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

SEEPS: A Simulation Tool for Understanding Impacts of Contact Tracing on Epidemiological Inference from Phylogenetic Data

Motivation

Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, is not well understood. To understand how this non-uniform sampling method influences a recovered phylogeny, a new simulation tool is needed.

Results

We developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. We also examined real data from a 2007-2008 Swedish HIV-1 outbreak and the broader 1998-2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak.

Availability

SEEPS is available at github.com/MolEvolEpid/SEEPS.

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Leveraging social networks for identification of people with HIV who are virally unsuppressed

OBJECTIVES

This study investigates primary peer-referral engagement (PRE) strategies to assess which strategy results in engaging higher numbers of people with HIV (PWH) who are virally unsuppressed.

DESIGN

We develop a modeling study that simulates an HIV epidemic (transmission, disease progression, and viral evolution) over 6 years using an agent-based model followed by simulating PRE strategies. We investigate two PRE strategies where referrals are based on social network strategies (SNS) or sexual partner contact tracing (SPCT).

METHODS

We parameterize, calibrate, and validate our study using data from Chicago on Black sexual minority men to assess these strategies for a population with high incidence and prevalence of HIV. For each strategy, we calculate the number of PWH recruited who are undiagnosed or out-of-care (OoC) and the number of direct or indirect transmissions.

RESULTS

SNS and SPCT identified 256.5 [95% confidence interval (CI) 234-279] and 15 (95% CI 7-27) PWH, respectively. Of these, SNS identified 159 (95% CI 142-177) PWH OoC and 32 (95% CI 21-43) PWH undiagnosed compared with 9 (95% CI 3-18) and 2 (95% CI 0-5) for SPCT. SNS identified 15.5 (95% CI 6-25) and 7.5 (95% CI 2-11) indirect and direct transmission pairs, whereas SPCT identified 6 (95% CI 0-8) and 5 (95% CI 0-8), respectively.

CONCLUSION

With no testing constraints, SNS is the more effective strategy to identify undiagnosed and OoC PWH. Neither strategy is successful at identifying sufficient indirect or direct transmission pairs to investigate transmission networks.

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Estimating contact network properties by integrating multiple data sources associated with infectious diseases

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.

Graph-Based Reconstruction and Analysis of Disease Transmission Networks Using Viral Genomic Data

Understanding the patterns of viral disease transmissions helps establish public health policies and aids in controlling and ending a disease outbreak. Classical methods for studying disease transmission dynamics that rely on epidemiological data, such as times of sample collection and duration of exposure intervals, struggle to provide desired insight due to limited informativeness of such data. A more precise characterization of disease transmissions may be acquired from sequencing data that reveal genetic distance between viral genomes in patient samples. Indeed, genetic distance between viral strains present in hosts contains valuable information about transmission history, thus motivating the design of methods that rely on genomic data to reconstruct a directed disease transmission network, detect transmission clusters, and identify significant network nodes (e.g., super-spreaders). In this article, we present a novel end-to-end framework for the analysis of viral transmissions utilizing viral genomic (sequencing) data. The proposed framework groups infected hosts into transmission clusters based on the reconstructed viral strains infecting them; the genetic distance between a pair of hosts is calculated using Earth Mover's Distance, and further used to infer transmission direction between the hosts. To quantify the significance of a host in the transmission network, the importance score is calculated by a graph convolutional autoencoder. The viral transmission network is represented by a directed minimum spanning tree utilizing the Edmond's algorithm modified to incorporate constraints on the importance scores of the hosts. The proposed framework outperforms state-of-the-art techniques for the analysis of viral transmission dynamics in several experiments on semiexperimental as well as experimental data.

ViQUF: De Novo Viral Quasispecies Reconstruction Using Unitig-Based Flow Networks

During viral infection, intrahost mutation and recombination can lead to significant evolution, resulting in a population of viruses that harbor multiple haplotypes. The task of reconstructing these haplotypes from short-read sequencing data is called viral quasispecies assembly, and it can be categorized as a multiassembly problem. We consider the de novo version of the problem, where no reference is available. We present ViQUF, a de novo viral quasispecies assembler that addresses haplotype assembly and quantification. ViQUF obtains a first draft of the assembly graph from a de Bruijn graph. Then, solving a min-cost flow over a flow network built for each pair of adjacent vertices based on their paired-end information creates an approximate paired assembly graph with suggested frequency values as edge labels, which is the first frequency estimation. Then, original haplotypes are obtained through a greedy path reconstruction guided by a min-cost flow solution in the approximate paired assembly graph. ViQUF outputs the contigs with their frequency estimations. Results on real and simulated data show that ViQUF is at least four times faster using at most half of the memory than previous methods, while maintaining, and in some cases outperforming, the high quality of assembly and frequency estimation of overlap graph-based methodologies, which are known to be more accurate but slower than the de Bruijn graph-based approaches.

A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods

Affinity maturation (AM) of B cells through somatic hypermutations (SHMs) enables the immune system to evolve to recognize diverse pathogens. The accumulation of SHMs leads to the formation of clonal lineages of antibody-secreting b cells that have evolved from a common naïve B cell. Advances in high-throughput sequencing have enabled deep scans of B cell receptor repertoires, paving the way for reconstructing clonal trees. However, it is not clear if clonal trees, which capture microevolutionary time scales, can be reconstructed using traditional phylogenetic reconstruction methods with adequate accuracy. In fact, several clonal tree reconstruction methods have been developed to fix supposed shortcomings of phylogenetic methods. Nevertheless, no consensus has been reached regarding the relative accuracy of these methods, partially because evaluation is challenging. Benchmarking the performance of existing methods and developing better methods would both benefit from realistic models of clonal lineage evolution specifically designed for emulating B cell evolution. In this paper, we propose a model for modeling B cell clonal lineage evolution and use this model to benchmark several existing clonal tree reconstruction methods. Our model, designed to be extensible, has several features: by evolving the clonal tree and sequences simultaneously, it allows modeling selective pressure due to changes in affinity binding; it enables scalable simulations of large numbers of cells; it enables several rounds of infection by an evolving pathogen; and, it models building of memory. In addition, we also suggest a set of metrics for comparing clonal trees and measuring their properties. Our results show that while maximum likelihood phylogenetic reconstruction methods can fail to capture key features of clonal tree expansion if applied naively, a simple post-processing of their results, where short branches are contracted, leads to inferences that are better than alternative methods.

Optimized phylogenetic clustering of HIV-1 sequence data for public health applications

Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.

SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.

The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

Understanding the circumstances that lead to pandemics is important for their prevention. We analyzed the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted "A" and "B." Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October to 8 December), and the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans before November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events.

VGsim: Scalable viral genealogy simulator for global pandemic

Accurate simulation of complex biological processes is an essential component of developing and validating new technologies and inference approaches. As an effort to help contain the COVID-19 pandemic, large numbers of SARS-CoV-2 genomes have been sequenced from most regions in the world. More than 5.5 million viral sequences are publicly available as of November 2021. Many studies estimate viral genealogies from these sequences, as these can provide valuable information about the spread of the pandemic across time and space. Additionally such data are a rich source of information about molecular evolutionary processes including natural selection, for example allowing the identification of new variants with transmissibility and immunity evasion advantages. To our knowledge, there is no framework that is both efficient and flexible enough to simulate the pandemic to approximate world-scale scenarios and generate viral genealogies of millions of samples. Here, we introduce a new fast simulator VGsim which addresses the problem of simulation genealogies under epidemiological models. The simulation process is split into two phases. During the forward run the algorithm generates a chain of population-level events reflecting the dynamics of the pandemic using an hierarchical version of the Gillespie algorithm. During the backward run a coalescent-like approach generates a tree genealogy of samples conditioning on the population-level events chain generated during the forward run. Our software can model complex population structure, epistasis and immunity escape.

We develop a fast and flexible simulation software package VGsim for modeling epidemiological processes and generating genealogies of large pathogen samples. The software takes into account host population structure, pathogen evolution, host immunity and some other epidemiological aspects. The computational efficiency of the package allows to simulate genealogies of tens of millions of samples, which is important, e.g., for SARS-CoV-2 genome studies.

Phylogenetic prioritization of HIV-1 transmission clusters with viral lineage-level diversification rates

Background and objectives

Public health officials faced with a large number of transmission clusters require a rapid, scalable and unbiased way to prioritize distribution of limited resources to maximize benefits. We hypothesize that transmission cluster prioritization based on phylogenetically derived lineage-level diversification rates will perform as well as or better than commonly used growth-based prioritization measures, without need for historical data or subjective interpretation.

Methodology

9822 HIV pol sequences collected during routine drug resistance genotyping were used alongside simulated sequence data to infer sets of phylogenetic transmission clusters via patristic distance threshold. Prioritized clusters inferred from empirical data were compared to those prioritized by the current public health protocols. Prioritization of simulated clusters was evaluated based on correlation of a given prioritization measure with future cluster growth, as well as the number of direct downstream transmissions from cluster members.

Results

Empirical data suggest diversification rate-based measures perform comparably to growth-based measures in recreating public heath prioritization choices. However, unbiased simulated data reveals phylogenetic diversification rate-based measures perform better in predicting future cluster growth relative to growth-based measures, particularly long-term growth. Diversification rate-based measures also display advantages over growth-based measures in highlighting groups with greater future transmission events compared to random groups of the same size. Furthermore, diversification rate measures were notably more robust to effects of decreased sampling proportion.

Conclusions and implications

Our findings indicate diversification rate-based measures frequently outperform growth-based measures in predicting future cluster growth and offer several additional advantages beneficial to optimizing the public health prioritization process.

TNet: Transmission Network Inference Using Within-Host Strain Diversity and its Application to Geographical Tracking of COVID-19 Spread

The inference of disease transmission networks is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference are highly computationally intensive and cannot take within-host strain diversity into account. Here, we introduce a new phylogenetic approach for inferring transmission networks, TNet, that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity. We also applied TNet to a large collection of SARS-CoV-2 genomes sampled from infected individuals in many countries around the world, demonstrating how our inference framework can be adapted to accurately infer geographical transmission networks. TNet is freely available from https://compbio.engr.uconn.edu/software/TNet/.

SEPIA: simulation-based evaluation of prioritization algorithms

Background

The ability to prioritize people living with HIV (PLWH) by risk of future transmissions could aid public health officials in optimizing epidemiological intervention. While methods exist to perform such prioritization based on molecular data, their effectiveness and accuracy are poorly understood, and it is unclear how one can directly compare the accuracy of different methods. We introduce SEPIA (Simulation-based Evaluation of PrIoritization Algorithms), a novel simulation-based framework for determining the effectiveness of prioritization algorithms. SEPIA expands upon prior related work by defining novel metrics of effectiveness with which to compare prioritization techniques, as well as by creating a simulation-based tool with which to perform such effectiveness comparisons. Under several metrics of effectiveness that we propose, we compare two existing prioritization approaches: one phylogenetic (ProACT) and one distance-based (growth of HIV-TRACE transmission clusters).

Results

Using all proposed metrics, ProACT consistently slightly outperformed the transmission cluster growth approach. However, both methods consistently performed just marginally better than random, suggesting that there is significant room for improvement in prioritization tools.

Conclusion

We hope that, by providing ways to quantify the effectiveness of prioritization methods in simulation, SEPIA will aid researchers in developing novel risk prioritization tools for PLWH.

Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases

Agent-based network modeling (ABNM) simulates each person at the individual-level as agents of the simulation, and uses network generation algorithms to generate the network of contacts between individuals. ABNM are suitable for simulating individual-level dynamics of infectious diseases, especially for diseases such as HIV that spread through close contacts within intricate contact networks. However, as ABNM simulates a scaled-version of the full population, consisting of all infected and susceptible persons, they are computationally infeasible for studying certain questions in low prevalence diseases such as HIV. We present a new simulation technique, agent-based evolving network modeling (ABENM), which includes a new network generation algorithm, Evolving Contact Network Algorithm (ECNA), for generating scale-free networks. ABENM simulates only infected persons and their immediate contacts at the individual-level as agents of the simulation, and uses the ECNA for generating the contact structures between these individuals. All other susceptible persons are modeled using a compartmental modeling structure. Thus, ABENM has a hybrid agent-based and compartmental modeling structure. The ECNA uses concepts from graph theory for generating scale-free networks. Multiple social networks, including sexual partnership networks and needle sharing networks among injecting drug-users, are known to follow a scale-free network structure. Numerical results comparing ABENM with ABNM estimations for disease trajectories of hypothetical diseases transmitted on scale-free contact networks are promising for application to low prevalence diseases.

Timing the SARS-CoV-2 index case in Hubei province

Understanding when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged is critical to evaluating our current approach to monitoring novel zoonotic pathogens and understanding the failure of early containment and mitigation efforts for COVID-19. We used a coalescent framework to combine retrospective molecular clock inference with forward epidemiological simulations to determine how long SARS-CoV-2 could have circulated before the time of the most recent common ancestor of all sequenced SARS-CoV-2 genomes. Our results define the period between mid-October and mid-November 2019 as the plausible interval when the first case of SARS-CoV-2 emerged in Hubei province, China. By characterizing the likely dynamics of the virus before it was discovered, we show that more than two-thirds of SARS-CoV-2-like zoonotic events would be self-limited, dying out without igniting a pandemic. Our findings highlight the shortcomings of zoonosis surveillance approaches for detecting highly contagious pathogens with moderate mortality rates.

HIV Care Prioritization Using Phylogenetic Branch Length

BACKGROUND

The structure of the HIV transmission networks can be dictated by just a few individuals. Public health intervention, such as ensuring people living with HIV adhere to antiretroviral therapy and remain virally suppressed, can help control the spread of the virus. However, such intervention requires using limited public health resource allocations. Determining which individuals are most at risk of transmitting HIV could allow public health officials to focus their limited resources on these individuals.

SETTING

Molecular epidemiology can help prioritize people living with HIV by patterns of transmission inferred from their sampled viral sequences. Such prioritization has been previously suggested and performed by monitoring cluster growth. In this article, we introduce Prioritization using AnCesTral edge lengths (ProACT), a phylogenetic approach for prioritizing individuals living with HIV.

METHODS

ProACT starts from a phylogeny inferred from sequence data and orders individuals according to their terminal branch length, breaking ties using ancestral branch lengths. We evaluated ProACT on a real data set of 926 HIV-1 subtype B pol data obtained in San Diego between 2005 and 2014 and a simulation data set modeling the same epidemic. Prioritization methods are compared by their ability to predict individuals who transmit most after the prioritization.

RESULTS

Across all simulation conditions and most real data sampling conditions, ProACT outperformed monitoring cluster growth for multiple metrics of prioritization efficacy.

CONCLUSION

The simple strategy used by ProACT improves the effectiveness of prioritization compared with state-of-the-art methods that rely on monitoring the growth of transmission clusters defined based on genetic distance.

Inference of latent event times and transmission networks in individual level infectious disease models

Transmission networks indicate who-infected-whom in epidemics. Reconstruction of transmission networks is invaluable in applying and developing effective control strategies for infectious diseases. We introduce transmission network individual level models (TN-ILMs), a competing-risk, continuous time extension to individual level model framework for infectious diseases of Deardon et al. (2010). Through simulation study using a Julia language software package, Pathogen.jl, we explore the models with respect to their ability to jointly infer latent event times, latent disease transmission networks, and the TN-ILM parameters. We find good parameter, event time, and transmission network inference, with enhanced performance for inference of transmission networks in epidemic simulations that have higher spatial signals in their infectivity kernel. Finally, an application of a TN-ILM to data from a greenhouse experiment on the spread of tomato spotted wilt virus is presented.

Timing the SARS-CoV-2 Index Case in Hubei Province

Understanding when SARS-CoV-2 emerged is critical to evaluating our current approach to monitoring novel zoonotic pathogens and understanding the failure of early containment and mitigation efforts for COVID-19. We employed a coalescent framework to combine retrospective molecular clock inference with forward epidemiological simulations to determine how long SARS-CoV-2 could have circulated prior to the time of the most recent common ancestor. Our results define the period between mid-October and mid-November 2019 as the plausible interval when the first case of SARS-CoV-2 emerged in Hubei province. By characterizing the likely dynamics of the virus before it was discovered, we show that over two-thirds of SARS-CoV-2-like zoonotic events would be self-limited, dying out without igniting a pandemic. Our findings highlight the shortcomings of zoonosis surveillance approaches for detecting highly contagious pathogens with moderate mortality rates.

Short Communication: Choosing the Right Program for the Identification of HIV-1 Transmission Networks from Nucleotide Sequences Sampled from Different Populations

HIV-TRAnsmission Cluster Engine (HIV-TRACE) and Cluster Picker are some of the most widely used programs for identifying HIV-1 transmission networks from nucleotide sequences. However, choosing between these tools is subjective and often a matter of personal preference. Because these software use different algorithms to detect HIV-1 transmission networks, their optimal use is better suited with different sequence data sets and under different scenarios. The performance of these tools has previously been evaluated across a range of genetic distance thresholds without an assessment of the differences in the structure of networks identified. In this study, we tested both programs on the same HIV-1 pol sequence data set (n = 2,017) from three Ugandan populations to examine their performance across different risk groups and evaluate the structure of networks identified. HIV-TRACE that uses a single-linkage algorithm identified more nodes in the same networks that were connected by sparse links than Cluster Picker. This suggests that the choice of the program used for identifying networks should depend on the study aims, the characteristics of the population being investigated, dynamics of the epidemic, sampling design, and the nature of research questions being addressed for optimum results. HIV-TRACE could be more applicable with larger data sets where the aim is to identify larger clusters that represent distinct transmission chains and in more diverse populations where infection has occurred over a period of time. In contrast, Cluster Picker is applicable in situations where more closely connected clusters are expected in the studied populations.

The emergence of SARS-CoV-2 in Europe and North America

Accurate understanding of the global spread of emerging viruses is critical for public health responses and for anticipating and preventing future outbreaks. Here we elucidate when, where, and how the earliest sustained severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission networks became established in Europe and North America. Our results suggest that rapid early interventions successfully prevented early introductions of the virus from taking hold in Germany and the United States. Other, later introductions of the virus from China to both Italy and Washington state, United States, founded the earliest sustained European and North America transmission networks. Our analyses demonstrate the effectiveness of public health measures in preventing onward transmission and show that intensive testing and contact tracing could have prevented SARS-CoV-2 outbreaks from becoming established in these regions.

nosoi: A stochastic agent-based transmission chain simulation framework in r

The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavour, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed.We here introduce nosoi, an open-source r package that offers a complete, tunable and expandable agent-based framework to simulate transmission chains under a wide range of epidemiological scenarios for single-host and dual-host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations.Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user-specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can-for example-be used to validate a wide range of reconstruction methods, including epidemic modelling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a hands-on exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.

The emergence of SARS-CoV-2 in Europe and the US

Accurate understanding of the global spread of emerging viruses is critically important for public health response and for anticipating and preventing future outbreaks. Here, we elucidate when, where and how the earliest sustained SARS-CoV-2 transmission networks became established in Europe and the United States (US). Our results refute prior findings erroneously linking cases in January 2020 with outbreaks that occurred weeks later. Instead, rapid interventions successfully prevented onward transmission of those early cases in Germany and Washington State. Other, later introductions of the virus from China to both Italy and Washington State founded the earliest sustained European and US transmission networks. Our analyses reveal an extended period of missed opportunity when intensive testing and contact tracing could have prevented SARS-CoV-2 from becoming established in the US and Europe.

Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic

Influenza A viruses cause seasonal epidemics and occasional pandemics in the human population. While the worldwide circulation of seasonal influenza is at least partly understood, the exact migration patterns between countries, states or cities are not well studied. Here, we use the Sankoff algorithm for parsimonious phylogeographic reconstruction together with effective distances based on a worldwide air transportation network. By first simulating geographic spread and then phylogenetic trees and genetic sequences, we confirmed that reconstructions with effective distances inferred phylogeographic spread more accurately than reconstructions with geographic distances and Bayesian reconstructions with BEAST that do not use any distance information, and led to comparable results to the Bayesian reconstruction using distance information via a generalized linear model. Our method extends Bayesian methods that estimate rates from the data by using fine-grained locations like airports and inferring intermediate locations not observed among sampled isolates. When applied to sequence data of the pandemic H1N1 influenza A virus in 2009, our approach correctly inferred the origin and proposed airports mainly involved in the spread of the virus. In case of a novel outbreak, this approach allows to rapidly analyze sequence data and infer origin and spread routes to improve disease surveillance and control.

Influenza A viruses infect up to 5 million people in recurring epidemics every year. Further, viruses of zoonotic origin constantly pose a pandemic risk. Understanding the geographical spread of these viruses, including the origin and the main spread routes between cities, states or countries, could help to monitor or contain novel outbreaks. Based on genetic sequences and sampling locations, the geographic spread can be reconstructed along a phylogenetic tree. Our approach uses a parsimonious reconstruction with air transportation data and was verified using a simulation of the 2009 H1N1 influenza A pandemic. Applied to real sequence data of the outbreak, our analysis gave detailed insights into spread patterns of influenza A viruses, highlighting the origin as well as airports mainly involved in the spread.

TreeSwift: A massively scalable Python tree package

Phylogenetic trees are essential to evolutionary biology, and numerous methods exist that attempt to extract phylogenetic information applicable to a wide range of disciplines, such as epidemiology and metagenomics. Currently, the three main Python packages for trees are Bio.Phylo, DendroPy, and the ETE Toolkit, but as dataset sizes grow, parsing and manipulating ultra-large trees becomes impractical for these tools. To address this issue, we present TreeSwift, a user-friendly and massively scalable Python package for traversing and manipulating trees that is ideal for algorithms performed on ultra-large trees.

SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic “intervention” event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework.

TreeCluster: Clustering biological sequences using phylogenetic trees

Clustering homologous sequences based on their similarity is a problem that appears in many bioinformatics applications. The fact that sequences cluster is ultimately the result of their phylogenetic relationships. Despite this observation and the natural ways in which a tree can define clusters, most applications of sequence clustering do not use a phylogenetic tree and instead operate on pairwise sequence distances. Due to advances in large-scale phylogenetic inference, we argue that tree-based clustering is under-utilized. We define a family of optimization problems that, given an arbitrary tree, return the minimum number of clusters such that all clusters adhere to constraints on their heterogeneity. We study three specific constraints, limiting (1) the diameter of each cluster, (2) the sum of its branch lengths, or (3) chains of pairwise distances. These three problems can be solved in time that increases linearly with the size of the tree, and for two of the three criteria, the algorithms have been known in the theoretical computer scientist literature. We implement these algorithms in a tool called TreeCluster, which we test on three applications: OTU clustering for microbiome data, HIV transmission clustering, and divide-and-conquer multiple sequence alignment. We show that, by using tree-based distances, TreeCluster generates more internally consistent clusters than alternatives and improves the effectiveness of downstream applications. TreeCluster is available at https://github.com/niemasd/TreeCluster.