FAVITES: simultaneous simulation of transmission networks, phylogenetic trees, and sequences

Abstract

Motivation:The 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 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.Results: We introduce 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).Redd, 2014). More recently, these methods have become widely used at both local and global scale . Nevertheless, several questions remain to be fully answered regarding the performance of these methods. It is not always clear which method/setting combination performs best for a specific downstream use-case or for specific epidemiological conditions. More broadly, the effectiveness of these methods in helping achieve public health goals is the subject of ongoing clinical and theoretical research.Accuracy of transmission networks is difficult to assess because the true order of transmissions is not known. Moreover, predicting the impact of parameters of interest (e.g., rate of treatment) on the epidemiological outcomes is difficult. In simulations, in contrast, the ground truth is known and parameters can be easily controlled. The simulation of transmission networks needs to combine models of social network, transmission, evolution, and ideally sampling biases and errors (Villandre et al., 2016) .We introduce FAVITES (FrAmework for VIral Transmission and Evolution Simulation), which can simulate numerous models of contact networks, viral transmission, phylogenetic and sequence evolution, data (sub)sampling, and real-world data perturbations, and which was built to be flexible such that users can seamlessly plug in statistical models at every step of the simulation process. Previous attempts to create an epidemic simulation tool include epinet (Groendyke et al., 2012), TreeSim (Stadler and Bonhoeffer, 2013), outbreaker (Jombart et al., 2014), seedy (Worby and Read, 2015), and...

HIV Care Prioritization using Phylogenetic Branch Length

“…In order to test ProACT’s efficacy, we performed a series of simulation experiments in which we used FAVITES (Moshiri et al, 2018) to generate a sexual contact network, transmission network, viral phylogeny, and viral sequences emulating HIV transmission in San Diego from 2005 to 2014 (Material and Methods). We have simulated nine model conditions (Table 1) by starting from a base model condition and varying the rate of ART initiation (λ + ), rate of ART termination (λ − ), and the expected degree of the sexual network ( E d ).…”

“…We use FAVITES to simulate a sexual contact network, transmission network, viral phylogeny, and viral sequences emulating HIV transmission in San Diego from 2005 to 2014 (Moshiri et al, 2018).…”

“…On a large dataset from New York, they show that the approach is able to predict individuals who have relatively larger numbers of transmission links in the near future. Moshiri et al (2018) have studied the same question in simulations and have shown that monitoring cluster growth can be used for predicting future transmissions substantially better than a random guess, whether clusters are defined using genetic distances or using phylogenetic methods. Most recently, Balaban et al (2019) showed in simulations that using a cluster-monitoring approach similar to that of Wertheim et al (2018) but defining clusters using a min-cut optimization problem gives a small but consistent improvement over defining clusters using genetic distances.…”

EoN (Epidemics on Networks): a fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks

“…However for complex contagions, we cannot determine who is responsible for inducing a transition, so the implementation does not provide a transmission tree. The transmission tree is useful for constructing synthetic phylogenies as in [18].…”

nosoi: a stochastic agent-based transmission chain simulation framework in R

“…In that context, a simulated dataset is extremely useful as the exact transmission history is known and can be compared to the histories inferred from different software packages. The last decade has seen the development of several integrated epidemic and genetic simulation tools that can be used to assess the performance of some of these models, such as TreeSim [14], SEEDY [15], outbreaker2 [16], or FAVITES [17].…”