ArticleBioinformatics (Oxford, England)2019
FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 66 citations in OpenAlex.
- Comparing methods to estimate time-varying reproduction numbers using genomic and epidemiological data.Infectious Disease Modelling · 2026Article
- The tree labeling polytope: A unified approach to ancestral reconstruction problems.Cell systems · 2026Article
- e3SIM: Epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.Methods in ecology and evolution · 2026Article
- SIMPLICITY is an agent-based, multi-scale mathematical model to study SARS-CoV-2 intra- and between-host evolution.Communications biology · 2026Article
- phyddle: Software for Exploring Phylogenetic Models with Deep Learning.Systematic biology · 2025Article
- A graph homomorphism approach for unraveling histories of metastatic cancers and viral outbreaks under evolutionary constraints.Nature communications · 2025Article
- Apollo: a comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell.Nature communications · 2025Article
- Hybrid analysis with phylogeny and population modeling to estimate the recent founding date of a population: A case study in the origins of COVID-19 illustrates how a branching process approximation can simplify a hybrid analysis.Mathematical biosciences · 2025Article
- phyddle: software for exploring phylogenetic models with deep learning.bioRxiv : the preprint server for biology · 2025Article
- Scalable Epidemic Simulation Using FAVITES-Lite.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Identifying impacts of contact tracing on HIV epidemiological inference from phylogenetic data.Virus evolution · 2025Article
- Apollo: A comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell.bioRxiv : the preprint server for biology · 2024Article
- Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.Systematic biology · 2024Article
- Article
- Estimating contact network properties by integrating multiple data sources associated with infectious diseases.Statistics in medicine · 2023Article
- Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.PLoS computational biology · 2022Article
- SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework.Cell systems · 2022Article
- The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2.Science (New York, N.Y.) · 2022Article
- VGsim: Scalable viral genealogy simulator for global pandemic.PLoS computational biology · 2022Article
- NiemaGraphGen: A memory-efficient global-scale contact network simulation toolkit.GigaByte (Hong Kong, China) · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
motivationThe ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like Human Immunodeficiency Virus (HIV) may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain 'truth' sets on which to test them and properly measure their performance.
resultsWe introduce FrAmework for VIral Transmission and Evolution Simulation (FAVITES), a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters. AVAILABILITY AND IMPLEMENTATION: FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.