Spatiotemporal statistical machine learning (ST-SML): theory, methods, and applications

Schmidt Futures

Grant: G-22-64476

Wellcome Trust

Grant: 225288/Z/22/Z

Wellcome Trust

Grant: 226052/Z/22/Z

Wellcome Trust

Grant: 228186/Z/23/Z

UK Research and Innovation

Grant: APP8583

UKRI | MRC | Medical Research Foundation

Grant: MRF-RG-ICCH-2022-100069

EC | ERC | HORIZON EUROPE European Research Council

Grant: 874850

MOnitoring Outbreak events for Disease surveillance in a data science context

EC | ERC | HORIZON EUROPE European Research Council

Grant: 101086640

Eco-Epidemiological Intelligence for early Warning and response to mosquito-borne disease risk in Endemic and Emergence settings

Wellcome Trust

Grant: 225288

The ISARIC data platform: Optimizing data-driven pandemic preparedness and response

Wellcome Trust

Grant: 228186

Global.health: data science and sharing for early response to emerging infectious diseases

Wellcome Trust

Grant: 226052

Dengue Advanced Readiness Tools (DART) - integrated digital system for dengue outbreak prediction and monitoring

National Institutes of Health

Grant: 5R01AI153044-04

Statistical innovation to integrate sequences and phenotypes for scalable phylodynamic inference

UK Research and Innovation

Grant: 10066422

E4WARNING: ECO-EPIDEMIOLOGICAL INTELLIGENCE FOR EARLY WARNING AND RESPONSE TO MOSQUITO-BORNE DISEASE RISK IN ENDEMIC AND EMERGENCE SETTINGS

Wellcome Trust

hybridLicense: CC BY

publisherhttps://pnas.org/doi/pdf/10.1073/pnas.2412424121

Europe_PMChttps://europepmc.org/articles/PMC11670094

Europe_PMChttps://europepmc.org/articles/PMC11670094?pdf=render

https://doi.org/10.1073/pnas.2412424121

https://pubmed.ncbi.nlm.nih.gov/39700149

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

HumansPopulation SurveillanceBayes TheoremDisease OutbreaksMachine Learning

Keywords

epidemiologyPublic HealthDisease SurveillanceActive LearningNetwork Dynamics330Bayes TheoremBiological SciencesDisease OutbreaksGraph theoryMachine LearningPopulation SurveillanceHumansPublic Health Surveillance