Phylogenetic and epidemic modeling of rapidly evolving infectious diseases

Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021

Phylodynamic methods have lately played a key role in understanding the spread of infectious diseases. During the coronavirus disease (COVID-19) pandemic, large scale genomic surveillance has further increased the potential of dynamic inference from viral genomes. With the continual emergence of novel severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) variants, explicitly allowing transmission rate differences between simultaneously circulating variants in phylodynamic inference is crucial. In this study, we present and empirically validate an extension to the BEAST2 package birth-death skyline model (BDSKY), BDSKY[Formula: see text], which introduces a scaling factor for the transmission rate between independent, jointly inferred trees. In an extensive simulation study, we show that BDSKY[Formula: see text] robustly infers the relative transmission rates under different epidemic scenarios. Using publicly available genome data of SARS-CoV-2, we apply BDSKY[Formula: see text] to quantify the transmission advantage of the Omicron over the Delta variant in Berlin, Germany. We find the overall transmission rate of Omicron to be scaled by a factor of two with pronounced variation between the individual clusters of each variant. These results quantify the transmission advantage of Omicron over the previously circulating Delta variant, in a crucial period of pre-established non-pharmaceutical interventions. By inferring variant- as well as cluster-specific transmission rate scaling factors, we show the differences in transmission dynamics for each variant. This highlights the importance of incorporating lineage-specific transmission differences in phylodynamic inference.

Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador

Human movement may be an important driver of transmission dynamics for enteric pathogens but has largely been underappreciated except for international 'travelers' diarrhea or cholera. Phylodynamic methods, which combine genomic and epidemiological data, are used to examine rates and dynamics of disease matching underlying evolutionary history and biogeographic distributions, but these methods often are not applied to enteric bacterial pathogens. We used phylodynamics to explore the phylogeographic and evolutionary patterns of diarrheagenic E. coli in northern Ecuador to investigate the role of human travel in the geographic distribution of strains across the country. Using whole genome sequences of diarrheagenic E. coli isolates, we built a core genome phylogeny, reconstructed discrete ancestral states across urban and rural sites, and estimated migration rates between E. coli populations. We found minimal structuring based on site locations, urban vs. rural locality, pathotype, or clinical status. Ancestral states of phylogenomic nodes and tips were inferred to have 51% urban ancestry and 49% rural ancestry. Lack of structuring by location or pathotype E. coli isolates imply highly connected communities and extensive sharing of genomic characteristics across isolates. Using an approximate structured coalescent model, we estimated rates of migration among circulating isolates were 6.7 times larger for urban towards rural populations compared to rural towards urban populations. This suggests increased inferred migration rates of diarrheagenic E. coli from urban populations towards rural populations. Our results indicate that investments in water and sanitation prevention in urban areas could limit the spread of enteric bacterial pathogens among rural populations.

Inferring Human Immunodeficiency Virus 1 Proviral Integration Dates With Bayesian Inference

Human immunodeficiency virus 1 (HIV) proviruses archived in the persistent reservoir currently pose the greatest obstacle to HIV cure due to their evasion of combined antiretroviral therapy and ability to reseed HIV infection. Understanding the dynamics of the HIV persistent reservoir is imperative for discovering a durable HIV cure. Here, we explore Bayesian methods using the software BEAST2 to estimate HIV proviral integration dates. We started with within-host longitudinal HIV sequences collected prior to therapy, along with sequences collected from the persistent reservoir during suppressive therapy. We built a BEAST2 model to estimate integration dates of proviral sequences collected during suppressive therapy, implementing a tip date random walker to adjust the sequence tip dates and a latency-specific prior to inform the dates. To validate our method, we implemented it on both simulated and empirical data sets. Consistent with previous studies, we found that proviral integration dates were spread throughout active infection. Path sampling to select an alternative prior for date estimation in place of the latency-specific prior produced unrealistic results in one empirical data set, whereas on another data set, the latency-specific prior was selected as best fitting. Our Bayesian method outperforms current date estimation techniques with a root mean squared error of 0.89 years on simulated data relative to 1.23-1.89 years with previously developed methods. Bayesian methods offer an adaptable framework for inferring proviral integration dates.

Global dissemination of influenza A virus is driven by wild bird migration through arctic and subarctic zones

Influenza A viruses (IAV) circulate endemically among many wild aquatic bird populations that seasonally migrate between wintering grounds in southern latitudes to breeding ranges along the perimeter of the circumpolar arctic. Arctic and subarctic zones are hypothesized to serve as ecologic drivers of the intercontinental movement and reassortment of IAVs due to high densities of disparate populations of long distance migratory and native bird species present during breeding seasons. Iceland is a staging ground that connects the East Atlantic and North Atlantic American flyways, providing a unique study system for characterizing viral flow between eastern and western hemispheres. Using Bayesian phylodynamic analyses, we sought to evaluate the viral connectivity of Iceland to proximal regions and how inter-species transmission and reassortment dynamics in this region influence the geographic spread of low and highly pathogenic IAVs. Findings demonstrate that IAV movement in the arctic and subarctic reflects wild bird migration around the perimeter of the circumpolar north, favouring short-distance flights between proximal regions rather than long distance flights over the polar interior. Iceland connects virus movement between mainland Europe and North America, consistent with the westward migration of wild birds from mainland Europe to Northeastern Canada and Greenland. Though virus diffusion rates were similar among avian taxonomic groups in Iceland, gulls play an outsized role as sinks of IAVs from other avian hosts prior to onward migration. These data identify patterns of virus movement in northern latitudes and inform future surveillance strategies related to seasonal and emergent IAVs with potential public health concern.

Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2

Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.

Robust Phylodynamic Analysis of Genetic Sequencing Data from Structured Populations

The multi-type birth–death model with sampling is a phylodynamic model which enables the quantification of past population dynamics in structured populations based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were computationally limited to trees consisting of up to approximately 250 genetic samples. We implemented important algorithmic changes to bdmm which dramatically increased the number of genetic samples that could be analyzed and which improved the numerical robustness and efficiency of the calculations. Including more samples led to the improved precision of parameter estimates, particularly for structured models with a high number of inferred parameters. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We applied this improved algorithm to two partly overlapping datasets of the Influenza A virus HA sequences sampled around the world—one with 500 samples and the other with only 175—for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. In this way, we show the information that is gained by analyzing the bigger dataset, which became possible with the presented algorithmic changes to bdmm. In summary, bdmm allows for the robust, faster, and more general phylodynamic inference of larger datasets.

Women in the European Virus Bioinformatics Center

Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing "Big Data" offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics.

African mitochondrial haplogroup L7: a 100,000-year-old maternal human lineage discovered through reassessment and new sequencing

Archaeological and genomic evidence suggest that modern Homo sapiens have roamed the planet for some 300–500 thousand years. In contrast, global human mitochondrial (mtDNA) diversity coalesces to one African female ancestor (“Mitochondrial Eve”) some 145 thousand years ago, owing to the ¼ gene pool size of our matrilineally inherited haploid genome. Therefore, most of human prehistory was spent in Africa where early ancestors of Southern African Khoisan and Central African rainforest hunter-gatherers (RFHGs) segregated into smaller groups. Their subdivisions followed climatic oscillations, new modes of subsistence, local adaptations, and cultural-linguistic differences, all prior to their exodus out of Africa. Seven African mtDNA haplogroups (L0–L6) traditionally captured this ancient structure—these L haplogroups have formed the backbone of the mtDNA tree for nearly two decades. Here we describe L7, an eighth haplogroup that we estimate to be ~ 100 thousand years old and which has been previously misclassified in the literature. In addition, L7 has a phylogenetic sublineage L7a*, the oldest singleton branch in the human mtDNA tree (~ 80 thousand years). We found that L7 and its sister group L5 are both low-frequency relics centered around East Africa, but in different populations (L7: Sandawe; L5: Mbuti). Although three small subclades of African foragers hint at the population origins of L5'7, the majority of subclades are divided into Afro-Asiatic and eastern Bantu groups, indicative of more recent admixture. A regular re-estimation of the entire mtDNA haplotype tree is needed to ensure correct cladistic placement of new samples in the future.

Epidemiological Inference From Pathogen Genomes: A Review of Phylodynamic Models and Applications

Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.

Threat, challenges, and preparedness for future pandemics: A descriptive review of phylogenetic analysis based predictions

For centuries the world has been confronted with many infectious diseases, with a potential to turn into a pandemic posing a constant threat to human lives. Some of these pandemics occurred due to the emergence of new disease or re-emergence of previously known diseases with a few mutations. In such scenarios their optimal prevention and control options were not adequately developed. Most of these diseases are highly contagious and for their timely control, knowledge about the pathogens and disease progression is the basic necessity. In this review, we have presented a documented chronology of the earlier pandemics, evolutionary analysis of the infectious disease with pandemic potential, the role of RNA, difficulties in controlling pandemics, and the likely pathogens that could trigger future pandemics. In this study, the evolutionary history of the pathogens was identified by carrying out phylogenetic analysis. The percentage similarity between different infectious diseases is critically analysed for the identification of their correlation using online sequence matcher tools. The Baltimore classification system was used for finding the genomic nature of the viruses. It was observed that most of the infectious pathogens rise from their animal hosts with some mutations in their genome composition. The phylogenetic tree shows that the single-stranded RNA diseases have a common origin and many of them are having high similarity percentage. The outcomes of this study will help in the identification of potential pathogens that can cause future pandemics. This information will be helpful in the development of early detection techniques, devising preventive mechanism to limit their spread, prophylactic measures, Infection control and therapeutic options, thereby, strengthening our approach towards global preparedness against future pandemics.

Accounting for spatial sampling patterns in Bayesian phylogeography

Statistical phylogeography has led to substantial progress in our understanding of the pace and means by which organisms colonize their habitats. Yet, inference from these models often relies on implicit assumptions pertaining to spatial sampling design, potentially leading to biased estimation of key biological parameters. While sampled locations sometimes convey signal about the processes that shape spatial biodiversity, they do not always do so. We present a statistical approach that permits accurate estimation of dispersal rates, even in cases where spatial sampling is driven by practical motivations unrelated to the outcome of the evolutionary process. The proposed framework paves the way to further developments in phylogeography with key applications, including the efficient monitoring of pandemics and invasive species during the course of their evolution.

Statistical phylogeography provides useful tools to characterize and quantify the spread of organisms during the course of evolution. Analyzing georeferenced genetic data often relies on the assumption that samples are preferentially collected in densely populated areas of the habitat. Deviation from this assumption negatively impacts the inference of the spatial and demographic dynamics. This issue is pervasive in phylogeography. It affects analyses that approximate the habitat as a set of discrete demes as well as those that treat it as a continuum. The present study introduces a Bayesian modeling approach that explicitly accommodates for spatial sampling strategies. An original inference technique, based on recent advances in statistical computing, is then described that is most suited to modeling data where sequences are preferentially collected at certain locations, independently of the outcome of the evolutionary process. The analysis of georeferenced genetic sequences from the West Nile virus in North America along with simulated data shows how assumptions about spatial sampling may impact our understanding of the forces shaping biodiversity across time and space.

Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020

By October 2021, 230 million SARS-CoV-2 diagnoses have been reported. Yet, a considerable proportion of cases remains undetected. Here, we propose GInPipe, a method that rapidly reconstructs SARS-CoV-2 incidence profiles solely from publicly available, time-stamped viral genomes. We validate GInPipe against simulated outbreaks and elaborate phylodynamic analyses. Using available sequence data, we reconstruct incidence histories for Denmark, Scotland, Switzerland, and Victoria (Australia) and demonstrate, how to use the method to investigate the effects of changing testing policies on case ascertainment. Specifically, we find that under-reporting was highest during summer 2020 in Europe, coinciding with more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. In post-pandemic times, when diagnostic efforts are decreasing, GInPipe may facilitate the detection of hidden infection dynamics.

What can phylodynamics bring to animal health research?

Infectious diseases are a major burden to global economies, and public and animal health. To date, quantifying the spread of infectious diseases to inform policy making has traditionally relied on epidemiological data collected during epidemics. However, interest has grown in recent phylodynamic techniques to infer pathogen transmission dynamics from genetic data. Here, we provide examples of where this new discipline has enhanced disease management in public health and illustrate how it could be further applied in animal health. In particular, we describe how phylodynamics can address fundamental epidemiological questions, such as inferring key transmission parameters in animal populations and quantifying spillover events at the wildlife-livestock interface, and generate important insights for the design of more effective control strategies.

Repeated introductions and intensive community transmission fueled a mumps virus outbreak in Washington State

In 2016/2017, Washington State experienced a mumps outbreak despite high childhood vaccination rates, with cases more frequently detected among school-aged children and members of the Marshallese community. We sequenced 166 mumps virus genomes collected in Washington and other US states, and traced mumps introductions and transmission within Washington. We uncover that mumps was introduced into Washington approximately 13 times, primarily from Arkansas, sparking multiple co-circulating transmission chains. Although age and vaccination status may have impacted transmission, our data set could not quantify their precise effects. Instead, the outbreak in Washington was overwhelmingly sustained by transmission within the Marshallese community. Our findings underscore the utility of genomic data to clarify epidemiologic factors driving transmission and pinpoint contact networks as critical for mumps transmission. These results imply that contact structures and historic disparities may leave populations at increased risk for respiratory virus disease even when a vaccine is effective and widely used.

Retrieval and Investigation of Data on SARS-CoV-2 and COVID-19 Using Bioinformatics Approach

Sudden emergence and a rapid outbreak of SARS-CoV-2 accompanied by a devastating impact on the economy and public health has driven extensive scientific mobilization to study and elucidate the various associated concerns about SARS-CoV-2. Bioinformatics plays a crucial role in addressing and providing solutions to questions about SARS-CoV-2. It helps shorten the duration for the vaccine development process and the discovery of potential clinical interventions through the simulation and information retrieval, and the development of well-ordered information hubs and resources, which are essential to derive data and meaningful findings from the current massive information about SARS-CoV-2. Advanced algorithms in this field also provide approaches that are essential to elucidate the relationship, origin, and evolutionary process of SARS-CoV-2. Here, we report essential bioinformatics entities, such as database and platform development, molecular evolution and phylogenetic analyses, and vaccine designs, that are useful to solve the SARS-CoV-2 conundrum.

Molecular epidemiology in the HIV and SARS-CoV-2 pandemics

PURPOSE OF REVIEW

The aim of this review was to compare and contrast the application of molecular epidemiology approaches for the improved management and understanding of the HIV versus SARS-CoV-2 epidemics.

RECENT FINDINGS

Molecular biology approaches, including PCR and whole genome sequencing (WGS), have become powerful tools for epidemiological investigation. PCR approaches form the basis for many high-sensitivity diagnostic tests and can supplement traditional contact tracing and surveillance strategies to define risk networks and transmission patterns. WGS approaches can further define the causative agents of disease, trace the origins of the pathogen, and clarify routes of transmission. When coupled with clinical datasets, such as electronic medical record data, these approaches can investigate co-correlates of disease and pathogenesis. In the ongoing HIV epidemic, these approaches have been effectively deployed to identify treatment gaps, transmission clusters and risk factors, though significant barriers to rapid or real-time implementation remain critical to overcome. Likewise, these approaches have been successful in addressing some questions of SARS-CoV-2 transmission and pathogenesis, but the nature and rapid spread of the virus have posed additional challenges.

SUMMARY

Overall, molecular epidemiology approaches offer unique advantages and challenges that complement traditional epidemiological tools for the improved understanding and management of epidemics.

Phylodynamic analysis and evaluation of the balance between anthropic and environmental factors affecting IBV spreading among Italian poultry farms

Infectious bronchitis virus (IBV) control is mainly based on wide vaccine administration. Although effective, its efficacy is not absolute, the viral circulation is not prevented and some side effects cannot be denied. Despite this, the determinants of IBV epidemiology and the factors affecting its circulation are still largely unknown and poorly investigated. In the present study, 361 IBV QX (the most relevant field genotype in Italy) sequences were obtained between 2012 and 2016 from the two main Italian integrated poultry companies. Several biostatistical and bioinformatics approaches were used to reconstruct the history of the QX genotype in Italy and to assess the effect of different environmental, climatic and social factors on its spreading patterns. Moreover, two structured coalescent models were considered in order to investigate if an actual compartmentalization occurs between the two integrated poultry companies and the role of a third "ghost" deme, representative of minor industrial poultry companies and the rural sector. The obtained results suggest that the integration of the poultry companies is an effective barrier against IBV spreading, since the strains sampled from the two companies formed two essentially-independent clades. Remarkably, the only exceptions were represented by farms located in the high densely populated poultry area of Northern Italy. The inclusion of a third deme in the model revealed the likely role of other poultry companies and rural farms (particularly concentrated in Northern Italy) as sources of strain introduction into one of the major poultry companies, whose farms are mainly located in the high densely populated poultry area of Northern Italy. Accordingly, when the effect of different environmental and urban parameters on IBV geographic spreading was investigated, no factor seems to contribute to IBV dispersal velocity, being poultry population density the only exception. Finally, the different viral population pattern observed in the two companies over the same time period supports the pivotal role of management and control strategies on IBV epidemiology. Overall, the present study results stress the crucial relevance of human action rather than environmental factors, highlighting the direct benefits that could derive from improved management and organization of the poultry sector on a larger scale.

A brief history of bird flu

In 1918, a strain of influenza A virus caused a human pandemic resulting in the deaths of 50 million people. A century later, with the advent of sequencing technology and corresponding phylogenetic methods, we know much more about the origins, evolution and epidemiology of influenza epidemics. Here we review the history of avian influenza viruses through the lens of their genetic makeup: from their relationship to human pandemic viruses, starting with the 1918 H1N1 strain, through to the highly pathogenic epidemics in birds and zoonoses up to 2018. We describe the genesis of novel influenza A virus strains by reassortment and evolution in wild and domestic bird populations, as well as the role of wild bird migration in their long-range spread. The emergence of highly pathogenic avian influenza viruses, and the zoonotic incursions of avian H5 and H7 viruses into humans over the last couple of decades are also described. The threat of a new avian influenza virus causing a human pandemic is still present today, although control in domestic avian populations can minimize the risk to human health.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

Using phylogeographic approaches to analyse the dispersal history, velocity and direction of viral lineages - Application to rabies virus spread in Iran

Recent years have seen the extensive use of phylogeographic approaches to unveil the dispersal history of virus epidemics. Spatially explicit reconstructions of viral spread represent valuable sources of lineage movement data that can be exploited to investigate the impact of underlying environmental layers on the dispersal of pathogens. Here, we performed phylogeographic inference and applied different post hoc approaches to analyse a new and comprehensive data set of viral genomes to elucidate the dispersal history and dynamics of rabies virus (RABV) in Iran, which have remained largely unknown. We first analysed the association between environmental factors and variations in dispersal velocity among lineages. Second, we present, test and apply a new approach to study the link between environmental conditions and the dispersal direction of lineages. The statistical performance (power of detection, false-positive rate) of this new method was assessed using simulations. We performed phylogeographic analyses of RABV genomes, allowing us to describe the large diversity of RABV in Iran and to confirm the cocirculation of several clades in the country. Overall, we estimate a relatively high lineage dispersal velocity, similar to previous estimates for dog rabies virus spread in northern Africa. Finally, we highlight a tendency for RABV lineages to spread in accessible areas associated with high human population density. Our analytical workflow illustrates how phylogeographic approaches can be used to investigate the impact of environmental factors on several aspects of viral dispersal dynamics.

The phylogenetic approach for viral infectious disease evolution and epidemiology: An updating review

In the last decade, the phylogenetic approach is recurrent in molecular evolutionary analysis. On 12 May, 2019, about 2 296 213 papers are found, but typing "phylogeny" or "epidemiology AND phylogeny" only 199 804 and 20 133 are retrieved, respectively. Molecular epidemiology in infectious diseases is widely used to define the source of infection as so as the ancestral relationships of individuals sampled from a population. Coalescent theory and phylogeographic analysis have had scientific application in several, recent pandemic events, and nosocomial outbreaks. Hepatitis viruses and immunodeficiency virus (human immunodeficiency virus) have been largely studied. Phylogenetic analysis has been recently applied on Polyomaviruses so as in the more recent outbreaks due to different arboviruses type as Zika and chikungunya viruses discovering the source of infection and the geographic spread. Data on sequences isolated by the microorganism are essential to apply the phylogenetic tools and research in the field of infectious disease phylodinamics is growing up. There is the need to apply molecular phylogenetic and evolutionary methods in areas out of infectious diseases, as translational genomics and personalized medicine. Lastly, the application of these tools in vaccine strategy so as in antibiotic and antiviral researchers are encouraged.

Phylogenomic Pipeline Validation for Foodborne Pathogen Disease Surveillance

Foodborne pathogen surveillance in the United States is transitioning from strain identification using restriction digest technology (pulsed-field gel electrophoresis [PFGE]) to shotgun sequencing of the entire genome (whole-genome sequencing [WGS]). WGS requires a new suite of analysis tools, some of which have long histories in academia but are new to the field of public health and regulatory decision making. Although the general workflow is fairly standard for collecting and analyzing WGS data for disease surveillance, there are a number of differences in how the data are collected and analyzed across public health agencies, both nationally and internationally. This impedes collaborative public health efforts, so national and international efforts are underway to enable direct comparison of these different analysis methods. Ultimately, the harmonization efforts will allow the (mutually trusted and understood) production and analysis of WGS data by labs and agencies worldwide, thus improving outbreak response capabilities globally. This review provides a historical perspective on the use of WGS for pathogen tracking and summarizes the efforts underway to ensure the major steps in phylogenomic pipelines used for pathogen disease surveillance can be readily validated. The tools for doing this will ensure that the results produced are sound, reproducible, and comparable across different analytic approaches.

Phylodynamic Model Adequacy Using Posterior Predictive Simulations

Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that, it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy, a package for the popular BEAST2 software that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analyzing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data were best described by the birth–death susceptible-infected-recovered model.

Retrospective detection and phylogenetic analysis of swine acute diarrhoea syndrome coronavirus in pigs in southern China

Swine acute diarrhoea syndrome coronavirus (SADS‐CoV), a novel coronavirus, was first discovered in southern China in January 2017 and caused a large scale outbreak of fatal diarrheal disease in piglets. Here, we conducted a retrospective investigation of 236 samples from 45 swine farms with a clinical history of diarrheal disease to evaluate the emergence and the distribution of SADS‐CoV in pigs in China. Our results suggest that SADS‐CoV has emerged in China at least since August 2016. Meanwhile, we detected a prevalence of SADS‐CoV (43.53%), porcine deltacoronavirus (8.83%), porcine epidemic diarrhoea virus (PEDV) (78.25%), rotavirus (21.77%), and transmissible gastroenteritis virus (0%), and we also found the co‐infection of SADS‐CoV and PEDV occurred most frequently with the rate of 17.65%. We screened and obtained two new complete genomes, five N and five S genes of SADS‐CoV. Phylogenetic analysis based on these sequences revealed that all SADS‐CoV sequences in this study clustered with previously reported SADS‐CoV strains to form a well defined branch that grouped with the bat coronavirus HKU2 strains. This study is the first retrospective investigation for SADS‐CoV and provides the epidemiological information of this new virus in China, which highlights the urgency to develop effective measures to control SADS‐CoV.

A Not-So-Long Introduction to Computational Molecular Evolution

In this chapter, we give a not-so-long and self-contained introduction to computational molecular evolution. In particular, we present the emergence of the use of likelihood-based methods, review the standard DNA substitution models, and introduce how model choice operates. We also present recent developments in inferring absolute divergence times and rates on a phylogeny, before showing how state-of-the-art models take inspiration from diffusion theory to link population genetics, which traditionally focuses at a taxonomic level below that of the species, and molecular evolution. Although this is not a cookbook chapter, we try and point to popular programs and implementations along the way.

Full-genome sequencing of porcine circovirus 3 field strains from Denmark, Italy and Spain demonstrates a high within-Europe genetic heterogeneity

Porcine circovirus 3 (PCV3) is a new species of the Circovirus genus, which has recently been associated with different clinical syndromes. Its presence has been reported in different countries of North and South America, Asia and recently also Europe (Poland). However, different from the other continents, no European PCV3 sequence is currently available in public databases. There is a strong need of epidemiological data and full-genome sequences from Europe because of its relevance in the understanding of PCV3 molecular epidemiology and control. To fill this lack of information, samples collected in Denmark, Italy and Spain in 2016 and 2017 were screened for PCV3. Of the Danish samples, 36 of 38 the lymph nodes, six of 20 serum samples and two of 20 lung samples tested positive. Similarly, 10 of 29 lungs, 20 of 29 organ pools, six of 33 sera and one of eight nasal swabs tested PCV3 positive in Italy. Fourteen of 94 serum pools from seven of 14 Spanish farms were also positive. Despite the convenience nature of the sampling prevents any precise prevalence estimation, the preliminary screening of the data from three European countries confirmed a rather wide PCV3 distribution in Europe. Furthermore, the analysis of the six obtained complete European PCV3 genomes and their comparison with the public available sequences seems to support a remarkable worldwide PCV3 circulation. These results underline once more the urgency of more extensive epidemiological studies to refine the current knowledge on PCV3 evolution, transmission, spreading patterns and impact on pig health.

Phylodynamic applications in 21st century global infectious disease research

BACKGROUND

Phylodynamics, the study of the interaction between epidemiological and pathogen evolutionary processes within and among populations, was originally defined in the context of rapidly evolving viruses and used to characterize transmission dynamics. The concept of phylodynamics has evolved since the early 21st century, extending its reach to slower-evolving pathogens, including bacteria and fungi, and to the identification of influential factors in disease spread and pathogen population dynamics.

RESULTS

The phylodynamic approach has now become a fundamental building block for the development of comparative phylogenetic tools capable of incorporating epidemiological surveillance data with molecular sequences into a single statistical framework. These innovative tools have greatly enhanced scientific investigations of the temporal and geographical origins, evolutionary history, and ecological risk factors associated with the growth and spread of viruses such as human immunodeficiency virus (HIV), Zika, and dengue and bacteria such as Methicillin-resistant Staphylococcus aureus.

CONCLUSIONS

Capitalizing on an extensive review of the literature, we discuss the evolution of the field of infectious disease epidemiology and recent accomplishments, highlighting the advancements in phylodynamics, as well as the challenges and limitations currently facing researchers studying emerging pathogen epidemics across the globe.

Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study

Inferring epidemiological parameters such as the R₀ from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) techniques. To compare our approach to existing ones, we simulated target trees under a variety of epidemiological models and settings, and inferred parameters of interest using the same priors. We found that, for large phylogenies, the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2. Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model. It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types. Lastly, by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone, we showed that regression-ABC provides more realistic estimates for the duration parameters (latency and infectiousness) than the likelihood-based method. Overall, ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies.

Given the rapid evolution of many pathogens, analysing their genomes by means of phylogenies can inform us about how they spread. This is the focus of the field known as “phylodynamics”. Most existing methods inferring epidemiological parameters from virus phylogenies are limited by the difficulty of handling complex likelihood functions, which commonly incorporate latent variables. Here, we use an alternative method known as regression-based Approximate Bayesian Computation (ABC), which circumvents this problem by using simulations and dataset comparisons. Since phylogenies are difficult to compare to one another, we introduce many summary statistics to describe them and take advantage of current machine learning techniques able to perform variable selection. We show that the accuracy we reach is comparable to that of existing methods. This accuracy increases with phylogeny size and can even be higher than that of existing methods for some parameters. Overall, regression-based ABC opens new perspectives to infer epidemiological parameters from large phylogenies.

A systematic review of early modelling studies of Ebola virus disease in West Africa

Phenomenological and mechanistic models are widely used to assist resource planning for pandemics and emerging infections. We conducted a systematic review, to compare methods and outputs of published phenomenological and mechanistic modelling studies pertaining to the 2013-2016 Ebola virus disease (EVD) epidemics in four West African countries - Sierra Leone, Liberia, Guinea and Nigeria. We searched Pubmed, Embase and Scopus databases for relevant English language publications up to December 2015. Of the 874 articles identified, 41 met our inclusion criteria. We evaluated these selected studies based on: the sources of the case data used, and modelling approaches, compartments used, population mixing assumptions, model fitting and calibration approaches, sensitivity analysis used and data bias considerations. We synthesised results of the estimated epidemiological parameters: basic reproductive number (R 0), serial interval, latent period, infectious period and case fatality rate, and examined their relationships. The median of the estimated mean R 0 values were between 1·30 and 1·84 in Sierra Leone, Liberia and Guinea. Much higher R 0 value of 9·01 was described for Nigeria. We investigated several issues with uncertainty around EVD modes of transmission, and unknown observation biases from early reported case data. We found that epidemic models offered R 0 mean estimates which are country-specific, but these estimates are not associating with the use of several key disease parameters within the plausible ranges. We find simple models generally yielded similar estimates of R 0 compared with more complex models. Models that accounted for data uncertainty issues have offered a higher case forecast compared with actual case observation. Simple model which offers transparency to public health policy makers could play a critical role for advising rapid policy decisions under an epidemic emergency.

Integrating molecular epidemiology and social network analysis to study infectious diseases: Towards a socio-molecular era for public health

The number of public health applications for molecular epidemiology and social network analysis has increased rapidly since the improvement in computational capacities and the development of new sequencing techniques. Currently, molecular epidemiology methods are used in a variety of settings: from infectious disease surveillance systems to the description of disease transmission pathways. The latter are of great epidemiological importance as they let us describe how a virus spreads in a community, make predictions for the further epidemic developments, and plan preventive interventions. Social network methods are used to understand how infections spread through communities and what the risk factors for this are, as well as in improved contact tracing and message-dissemination interventions. Research is needed on how to combine molecular and social network data as both include essential, but not fully sufficient information on infection transmission pathways. The main differences between the two data sources are that, firstly, social network data include uninfected individuals unlike the molecular data sampled only from infected network members. Thus, social network data include more detailed picture of a network and can improve inferences made from molecular data. Secondly, network data refer to the current state and interactions within the social network, while molecular data refer to the time points when transmissions happened, which might have happened years before the sampling date. As of today, there have been attempts to combine and compare the data obtained from the two sources. Even though there is no consensus on whether and how social and genetic data complement each other, this research might significantly improve our understanding of how viruses spread through communities.

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We summarise and analyse the roles of molecular evolution studies in molecular epidemiology of infectious diseases.

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We review how social network and molecular sequence data have been integrated in the past.

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We show how integrating social network and molecular evolution approaches may change the study of infectious diseases.

How well can the exponential-growth coalescent approximate constant-rate birth-death population dynamics?

One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth–death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R₀ close to 1, but substantial differences remain for large R₀. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.

Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data

Background

Phylogenetic analysis is now an important tool in the study of viral outbreaks. It can reconstruct epidemic history when surveillance epidemiology data are sparse, and can indicate transmission linkages among infections that may not otherwise be evident. However, a remaining challenge is to develop an analytical framework that can test hypotheses about the effect of environmental variables on pathogen spatial spread. Recent phylogeographic approaches can reconstruct the history of virus dispersal from sampled viral genomes and infer the locations of ancestral infections. Such methods provide a unique source of spatio-temporal information, and are exploited here.

Results

We present and apply a new statistical framework that combines genomic and geographic data to test the impact of environmental variables on the mode and tempo of pathogen dispersal during emerging epidemics. First, the spatial history of an emerging pathogen is estimated using standard phylogeographic methods. The inferred dispersal path for each phylogenetic lineage is then assigned a “weight” using environmental data (e.g. altitude, land cover). Next, tests measure the association between each environmental variable and lineage movement. A randomisation procedure is used to assess statistical confidence and we validate this approach using simulated data. We apply our new framework to a set of gene sequences from an epidemic of rabies virus in North American raccoons. We test the impact of six different environmental variables on this epidemic and demonstrate that elevation is associated with a slower rabies spread in a natural population.

Conclusion

This study shows that it is possible to integrate genomic and environmental data in order to test hypotheses concerning the mode and tempo of virus dispersal during emerging epidemics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0924-x) contains supplementary material, which is available to authorized users.

Substitution Model Adequacy and Assessing the Reliability of Estimates of Virus Evolutionary Rates and Time Scales

Determining the time scale of virus evolution is central to understanding their origins and emergence. The phylogenetic methods commonly used for this purpose can be misleading if the substitution model makes incorrect assumptions about the data. Empirical studies consider a pool of models and select that with the highest statistical fit. However, this does not allow the rejection of all models, even if they poorly describe the data. An alternative is to use model adequacy methods that evaluate the ability of a model to predict hypothetical future observations. This can be done by comparing the empirical data with data generated under the model in question. We conducted simulations to evaluate the sensitivity of such methods with nucleotide, amino acid, and codon data. These effectively detected underparameterized models, but failed to detect mutational saturation and some instances of nonstationary base composition, which can lead to biases in estimates of tree topology and length. To test the applicability of these methods with real data, we analyzed nucleotide and amino acid data sets from the genus Flavivirus of RNA viruses. In most cases these models were inadequate, with the exception of a data set of relatively closely related sequences of Dengue virus, for which the GTR+Γ nucleotide and LG+Γ amino acid substitution models were adequate. Our results partly explain the lack of consensus over estimates of the long-term evolutionary time scale of these viruses, and indicate that assessing the adequacy of substitution models should be routinely used to determine whether estimates are reliable.

Elucidating the phylodynamics of endemic rabies virus in eastern Africa using whole-genome sequencing

Many of the pathogens perceived to pose the greatest risk to humans are viral zoonoses, responsible for a range of emerging and endemic infectious diseases. Phylogeography is a useful tool to understand the processes that give rise to spatial patterns and drive dynamics in virus populations. Increasingly, whole-genome information is being used to uncover these patterns, but the limits of phylogenetic resolution that can be achieved with this are unclear. Here, whole-genome variation was used to uncover fine-scale population structure in endemic canine rabies virus circulating in Tanzania. This is the first whole-genome population study of rabies virus and the first comprehensive phylogenetic analysis of rabies virus in East Africa, providing important insights into rabies transmission in an endemic system. In addition, sub-continental scale patterns of population structure were identified using partial gene data and used to determine population structure at larger spatial scales in Africa. While rabies virus has a defined spatial structure at large scales, increasingly frequent levels of admixture were observed at regional and local levels. Discrete phylogeographic analysis revealed long-distance dispersal within Tanzania, which could be attributed to human-mediated movement, and we found evidence of multiple persistent, co-circulating lineages at a very local scale in a single district, despite on-going mass dog vaccination campaigns. This may reflect the wider endemic circulation of these lineages over several decades alongside increased admixture due to human-mediated introductions. These data indicate that successful rabies control in Tanzania could be established at a national level, since most dispersal appears to be restricted within the confines of country borders but some coordination with neighbouring countries may be required to limit transboundary movements. Evidence of complex patterns of rabies circulation within Tanzania necessitates the use of whole-genome sequencing to delineate finer scale population structure that can that can guide interventions, such as the spatial scale and design of dog vaccination campaigns and dog movement controls to achieve and maintain freedom from disease.

New Routes to Phylogeography: A Bayesian Structured Coalescent Approximation

Phylogeographic methods aim to infer migration trends and the history of sampled lineages from genetic data. Applications of phylogeography are broad, and in the context of pathogens include the reconstruction of transmission histories and the origin and emergence of outbreaks. Phylogeographic inference based on bottom-up population genetics models is computationally expensive, and as a result faster alternatives based on the evolution of discrete traits have become popular. In this paper, we show that inference of migration rates and root locations based on discrete trait models is extremely unreliable and sensitive to biased sampling. To address this problem, we introduce BASTA (BAyesian STructured coalescent Approximation), a new approach implemented in BEAST2 that combines the accuracy of methods based on the structured coalescent with the computational efficiency required to handle more than just few populations. We illustrate the potentially severe implications of poor model choice for phylogeographic analyses by investigating the zoonotic transmission of Ebola virus. Whereas the structured coalescent analysis correctly infers that successive human Ebola outbreaks have been seeded by a large unsampled non-human reservoir population, the discrete trait analysis implausibly concludes that undetected human-to-human transmission has allowed the virus to persist over the past four decades. As genomics takes on an increasingly prominent role informing the control and prevention of infectious diseases, it will be vital that phylogeographic inference provides robust insights into transmission history.

When studying infectious diseases it is often important to understand how germs spread from location-to-location, person-to-person, or even one part of the body to another. Using phylogeographic methods, it is possible to recover the history of spread of pathogens (or other organisms) by studying their genetic material. Here we reveal that some popular, fast phylogeographic methods are inaccurate, and we introduce a new more reliable method to address the problem. By comparing different phylogeographic methods based on principled population models and fast alternatives, we found that different approaches can give diametrically opposed results, and we offer concrete examples in the context of the ongoing Ebola outbreak in West Africa and the world-wide outbreaks of Avian Influenza Virus and Tomato Yellow Leaf Curl Virus. We found that the most popular phylogeographic method often produces completely inaccurate conclusions. One of the reasons for its popularity has been its computational speed, which has allowed users to analyse large genetic datasets with complex models. More accurate approaches have until now been considerably slower, and therefore we propose a new method called BASTA that achieves good accuracy in a reasonable time. We are relying more and more on genetic sequencing to learn about the origin and spread of infections, and as this role continues to grow, it will be essential to use accurate phylogeographic methods when designing policies to prevent or curb the spread of disease.

Phylogenetic and phylogeographic mapping of the avian coronavirus spike protein-encoding gene in wild and synanthropic birds

The evolution and population dynamics of avian coronaviruses (AvCoVs) remain underexplored. In the present study, in-depth phylogenetic and Bayesian phylogeographic studies were conducted to investigate the evolutionary dynamics of AvCoVs detected in wild and synanthropic birds. A total of 500 samples, including tracheal and cloacal swabs collected from 312 wild birds belonging to 42 species, were analysed using molecular assays. A total of 65 samples (13%) from 22 bird species were positive for AvCoV. Molecular evolution analyses revealed that the sequences from samples collected in Brazil did not cluster with any of the AvCoV S1 gene sequences deposited in the GenBank database. Bayesian framework analysis estimated an AvCoV strain from Sweden (1999) as the most recent common ancestor of the AvCoVs detected in this study. Furthermore, the analysis inferred an increase in the AvCoV dynamic demographic population in different wild and synanthropic bird species, suggesting that birds may be potential new hosts responsible for spreading this virus.

The Performance of the Date-Randomization Test in Phylogenetic Analyses of Time-Structured Virus Data

Rates and timescales of viral evolution can be estimated using phylogenetic analyses of time-structured molecular sequences. This involves the use of molecular-clock methods, calibrated by the sampling times of the viral sequences. However, the spread of these sampling times is not always sufficient to allow the substitution rate to be estimated accurately. We conducted Bayesian phylogenetic analyses of simulated virus data to evaluate the performance of the date-randomization test, which is sometimes used to investigate whether time-structured data sets have temporal signal. An estimate of the substitution rate passes this test if its mean does not fall within the 95% credible intervals of rate estimates obtained using replicate data sets in which the sampling times have been randomized. We find that the test sometimes fails to detect rate estimates from data with no temporal signal. This error can be minimized by using a more conservative criterion, whereby the 95% credible interval of the estimate with correct sampling times should not overlap with those obtained with randomized sampling times. We also investigated the behavior of the test when the sampling times are not uniformly distributed throughout the tree, which sometimes occurs in empirical data sets. The test performs poorly in these circumstances, such that a modification to the randomization scheme is needed. Finally, we illustrate the behavior of the test in analyses of nucleotide sequences of cereal yellow dwarf virus. Our results validate the use of the date-randomization test and allow us to propose guidelines for interpretation of its results.

Eight challenges in phylodynamic inference

The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.

Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models

Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2-13% vs. 31-75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.

Efficient Bayesian inference under the structured coalescent

Motivation: Population structure significantly affects evolutionary dynamics. Such structure may be due to spatial segregation, but may also reflect any other gene-flow-limiting aspect of a model. In combination with the structured coalescent, this fact can be used to inform phylogenetic tree reconstruction, as well as to infer parameters such as migration rates and subpopulation sizes from annotated sequence data. However, conducting Bayesian inference under the structured coalescent is impeded by the difficulty of constructing Markov Chain Monte Carlo (MCMC) sampling algorithms (samplers) capable of efficiently exploring the state space.

Results: In this article, we present a new MCMC sampler capable of sampling from posterior distributions over structured trees: timed phylogenetic trees in which lineages are associated with the distinct subpopulation in which they lie. The sampler includes a set of MCMC proposal functions that offer significant mixing improvements over a previously published method. Furthermore, its implementation as a BEAST 2 package ensures maximum flexibility with respect to model and prior specification. We demonstrate the usefulness of this new sampler by using it to infer migration rates and effective population sizes of H3N2 influenza between New Zealand, New York and Hong Kong from publicly available hemagglutinin (HA) gene sequences under the structured coalescent.

Availability and implementation: The sampler has been implemented as a publicly available BEAST 2 package that is distributed under version 3 of the GNU General Public License at http://compevol.github.io/MultiTypeTree.

Contact:tgvaughan@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Simultaneous reconstruction of evolutionary history and epidemiological dynamics from viral sequences with the birth-death SIR model

The evolution of RNA viruses, such as human immunodeficiency virus (HIV), hepatitis C virus and influenza virus, occurs so rapidly that the viruses' genomes contain information on past ecological dynamics. Hence, we develop a phylodynamic method that enables the joint estimation of epidemiological parameters and phylogenetic history. Based on a compartmental susceptible–infected–removed (SIR) model, this method provides separate information on incidence and prevalence of infections. Detailed information on the interaction of host population dynamics and evolutionary history can inform decisions on how to contain or entirely avoid disease outbreaks. We apply our birth–death SIR method to two viral datasets. First, five HIV type 1 clusters sampled in the UK between 1999 and 2003 are analysed. The estimated basic reproduction ratios range from 1.9 to 3.2 among the clusters. All clusters show a decline in the growth rate of the local epidemic in the middle or end of the 1990s. The analysis of a hepatitis C virus genotype 2c dataset shows that the local epidemic in the Córdoban city Cruz del Eje originated around 1906 (median), coinciding with an immigration wave from Europe to central Argentina that dates from 1880 to 1920. The estimated time of epidemic peak is around 1970.

Intra-host evolution of multiple genotypes of hepatitis C virus in a chronically infected patient with HIV along a 13-year follow-up period

The intra-host evolutionary process of hepatitis C virus (HCV) was analyzed by phylogenetic and coalescent methodologies in a patient co-infected with HCV-1a, HCV-2a, HCV-3a and human immunodeficiency virus (HIV) along a 13-year period. Direct sequence analysis of the E2 and NS5A regions showed diverse evolutionary dynamics, in agreement with different relationships between these regions and the host factors. The Bayesian Skyline Plot analyses of the E2 sequences (cloned) yielded different intra-host evolutionary patterns for each genotype: a steady state of a "consensus" sequence for HCV-1a; a pattern of lineage splitting and extinction for HCV-2a; and a two-phase (drift/diversification) process for HCV-3a. Each genotype evolving in the same patient and at the same time presents a different pattern apparently modulated by the immune pressure of the host. This study provides useful information for the management of co-infected patients and provides insights into the mechanisms behind the intra-host evolution of HCV.

Spatiotemporal dynamics and epistatic interaction sites in dengue virus type 1: a comprehensive sequence-based analysis

The continuing threat of dengue fever necessitates a comprehensive characterisation of its epidemiological trends. Phylogenetic and recombination events were reconstructed based on 100 worldwide dengue virus (DENV) type 1 genome sequences with an outgroup (prototypes of DENV2-4). The phylodynamic characteristics and site-specific variation were then analysed using data without the outgroup. Five genotypes (GI-GV) and a ladder-like structure with short terminal branch topology were observed in this study. Apparently, the transmission of DENV1 was geographically random before gradual localising with human activity as GI-GIII in South Asia, GIV in the South Pacific, and GV in the Americas. Genotypes IV and V have recently shown higher population densities compared to older genotypes. All codon regions and all tree branches were skewed toward a negative selection, which indicated that their variation was restricted by protein function. Notably, multi-epistatic interaction sites were found in both PrM 221 and NS3 1730. Recombination events accumulated in regions E, NS3-NS4A, and particularly in region NS5. The estimated coevolution pattern also highlights the need for further study of the biological role of protein PrM 221 and NS3 1730. The recent transmission of emergent GV sublineages into Central America and Europe mandates closely monitoring of genotype interaction and succession.

Viral phylodynamics

Viral phylodynamics is defined as the study of how epidemiological, immunological, and evolutionary processes act and potentially interact to shape viralphylogenies. Since the coining of the term in 2004, research on viral phylodynamics has focused on transmission dynamics in an effort to shed light on how these dynamics impact viral genetic variation. Transmission dynamics can be considered at the level of cells within an infected host, individual hosts within a population, or entire populations of hosts. Many viruses, especially RNA viruses, rapidly accumulate genetic variation because of short generation times and high mutation rates. Patterns of viral genetic variation are therefore heavily influenced by how quickly transmission occurs and by which entities transmit to one another. Patterns of viral genetic variation will also be affected by selection acting on viral phenotypes. Although viruses can differ with respect to many phenotypes, phylodynamic studies have to date tended to focus on a limited number of viral phenotypes. These include virulence phenotypes, phenotypes associated with viral transmissibility, cell or tissue tropism phenotypes, and antigenic phenotypes that can facilitate escape from host immunity. Due to the impact that transmission dynamics and selection can have on viral genetic variation, viral phylogenies can therefore be used to investigate important epidemiological, immunological, and evolutionary processes, such as epidemic spread[2], spatio-temporal dynamics including metapopulation dynamics[3], zoonotic transmission, tissue tropism[4], and antigenic drift[5]. The quantitative investigation of these processes through the consideration of viral phylogenies is the central aim of viral phylodynamics.

A stochastic simulator of birth-death master equations with application to phylodynamics

In this article, we present a versatile new software tool for the simulation and analysis of stochastic models of population phylodynamics and chemical kinetics. Models are specified via an expressive and human-readable XML format and can be used as the basis for generating either single population histories or large ensembles of such histories. Importantly, phylogenetic trees or networks can be generated alongside the histories they correspond to, enabling investigations into the interplay between genealogies and population dynamics. Summary statistics such as means and variances can be recorded in place of the full ensemble, allowing for a reduction in the amount of memory used--an important consideration for models including large numbers of individual subpopulations or demes. In the case of population size histories, the resulting simulation output is written to disk in the flexible JSON format, which is easily read into numerical analysis environments such as R for visualization or further processing. Simulated phylogenetic trees can be recorded using the standard Newick or NEXUS formats, with extensions to these formats used for non-tree-like inheritance relationships.

Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV)

Phylogenetic trees can be used to infer the processes that generated them. Here, we introduce a model, the bayesian birth-death skyline plot, which explicitly estimates the rate of transmission, recovery, and sampling and thus allows inference of the effective reproductive number directly from genetic data. Our method allows these parameters to vary through time in a piecewise fashion and is implemented within the BEAST2 software framework. The method is a powerful alternative to the existing coalescent skyline plot, providing insight into the differing roles of incidence and prevalence in an epidemic. We apply this method to data from the United Kingdom HIV-1 epidemic and Egyptian hepatitis C virus (HCV) epidemic. The analysis reveals temporal changes of the effective reproductive number that highlight the effect of past public health interventions.

Phylodynamic analysis of a viral infection network

Viral infections by sexual and droplet transmission routes typically spread through a complex host-to-host contact network. Clarifying the transmission network and epidemiological parameters affecting the variations and dynamics of a specific pathogen is a major issue in the control of infectious diseases. However, conventional methods such as interview and/or classical phylogenetic analysis of viral gene sequences have inherent limitations and often fail to detect infectious clusters and transmission connections. Recent improvements in computational environments now permit the analysis of large datasets. In addition, novel analytical methods have been developed that serve to infer the evolutionary dynamics of virus genetic diversity using sample date information and sequence data. This type of framework, termed "phylodynamics," helps connect some of the missing links on viral transmission networks, which are often hard to detect by conventional methods of epidemiology. With sufficient number of sequences available, one can use this new inference method to estimate theoretical epidemiological parameters such as temporal distributions of the primary infection, fluctuation of the pathogen population size, basic reproductive number, and the mean time span of disease infectiousness. Transmission networks estimated by this framework often have the properties of a scale-free network, which are characteristic of infectious and social communication processes. Network analysis based on phylodynamics has alluded to various suggestions concerning the infection dynamics associated with a given community and/or risk behavior. In this review, I will summarize the current methods available for identifying the transmission network using phylogeny, and present an argument on the possibilities of applying the scale-free properties to these existing frameworks.