Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes is a research paper published in Biometrics (2024). On theSindex it has a DataRank of 0. It has been cited 4 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Science Foundation
Grant: DMS-2030355
National Science Foundation
Grant: DMS-2230074
National Science Foundation
Grant: PIPP-2200047
National Institutes of Health
Grant: R01 AI153044
National Institutes of Health
Grant: R01 AI155080-01A1
Engineering and Physical Sciences Research Council
Grant: EP/X038440/1
Deep Poisson process pathogen phylodynamics to accelerate understanding in disease transmission
Bill and Melinda Gates Foundation
Grant: OPP1175094
Bill and Melinda Gates Foundation
Grant: OPP1084362
NIAID NIH HHS
Grant: UM1 AI068613
NIAID NIH HHS
Grant: R01 AI155080
National Institutes of Health
Grant: 5R01AI153044-04
Statistical innovation to integrate sequences and phenotypes for scalable phylodynamic inference
National Science Foundation
Grant: 2200047
PIPP Phase I: Modeling the Pandemic Lifecycle for Disease Control
National Science Foundation
Grant: 2030355
Collaborative Research: RAPID: Statistical Tools to Quantify and Mitigate the Spread of COVID-19
National Science Foundation
Grant: 2230074
RAISE: IHBEM: Equilibrium, Network Formation, and Infectious-Disease Spread: Bridging the Divide between Mathematical Biology and Economics
FWCI
0.92
Citation Percentile
0.7%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals