The epidemic behavior of the hepatitis C virus

'Unusual' HCV genotype subtypes: origin, distribution, sensitivity to direct-acting antiviral drugs and behaviour on antiviral treatment and retreatment

The high genetic diversity of hepatitis C virus (HCV) has led to the emergence of eight genotypes and a large number of subtypes in limited geographical areas. Currently approved pangenotypic DAA regimens have been designed and developed to be effective against the most common subtypes (1a, 1b, 2a, 2b, 2c, 3a, 4a, 5a and 6a). However, large populations living in Africa and Asia, or who have migrated from these regions to industrialised countries, are infected with 'unusual', non-epidemic HCV subtypes, including some that are inherently resistant to currently available direct-acting antiviral (DAA) drugs due to the presence of natural polymorphisms at resistance-associated substitution positions. In this review article, we describe the origin and subsequent global spread of HCV genotypes and subtypes, the current global distribution of common and unusual HCV subtypes, the polymorphisms naturally present in the genome sequences of unusual HCV subtypes that may confer inherently reduced susceptibility to DAA drugs and the available data on the response of unusual HCV subtypes to first-line HCV therapy and retreatment. We conclude that the problem of unusual HCV subtypes that are inherently resistant to DAAs and its threat to the global efforts to eliminate viral hepatitis are largely underestimated and warrant vigorous action.

Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences

Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction number R and the dispersion parameter k have however mainly remained limited to the analysis of epidemiological clusters or contact tracing data, whose collection often proves difficult. Here, we show that clusters of identical sequences are imprinted by the pathogen offspring distribution, and we derive an analytical formula for the distribution of the size of these clusters. We develop and evaluate an inference framework to jointly estimate the reproduction number and the dispersion parameter from the size distribution of clusters of identical sequences. We then illustrate its application across a range of epidemiological situations. Finally, we develop a hypothesis testing framework relying on clusters of identical sequences to determine whether a given pathogen genetic subpopulation is associated with increased or reduced transmissibility. Our work provides tools to estimate the reproduction number and transmission heterogeneity from pathogen sequences without building a phylogenetic tree, thus making it easily scalable to large pathogen genome datasets.

Temporal trends and transmission dynamics of pre-treatment HIV-1 drug resistance within and between risk groups in Kenya, 1986-2020

BACKGROUND

Evidence on the distribution of pre-treatment HIV-1 drug resistance (HIVDR) among risk groups is limited in Africa. We assessed the prevalence, trends and transmission dynamics of pre-treatment HIVDR within and between MSM, people who inject drugs (PWID), female sex workers (FSWs), heterosexuals (HETs) and perinatally infected children in Kenya.

METHODS

HIV-1 partial pol sequences from antiretroviral-naive individuals collected from multiple sources between 1986 and 2020 were used. Pre-treatment reverse transcriptase inhibitor (RTI), PI and integrase inhibitor (INSTI) mutations were assessed using the Stanford HIVDR database. Phylogenetic methods were used to determine and date transmission clusters.

RESULTS

Of 3567 sequences analysed, 550 (15.4%, 95% CI: 14.2-16.6) had at least one pre-treatment HIVDR mutation, which was most prevalent amongst children (41.3%), followed by PWID (31.0%), MSM (19.9%), FSWs (15.1%) and HETs (13.9%). Overall, pre-treatment HIVDR increased consistently, from 6.9% (before 2005) to 24.2% (2016-20). Among HETs, pre-treatment HIVDR increased from 6.6% (before 2005) to 20.2% (2011-15), but dropped to 6.5% (2016-20). Additionally, 32 clusters with shared pre-treatment HIVDR mutations were identified. The majority of clusters had R0 ≥ 1.0, indicating ongoing transmissions. The largest was a K103N cluster involving 16 MSM sequences sampled between 2010 and 2017, with an estimated time to the most recent common ancestor (tMRCA) of 2005 [95% higher posterior density (HPD), 2000-08], indicating propagation over 12 years.

CONCLUSIONS

Compared to HETs, children and key populations had higher levels of pre-treatment HIVDR. Introduction of INSTIs after 2017 may have abrogated the increase in pre-treatment RTI mutations, albeit in the HET population only. Taken together, our findings underscore the need for targeted efforts towards equitable access to ART for children and key populations in Kenya.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

Genetic history of hepatitis C virus genotype 6 in Taiwan

Unlike hepatitis C virus (HCV) genotype (GT) 6, which is widely circulated in Southeast Asia and South China, GT 6 was not reported in Taiwan until 2006. GT 1b and 2a, also known as global HCV subtypes, have been reported as major GTs circulating in Taiwan. Because of improvement in genotyping kits and sequencing techniques for the subtyping of HCV, an increasing number of GT 6 subtypes have been reported, especially subtype 6a among intravenous drug users with human immunodeficiency virus infection after an outbreak since 2003. Thus, HCV GT 6 infection is regarded to be closely associated with injection drug use. However, recently, we found an unexpectedly high GT 6 prevalence in the general population in Tainan, southern Taiwan. Most of these GT 6 samples belonged to a putative novel subtype closely related to 6g and 6w instead of 6a. Phylogenetic analyses indicated that this putative 6g-related novel subtype and 6w could be indigenous in southern Taiwan for centuries. Southern Taiwan could be the origin of HCV subtype 6w. This finding might change the perspective of HCV epidemiology in Taiwan.

Wastewater surveillance beyond COVID-19: a ranking system for communicable disease testing in the tri-county Detroit area, Michigan, USA

Introduction

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been utilized to monitor the disease in the United States through routine national, statewide, and regional monitoring projects. A significant canon of evidence was produced showing that wastewater surveillance is a credible and effective tool for disease monitoring. Hence, the application of wastewater surveillance can extend beyond monitoring SARS-CoV-2 to encompass a diverse range of emerging diseases. This article proposed a ranking system for prioritizing reportable communicable diseases (CDs) in the Tri-County Detroit Area (TCDA), Michigan, for future wastewater surveillance applications at the Great Lakes Water Authority's Water Reclamation Plant (GLWA's WRP).

Methods

The comprehensive CD wastewater surveillance ranking system (CDWSRank) was developed based on 6 binary and 6 quantitative parameters. The final ranking scores of CDs were computed by summing the multiplication products of weighting factors for each parameter, and then were sorted based on decreasing priority. Disease incidence data from 2014 to 2021 were collected for the TCDA. Disease incidence trends in the TCDA were endowed with higher weights, prioritizing the TCDA over the state of Michigan.

Results

Disparities in incidences of CDs were identified between the TCDA and state of Michigan, indicating epidemiological differences. Among 96 ranked CDs, some top ranked CDs did not present relatively high incidences but were prioritized, suggesting that such CDs require significant attention by wastewater surveillance practitioners, despite their relatively low incidences in the geographic area of interest. Appropriate wastewater sample concentration methods are summarized for the application of wastewater surveillance as per viral, bacterial, parasitic, and fungal pathogens.

Discussion

The CDWSRank system is one of the first of its kind to provide an empirical approach to prioritize CDs for wastewater surveillance, specifically in geographies served by centralized wastewater collection in the area of interest. The CDWSRank system provides a methodological tool and critical information that can help public health officials and policymakers allocate resources. It can be used to prioritize disease surveillance efforts and ensure that public health interventions are targeted at the most potentially urgent threats. The CDWSRank system can be easily adopted to geographical locations beyond the TCDA.

Optimal Frequency of Hepatitis C Virus (HCV) RNA Testing for Detection of Acute HCV Infection Among At-risk People With Human Immunodeficiency Virus: A Multicenter Study

Using 3-stage pooled-plasma hepatitis C virus (HCV) RNA testing performed quarterly among at-risk people with human immunodeficiency virus (PWH), we found that if testing had been performed every 6 or 12 months, 58.6%-91.7% of PWH who recently acquired HCV would be delayed for diagnosis and might contribute to onward HCV transmission with longer durations.

Model design for nonparametric phylodynamic inference and applications to pathogen surveillance

Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R ₀ ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R ₀ values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R ₀ estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R ₀ estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R ₀ estimation and surveillance-based R ₀ estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.

Mathematical modeling of plus-strand RNA virus replication to identify broad-spectrum antiviral treatment strategies

Plus-strand RNA viruses are the largest group of viruses. Many are human pathogens that inflict a socio-economic burden. Interestingly, plus-strand RNA viruses share remarkable similarities in their replication. A hallmark of plus-strand RNA viruses is the remodeling of intracellular membranes to establish replication organelles (so-called "replication factories"), which provide a protected environment for the replicase complex, consisting of the viral genome and proteins necessary for viral RNA synthesis. In the current study, we investigate pan-viral similarities and virus-specific differences in the life cycle of this highly relevant group of viruses. We first measured the kinetics of viral RNA, viral protein, and infectious virus particle production of hepatitis C virus (HCV), dengue virus (DENV), and coxsackievirus B3 (CVB3) in the immuno-compromised Huh7 cell line and thus without perturbations by an intrinsic immune response. Based on these measurements, we developed a detailed mathematical model of the replication of HCV, DENV, and CVB3 and show that only small virus-specific changes in the model were necessary to describe the in vitro dynamics of the different viruses. Our model correctly predicted virus-specific mechanisms such as host cell translation shut off and different kinetics of replication organelles. Further, our model suggests that the ability to suppress or shut down host cell mRNA translation may be a key factor for in vitro replication efficiency which may determine acute self-limited or chronic infection. We further analyzed potential broad-spectrum antiviral treatment options in silico and found that targeting viral RNA translation, especially polyprotein cleavage, and viral RNA synthesis may be the most promising drug targets for all plus-strand RNA viruses. Moreover, we found that targeting only the formation of replicase complexes did not stop the viral replication in vitro early in infection, while inhibiting intracellular trafficking processes may even lead to amplified viral growth.

Author summary

Plus-strand RNA viruses comprise a large group of related and medically relevant viruses. The current global pandemic of COVID-19 caused by the SARS-coronavirus-2 as well as the constant spread of diseases such as dengue and chikungunya fever show the necessity of a comprehensive and precise analysis of plus-strand RNA virus infections. Plus-strand RNA viruses share similarities in their life cycle. To understand their within-host replication strategies, we developed a mathematical model that studies pan-viral similarities and virus-specific differences of three plus-strand RNA viruses, namely hepatitis C, dengue, and coxsackievirus. By fitting our model to in vitro data, we found that only small virus-specific variations in the model were required to describe the dynamics of all three viruses. Furthermore, our model predicted that ribosomes involved in viral RNA translation seem to be a key player in plus-strand RNA replication efficiency, which may determine acute or chronic infection outcome. Furthermore, our in-silico drug treatment analysis suggests that targeting viral proteases involved in polyprotein cleavage, in combination with viral RNA replication, may represent promising drug targets with broad-spectrum antiviral activity.

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.

Molecular Epidemiology and Transmission Dynamics of the HIV-1 Epidemic in Ethiopia: Epidemic Decline Coincided With Behavioral Interventions Before ART Scale-Up

Background

Ethiopia is one of the sub-Saharan countries hit hard by the HIV epidemic. Previous studies have shown that subtype C dominates the Ethiopian HIV-1 epidemic, but the evolutionary and temporal dynamics of HIV-1 in Ethiopia have not been closely scrutinized. Understanding the evolutionary and epidemiological pattern of HIV is vital to monitor the spread, evaluate and implement HIV prevention strategies.

Methods

We analyzed 1,276 Ethiopian HIV-1 subtype C polymerase (pol sequences), including 144 newly generated sequences, collected from different parts of the country from 1986 to 2017. We employed state-of-art maximum likelihood and Bayesian phylodynamic analyses to comprehensively describe the evolutionary dynamics of the HIV-1 epidemic in Ethiopia. We used Bayesian phylodynamic models to estimate the dynamics of the effective population size (Ne) and reproductive numbers (Re) through time for the HIV epidemic in Ethiopia.

Results

Our analysis revealed that the Ethiopian HIV-1 epidemic originated from two independent introductions at the beginning of the 1970s and 1980s from eastern and southern African countries, respectively, followed by epidemic growth reaching its maximum in the early 1990s. We identified three large clusters with a majority of Ethiopian sequences. Phylodynamic analyses revealed that all three clusters were characterized by high transmission rates during the early epidemic, followed by a decline in HIV-1 transmissions after 1990. Re was high (4–6) during the earlier time of the epidemic but dropped significantly and remained low (Re < 1) after the mid-1990. Similarly, with an expected shift in time, the effective population size (Ne) steadily increased until the beginning of 2000, followed by a decline and stabilization until recent years. The phylodynamic analyses corroborated the modeled UNAIDS incidence and prevalence estimates.

Conclusion

The rapid decline in the HIV epidemic took place a decade before introducing antiretroviral therapy in Ethiopia and coincided with early behavioral, preventive, and awareness interventions implemented in the country. Our findings highlight the importance of behavioral interventions and antiretroviral therapy scale-up to halt and maintain HIV transmissions at low levels (Re < 1). The phylodynamic analyses provide epidemiological insights not directly available using standard surveillance and may inform the adjustment of public health strategies in HIV prevention in Ethiopia.

A computationally tractable birth-death model that combines phylogenetic and epidemiological data

Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each type of data provides different, and potentially complementary, insight. Recent studies have recognised that combining data sources can improve estimates of the transmission rate and the number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the unobserved prevalence. Using simulated data, we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace.

Quantifying transmission dynamics of acute hepatitis C virus infections in a heterogeneous population using sequence data

Opioid substitution and syringes exchange programs have drastically reduced hepatitis C virus (HCV) spread in France but HCV sexual transmission in men having sex with men (MSM) has recently arisen as a significant public health concern. The fact that the virus is transmitting in a heterogeneous population, with different transmission routes, makes prevalence and incidence rates poorly informative. However, additional insights can be gained by analyzing virus phylogenies inferred from dated genetic sequence data. By combining a phylodynamics approach based on Approximate Bayesian Computation (ABC) and an original transmission model, we estimate key epidemiological parameters of an ongoing HCV epidemic among MSMs in Lyon (France). We show that this new epidemic is largely independent of the previously observed non-MSM HCV epidemics and that its doubling time is ten times lower (0.44 years versus 4.37 years). These results have practical implications for HCV control and illustrate the additional information provided by virus genomics in public health.

Model design for non-parametric phylodynamic inference and applications to pathogen surveillance

Inference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. The combination of non-parametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments, and apply the methodology to reconstruct the previously described waves in the seventh pandemic of cholera. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Infer HIV transmission dynamics from gene sequences among young men who have sex with men in China

To investigate the transmission dynamics and temporal and spatial migration characteristics of HIV spread among men who have sex with men (MSM) in China, a total of 1012 HIV-1 partial pol sequences, including five subtypes, were studied. Bayesian analysis were applied for each subtype to infer its dynamic characters including the effective reproductive number (R _e ) and migration process. The mean curve of each R _e was almost always greater than 1 (even the 95% highest posterior density (HPD) lower value) along with time, which supports the necessity for a comprehensive study about risk behaviors among young MSM group in China. We also should reappraise the free treatment strategy, especially the therapeutic effect during the free treatment policy.

Coevolutionary transitions emerging from flexible molecular recognition and eco-evolutionary feedback

Highly mutable viruses evolve to evade host immunity that exerts selective pressure and adapts to viral dynamics. Here, we provide a framework for identifying key determinants of the mode and fate of viral-immune coevolution by linking molecular recognition and eco-evolutionary dynamics. We find that conservation level and initial diversity of antigen jointly determine the timing and efficacy of narrow and broad antibody responses, which in turn control the transition between viral persistence, clearance, and rebound. In particular, clearance of structurally complex antigens relies on antibody evolution in a larger antigenic space than where selection directly acts; viral rebound manifests binding-mediated feedback between ecology and rapid evolution. Finally, immune compartmentalization can slow viral escape but also delay clearance. This work suggests that flexible molecular binding allows a plastic phenotype that exploits potentiating neutral variations outside direct contact, opening new and shorter paths toward highly adaptable states.

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A scale-crossing framework identifies key determinants of viral-immune coevolution

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Fast specific response influences slow broad response by shaping antigen dynamics

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Antibody footprint shift enables breadth acquisition and viral clearance

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Model explains divergent kinetics and outcomes of HCV infection in humans

Multiple Merger Genealogies in Outbreaks of Mycobacterium tuberculosis

The Kingman coalescent and its developments are often considered among the most important advances in population genetics of the last decades. Demographic inference based on coalescent theory has been used to reconstruct the population dynamics and evolutionary history of several species, including Mycobacterium tuberculosis (MTB), an important human pathogen causing tuberculosis. One key assumption of the Kingman coalescent is that the number of descendants of different individuals does not vary strongly, and violating this assumption could lead to severe biases caused by model misspecification. Individual lineages of MTB are expected to vary strongly in reproductive success because 1) MTB is potentially under constant selection due to the pressure of the host immune system and of antibiotic treatment, 2) MTB undergoes repeated population bottlenecks when it transmits from one host to the next, and 3) some hosts show much higher transmission rates compared with the average (superspreaders).

Here, we used an approximate Bayesian computation approach to test whether multiple-merger coalescents (MMC), a class of models that allow for large variation in reproductive success among lineages, are more appropriate models to study MTB populations. We considered 11 publicly available whole-genome sequence data sets sampled from local MTB populations and outbreaks and found that MMC had a better fit compared with the Kingman coalescent for 10 of the 11 data sets. These results indicate that the null model for analyzing MTB outbreaks should be reassessed and that past findings based on the Kingman coalescent need to be revisited.

The roles of environmental variation and parasite survival in virulence-transmission relationships

Disease outbreaks are a consequence of interactions among the three components of a host-parasite system: the infectious agent, the host and the environment. While virulence and transmission are widely investigated, most studies of parasite life-history trade-offs are conducted with theoretical models or tractable experimental systems where transmission is standardized and the environment controlled. Yet, biotic and abiotic environmental factors can strongly affect disease dynamics, and ultimately, host-parasite coevolution. Here, we review research on how environmental context alters virulence-transmission relationships, focusing on the off-host portion of the parasite life cycle, and how variation in parasite survival affects the evolution of virulence and transmission. We review three inter-related 'approaches' that have dominated the study of the evolution of virulence and transmission for different host-parasite systems: (i) evolutionary trade-off theory, (ii) parasite local adaptation and (iii) parasite phylodynamics. These approaches consider the role of the environment in virulence and transmission evolution from different angles, which entail different advantages and potential biases. We suggest improvements to how to investigate virulence-transmission relationships, through conceptual and methodological developments and taking environmental context into consideration. By combining developments in life-history evolution, phylogenetics, adaptive dynamics and comparative genomics, we can improve our understanding of virulence-transmission relationships across a diversity of host-parasite systems that have eluded experimental study of parasite life history.

HCV virology and diagnosis

Hepatitis C virus (HCV) infection is a major cause of severe liver disease including chronic hepatitis, cirrhosis and hepatocellular carcinoma. The HCV burden in public health is estimated at about 71 million people worldwide by World Health Organization (WHO) with at least 400,000 people that died every year from HCV disease [1]. New hepatitis C treatments with oral direct-acting antivirals (DAAs) showing high rates of response, with short treatment duration [2] have been available. HCV can now be eradicated with minimal side effects. Unfortunately, there is no vaccine yet available, but the development of a safe prophylactic vaccine remains a medical priority [3]. For this purpose, Hepatitis B-C subviral envelope particles can be produced by industrialized procedure. It seems to be very promising as this HBV-HCV vaccine candidate has been shown to elicit a broadly cross neutralizing activity against HCV [4]. Despite this revolution in the HCV-treatment, one of major challenge to achieve a global eradication of HCV remains to reduce the under diagnosis. The low rate of diagnosis is a major obstacle in resources limited countries and is mainly due to the cost of molecular tools, that are essential to diagnose and follow chronic HCV infection. In another hand, the mild clinical symptoms observed in HCV chronic disease, may explain that the majority of HCV infected individuals are unaware of their infection, because HCV testing is not generalized, like it is for HIV. HCV was discovered in 1989 after many years of work, by several researchers, who recently obtained the Nobel price [5-7]. This major discovery allowed the description of the HCV genome and later on of the virus replication and cell cycle, and also, importantly, the development of diagnostic tests for the detection of HCV antibodies (Ab) and RNA who were a priority in transfusion. In this review, we will try to get into the virology and cell biology of HCV. Thereafter, we will discuss the different categories of laboratory tests to diagnose/explore HCV infected subjects.

Nowcasting epidemics of novel pathogens: lessons from COVID-19

Epidemic nowcasting broadly refers to assessing the current state by understanding key pathogenic, epidemiologic, clinical and socio-behavioral characteristics of an ongoing outbreak. Its primary objective is to provide situational awareness and inform decisions on control responses. In the event of large-scale sustained emergencies, such as the COVID-19 pandemic, scientists need to constantly update their aims and analytics with respect to the rapidly evolving emergence of new questions, data and findings in order to synthesize real-time evidence for policy decisions. In this Perspective, we share our views on the functional aims, rationale, data requirements and challenges of nowcasting at different stages of an epidemic, drawing on the ongoing COVID-19 experience. We highlight how recent advances in the computational and laboratory sciences could be harnessed to complement traditional approaches to enhance the scope, timeliness, reliability and utility of epidemic nowcasting.

Time and Mode of Epidemic HCV-2 Subtypes Spreading in Europe: Phylodynamics in Italy and Albania

Hepatitis C virus (HCV) genotype 2 causes about 10% of global infections and has the most variable circulation profile in Europe. The history of “endemic” HCV-2 subtypes has been satisfactorily reconstructed, instead there is little information about the recent spread of the “epidemic” subtypes, including HCV-2c. To investigate the origin and dispersion pathways of HCV-2c, 245 newly characterized Italian and Albanian HCV-2 NS5B sequences were aligned with 247 publicly available sequences and included in phylogeographic and phylodynamic analyses using the Bayesian framework. Our findings show that HCV-2c was the most prevalent subtype in Italy and Albania. The phylogeographic analysis suggested an African origin of HCV-2c before it reached Italy about in the 1940s. Phylodynamic analysis revealed an exponential increase in the effective number of infections and Re in Italy between the 1940s and 1960s, and in Albania between the 1990s and the early 2000s. It seems very likely that HCV-2c reached Italy from Africa at the time of the second Italian colonization but did not reach Albania until the period of dramatic migration to Italy in the 1990s. This study contributes to reconstructing the history of the spread of epidemic HCV-2 subtypes to Europe.

Transmission dynamics of SARS-CoV-2 on the Diamond Princess uncovered using viral genome sequence analysis

An outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred aboard the Diamond Princess cruise ship between her January 20 departure and late February 2020. Here, we used phylodynamic analyses to investigate the transmission dynamics of SARS-CoV-2 during the outbreak. Using a Bayesian coalescent-based method, the estimated mean nucleotide substitution rate of 240 SARS-CoV-2 whole-genome sequences was approximately 7.13 × 10⁻⁴ substitutions per site per year. Population dynamics and the effective reproductive number (R_e) of SARS-CoV-2 infections were estimated using a Bayesian framework. The estimated origin of the outbreak was January 21, 2020. The infection spread substantially before quarantine on February 5. The R_e peaked at 6.06 on February 4 and gradually declined to 1.51, suggesting that transmission continued slowly even after quarantine. These findings highlight the high transmissibility of SARS-CoV-2 and the need for effective measures to control outbreaks in confined settings.

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.

Characterising the epidemic spread of influenza A/H3N2 within a city through phylogenetics

Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, its local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. While we did not find that transmission dynamics in Basel correlate with humidity or school closures, we did find some evidence that it may positively correlated with temperature. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network. In the remaining transmission network, our analyses suggest that school-aged children likely play a more central role than pre-school aged children. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities.

Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models

Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of observed incident cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimizing p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimizes p so that R and N estimates properly and meaningfully adapt to available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems, given minimal knowledge of the parameter space, and exposes statistical similarities among renewal, skyline, and other models in biology. Rigorous and interpretable model selection is necessary if trustworthy and justifiable conclusions are to be drawn from piecewise models. [Coalescent processes; epidemiology; information theory; model selection; phylodynamics; renewal models; skyline plots].

Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics

The SIR ('susceptible-infectious-recovered') formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number Ro inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled Ro = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of Ro = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of 'super-spreaders' on the inference of Ro are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled Ro are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in Ro > 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.

Key questions for modelling COVID-19 exit strategies

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

High prevalence of Hepatitis C Virus infection among people who use crack cocaine in an important international drug trafficking route in Central-West Region Brazil

In this study, the prevalence rate, associated risk factors and genetic diversity of hepatitis C virus (HCV) infection were determined among people who use crack from an international drug trafficking route in Central-West, Brazil. Blood samples were collected from 700 users of crack from Campo Grande and two border cities of Mato Grosso do Sul State and tested for HCV infection using serological and molecular testing methodologies. Anti-HCV was detected in 31/700 (4.5%, 95% CI: 2.9-6.0%) and HCV RNA in 26/31 (83.9%) of anti-HCV positive samples. Phylogenetic analysis of three HCV sub-genomic regions (5'UTR, NS5B and HVR-1) revealed the circulation of 1a (73.9%), 1b (8.7%) and 3a (17.4%) genotypes. Next-generation sequencing and phylogenetic analysis of intra-host viral populations of HCV HVR-1 showed a significant variation in intra-host genetic diversity among infected individuals, with 58.8% composed of more than one sub-population. Bayesian analysis estimated that the most recent common HCV ancestor for strains identified here was introduced to this region after 1975 following expansion of intravenous drug use in Brazil. Multivariate analyses showed that only 'ever having injected drugs' was independently associated with HCV infection. These results indicate an increasing spread of multiple HCV strains requiring public health intervention, such as harm reduction, testing services and treatment among crack users in this important border region of Central Brazil.

Early phylogenetic estimate of the effective reproduction number of SARS-CoV-2

To reconstruct the evolutionary dynamics of the 2019 novel‐coronavirus recently causing an outbreak in Wuhan, China, 52 SARS‐CoV‐2 genomes available on 4 February 2020 at Global Initiative on Sharing All Influenza Data were analyzed. The two models used to estimate the reproduction number (coalescent‐based exponential growth and a birth‐death skyline method) indicated an estimated mean evolutionary rate of 7.8 × 10⁻⁴ subs/site/year (range, 1.1 × 10⁻⁴‐15 × 10⁻⁴) and a mean tMRCA of the tree root of 73 days. The estimated R value was 2.6 (range, 2.1‐5.1), and increased from 0.8 to 2.4 in December 2019. The estimated mean doubling time of the epidemic was between 3.6 and 4.1 days. This study proves the usefulness of phylogeny in supporting the surveillance of emerging new infections even as the epidemic is growing.

The aim of this study was to investigate the temporal origin, rate of viral evolution and population dynamics of SARS‐CoV‐2.

The Bayesian approach used indicated a most probable origin of the epidemic between October and November 2019.

The estimated effective reproductive number increased from 0.8 to 2.4 in December 2019 when the mean doubling time was about 4 days.

This study proves the usefulness of phylogeny in supporting the surveillance of emerging new infections.

Estimating Epidemic Incidence and Prevalence from Genomic Data

Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth–death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth–death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics

Recent advances in statistical machine learning techniques have led to the creation of probabilistic programming frameworks. These frameworks enable probabilistic models to be rapidly prototyped and fit to data using scalable approximation methods such as variational inference. In this work, we explore the use of the Stan language for probabilistic programming in application to phylogenetic models. We show that many commonly used phylogenetic models including the general time reversible substitution model, rate heterogeneity among sites, and a range of coalescent models can be implemented using a probabilistic programming language. The posterior probability distributions obtained via the black box variational inference engine in Stan were compared to those obtained with reference implementations of Markov chain Monte Carlo (MCMC) for phylogenetic inference. We find that black box variational inference in Stan is less accurate than MCMC methods for phylogenetic models, but requires far less compute time. Finally, we evaluate a custom implementation of mean-field variational inference on the Jukes-Cantor substitution model and show that a specialized implementation of variational inference can be two orders of magnitude faster and more accurate than a general purpose probabilistic implementation.

Estimating Transmission Potential of H5N1 Viruses Among Humans in Egypt Using Phylogeny, Genetic Distance and Sampling Time Interval

In 2014 and 2015, the number of human cases of H5N1 avian influenza virus infections had increased dramatically in Egypt. This increase might be related to increase in the transmission potential of the virus among humans. To clarify the cause of the increase in H5N1 human cases, we investigate the transmissibility of H5N1 viruses among humans via estimating the basic reproduction number R₀ using nucleotide sequences and sampling dates of viruses. To this end, full-length hemagglutinin gene sequences of human and avian H5N1 influenza viruses isolated from 2006 to 2016 in Egypt were obtained from the NCBI influenza virus resource. Taking into account the phylogeny, genetic distance, sampling time difference among viruses, R₀ was estimated to be 0.05 (95% CI: 0.01, 0.13) assuming that human-to-human transmissions occurred within a city, 0.23(95% CI: 0.14, 0.35) assuming human-to-human transmissions among cities. Our results indicate that human-to-human transmission of H5N1 viruses in Egypt is limited, and the large increase in human cases is likely attributed to other factor than increase in human-to-human transmission potential.

The First Complete Genome Sequences of Hepatitis C Virus Subtype 2b from Latin America: Molecular Characterization and Phylogeographic Analysis

The hepatitis C virus (HCV) has remarkable genetic diversity and exists as eight genotypes (1 to 8) with distinct geographic distributions. No complete genome sequence of HCV subtype 2b (HCV-2b) is available from Latin American countries, and the factors underlying its emergence and spread within the continent remain unknown. The present study was conducted to determine the first full-length genomic sequences of HCV-2b isolates from Latin America and reconstruct the spatial and temporal diversification of this subtype in Brazil. Nearly complete HCV-2b genomes isolated from two Brazilian patients were obtained by direct sequencing of long PCR fragments and analyzed together with reference sequences using the Bayesian coalescent and phylogeographic framework approaches. The two HCV-2b genomes were 9318 nucleotides (nt) in length (nt 37-9354). Interestingly, the long RT-PCR technique was able to detect co-circulation of viral variants that contained an in-frame deletion of 2022 nt encompassing E1, E2, and p7 proteins. Spatiotemporal reconstruction analyses suggest that HCV-2b had a single introduction in Brazil during the early 1980s, displaying an epidemic history characterized by a low and virtually constant population size until the present time. These results coincide with epidemiological data in Brazil and may explain the low national prevalence of this subtype.

Mode and tempo of human hepatitis virus evolution

Human viral hepatitis, a major cause of morbidity and mortality worldwide, is caused by highly diverse viruses with different genetic, ecological, and pathogenetic features. Technological advances that allow throughput sequencing of viral genomes, as well as the development of computational tools to analyze such genome data, have largely expanded our knowledge on the host range and evolutionary history of human hepatitis viruses. Thus, with the exclusion of hepatitis D virus, close or distant relatives of these human pathogens were identified in a number of domestic and wild mammals. Also, sequences of human viral strains isolated from different geographic locations and over different time-spans have allowed the application of phylogeographic and molecular dating approaches to large viral phylogenies. In this review, we summarize the most recent insights into our understanding of the evolutionary events and ecological contexts that determined the origin and spread of human hepatitis viruses.

MASCOT: parameter and state inference under the marginal structured coalescent approximation

Motivation

The structured coalescent is widely applied to study demography within and migration between sub-populations from genetic sequence data. Current methods are either exact but too computationally inefficient to analyse large datasets with many sub-populations, or make strong approximations leading to severe biases in inference. We recently introduced an approximation based on weaker assumptions to the structured coalescent enabling the analysis of larger datasets with many different states. We showed that our approximation provides unbiased migration rate and population size estimates across a wide parameter range.

Results

We extend this approach by providing a new algorithm to calculate the probability of the state of internal nodes that includes the information from the full phylogenetic tree. We show that this algorithm is able to increase the probability attributed to the true sub-population of a node. Furthermore we use improved integration techniques, such that our method is now able to analyse larger datasets, including a H3N2 dataset with 433 sequences sampled from five different locations.

Availability and implementation

The presented methods are part of the BEAST2 package MASCOT, the Marginal Approximation of the Structured COalescenT. This package can be downloaded via the BEAUti package manager. The source code is available at https://github.com/nicfel/Mascot.git.

Supplementary information

Supplementary data are available at Bioinformatics online.

Hepatitis C virus genotype 1 and 2 recombinant genomes and the phylogeographic history of the 2k/1b lineage

Recombination is an important driver of genetic diversity, though it is relatively uncommon in hepatitis C virus (HCV). Recent investigation of sequence data acquired from HCV clinical trials produced twenty-one full-genome recombinant viruses belonging to three putative inter-subtype forms 2b/1a, 2b/1b, and 2k/1b. The 2k/1b chimera is the only known HCV circulating recombinant form (CRF), provoking interest in its genetic structure and origin. Discovered in Russia in 1999, 2k/1b cases have since been detected throughout the former Soviet Union, Western Europe, and North America. Although 2k/1b prevalence is highest in the Caucasus mountain region (i.e., Armenia, Azerbaijan, and Georgia), the origin and migration patterns of CRF 2k/1b have remained obscure due to a paucity of available sequences. We assembled an alignment which spans the entire coding region of the HCV genome containing all available 2k/1b sequences (>500 nucleotides; n = 109) sampled in ninteen countries from public databases (102 individuals), additional newly sequenced genomic regions (from 48 of these 102 individuals), unpublished isolates with newly sequenced regions (5 additional individuals), and novel complete genomes (2 additional individuals) generated in this study. Analysis of this expanded dataset reconfirmed the monophyletic origin of 2k/1b with a recombination breakpoint at position 3,187 (95% confidence interval: 3,172–3,202; HCV GT1a reference strain H77). Phylogeography is a valuable tool used to reveal viral migration dynamics. Inference of the timed history of spread in a Bayesian framework identified Russia as the ancestral source of the CRF 2k/1b clade. Further, we found evidence for migration routes leading out of Russia to other former Soviet Republics or countries under the Soviet sphere of influence. These findings suggest an interplay between geopolitics and the historical spread of CRF 2k/1b.

A study on the circulating genotypes of hepatitis C virus in a tertiary care hospital in Central Kerala

Background

Hepatitis C is an emerging infection in India, which is known to progresses to liver cirrhosis and hepatocellular carcinoma. The persistence of chronic HCV infection is due to the existence of various genotypes and its various subtypes. There are seven different genotypes of HCV. These genotypes vary in their severity to cause infections as well as their response to treatment.

Aim

This study aims at identifying the predominant genotypes of HCV in a population of patients presenting in a tertiary care center in Central Kerala.

Settings and Design

This study was conducted at a tertiary care hospital and medical college, located in Central Kerala in the Department of Microbiology from January 2014 to June 2015.The sample size was 600 and a high risk group of patients attending the gastroenterology department, deaddiction centre and health care workers were screened.

Materials and Methods

Serum samples were subjected to EIA, either rapid card or ELISA. Serum samples that were positive for HCV antibodies were confirmed by PCR. Twenty seven samples were positive for HCV antibodies by ELISA/rapid card, out of which 16 were confirmed by PCR. These 16 samples were subjected to gene sequencing to identify the genotype.

Results

The prevalent genotypes isolated in this study was genotype 1, 3 and 4. Genotype 1 and 3 was predominantly seen transmitted by blood transfusions and multiple hemodialysis. The variability in laboratory parameters like SGOT and SGPT and its ratio with each genotype was also evaluated.

Conclusion

To conclude, the occurrence of genotype 4 at a similar level to genotype 1 shows diffusion of new genotype in Kerala.

HCV Phylogeography of the General Population and High-Risk Groups in Cyprus Identifies the Island as a Global Sink for and Source of Infection

Hepatitis C virus (HCV) genotype and subtype distribution differs according to geographic origin and transmission risk category. Previous molecular epidemiology studies suggest the presence of multiple subtypes among Cypriot subjects. To investigate HCV genotype- and subtype-specific dissemination patterns, origins, and transmission in Cyprus, we analyzed HCV sequences encoding partial Core-E1 and NS5B regions. Analyzed populations comprised the general population and high-risk cohorts in Cyprus and a globally sampled dataset. Maximum-likelihood phylogeny reconstruction with bootstrap evaluation, character reconstruction using parsimony, and bootstrap trees estimated by ML were performed to identify the geographic origin of HCV subtypes and statistically significant dispersal pathways among geographic regions. Phylogeographic analyses traced the origin of subtypes in the general population and among PWID in Cyprus to unique and overlapping globally distributed regions. Phylogenetic analysis in Core-E1 revealed that most sequences from incarcerated populations in Cyprus clustered with the general population and PWID. We estimate that HCV infections in Cyprus originate from multiple global sources while most HCV transmissions among incarcerated individuals occur locally. This analysis is one of a few studies tracing HCV dispersal patterns using global datasets, and these practices and findings should inform how HCV epidemics are targeted by future prevention policies.

Complete genome sequencing and evolutionary analysis of HCV subtype 6xg from IDUs in Yunnan, China

Background

HCV genotype 6 (HCV-6) typically circulates in Southeast Asia and exhibits the highest genetic diversity among the eight HCV genotypes. In our previous work, a group of HCV-6 sequences was not clearly classified. Here, we further characterized this HCV-6 variant and analyzed the evolutionary history of the enlarged HCV-6 family.

Methods

Blood samples from eight HCV seropositive samples collected from intravenous drug users (IDUs) in 2014 in Yunnan Province, China. The full-length HCV genome sequences were amplified by using reverse transcription PCR followed by DNA sequencing and phylogenetic analysis. Bayesian evolutionary analysis was performed with the complete coding region sequences of subtype 6a-6xh.

Results

The eight genomes had the same coding region of 9051 nucleotides. The complete coding region sequences of the eight HCV isolates formed a distinct phylogenetic group from the previously assigned HCV-6 subtypes (6a-6xf), however which clustered with 6xg reference sequences that were found in Kachin State, Myanmar, and recently assigned and released. The p-distances of the eight isolates to subtype 6a-6xf and 6xh ranged from 0.143 to 0.283. Based on the HCV-6 complete coding region sequences, we constructed a timescaled phylogenetic tree to reveal the HCV-6 evolutionary history, in which there were four HCV-6 phylogenetic subsets, whose median tMRCAs were 294.8, 388.5, 348.5 and 197.0 years ago, respectively. Subtype 6xg clustered into Subset I, and had the most recent common ancestor with subtype 6n, which dated back to 101.2 (95% HPD: 78.7, 125.8) years ago. The genetic evolutionary analysis further confirmed that subtype 6xg originated from Myanmar, and transmitted to Dehong through cross-border IDUs.

Conclusion

The HCV-6 variant characterized in this study belonged to newly assigned subtype 6xg. Our finding further confirmed the assignment of 6xg. HCV-6 family was highly divers and had a complicated evolutionary history in Southeast Asia. It is necessary to further characterize HCV-6 genetics in this region.

Dynamics between infectious diseases with two susceptibility conditions: A mathematical model

This paper presents a novel epidemiological transmission model of a population affected by two different susceptible-infected-susceptible infectious diseases. For each disease, individuals fall into one of the two susceptibility conditions in which one of the diseases has the highest occurrence level. This model is unique in assuming that: (a) if an individual is infected by one disease, their susceptibility to the other disease is increased; (b) when an individual recovers from a disease they become less susceptible to it, i.e. they acquire partial immunity. The model captures these two assumptions by utilizing a coupled system of differential equations. Dynamic analysis of the system is based on basic reproductive number theory, and pattern visualization was performed using numerical simulation.

A practical generation-interval-based approach to inferring the strength of epidemics from their speed

Infectious disease outbreaks are often characterized by the reproduction number R and exponential rate of growth r. R provides information about outbreak control and predicted final size, but estimating R is difficult, while r can often be estimated directly from incidence data. These quantities are linked by the generation interval - the time between when an individual is infected by an infector, and when that infector was infected. It is often infeasible to obtain the exact shape of a generation-interval distribution, and to understand how this shape affects estimates of R. We show that estimating generation interval mean and variance provides insight into the relationship between R and r. We use examples based on Ebola, rabies and measles to explore approximations based on gamma-distributed generation intervals, and find that use of these simple approximations are often sufficient to capture the r-R relationship and provide robust estimates of R.

What Is Needed to Eliminate Hepatitis B Virus and Hepatitis C Virus as Global Health Threats

Hepatitis B virus (HBV) and hepatitis C virus (HCV) cause 1.3 million deaths annually. To prevent more than 7 million deaths by 2030, the World Health Organization set goals to eliminate HBV and HCV, defined as a 90% reduction in new infections and a 65% reduction in deaths, and prevent more than 7 million related deaths by 2030. Elimination of HBV and HCV is feasible because of characteristics of the viruses, reliable diagnostic tools, and available cost-effective or cost-saving interventions. Broad implementation of infant immunization against HBV, blood safety, and infection-control programs have greatly reduced the burden of HBV and HCV infections. To achieve elimination, priorities include implementation of HBV vaccine-based strategies to prevent perinatal transmission, safe injection practices and HCV treatment for persons who inject drugs, and testing and treatment for HBV- and HCV-infected persons. With sufficient capacity, HBV and HCV elimination programs can meet their goals.

Profile of the VERSANT HCV genotype 2.0 assay

INTRODUCTION

Hepatitis C virus (HCV) is divided into 7 genotypes and 67 subtypes. HCV genotype studies reflect the viral transmission patterns as well as human migration routes. In a clinical setting, HCV genotype is a baseline predictor for the sustained virological response (SVR) in chronic hepatitis C patients treated with peginterferon or some direct acting antivirals (DAAs). The Versant HCV genotype 2.0 assay has been globally used for HCV genotyping over a decade. Areas covered: The assay is based on reverse hybridization principle. It is evolved from its former versions, and the accuracy and successful genotyping/subtyping rate are substantially improved. It shows an accuracy of 99-100% for genotypes 1-6. It can also reliably identify subtypes 1a and 1b. However, the assay does not allow a high resolution for many other subtypes. Reasons for indeterminate or inaccurate genotyping/subtyping results are discussed. Expert commentary: Genotyping helps to find the most efficacious and cost-effective treatment regimen. The rapid development of anti-HCV treatment regimens, however, is greatly simplifying laboratory tests. In the near future, the need for HCV genotyping and frequent serial on-treatment HCV RNA tests will decrease along with the wide use of the more potent and pan-genotypic DAA regimens.

Modeling the Growth and Decline of Pathogen Effective Population Size Provides Insight into Epidemic Dynamics and Drivers of Antimicrobial Resistance

Nonparametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stochastic processes with stationary increments which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that nonparametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a nonparametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data are sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta$\end{document}-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://github.com/mrc-ide/skygrowth.

Inferring population dynamics of HIV-1 subtype C epidemics in Eastern Africa and Southern Brazil applying different Bayesian phylodynamics approaches

The subtype C Eastern Africa clade (C_EA), a particularly successful HIV-1 subtype C lineage, has seeded several sub-epidemics in Eastern African countries and Southern Brazil during the 1960s and 1970s. Here, we characterized the past population dynamics of the major C_EA sub-epidemics in Eastern Africa and Brazil by using Bayesian phylodynamic approaches based on coalescent and birth-death models. All phylodynamic models support similar epidemic dynamics and exponential growth rates until roughly the mid-1980s for all the C_EA sub-epidemics. Divergent growth patterns, however, were supported afterwards. The Bayesian skygrid coalescent model (BSKG) and the birth-death skyline model (BDSKY) supported longer exponential growth phases than the Bayesian skyline coalescent model (BSKL). The BDSKY model uncovers patterns of a recent decline for the C_EA sub-epidemics in Burundi/Rwanda and Tanzania (R_e < 1) and a recent growth for Southern Brazil (R_e > 1); whereas coalescent models infer an epidemic stabilization. To the contrary, the BSKG model captured a decline of Ethiopian C_EA sub-epidemic between the mid-1990s and mid-2000s that was not uncovered by the BDSKY model. These results underscore that the joint use of different phylodynamic approaches may yield complementary insights into the past HIV population dynamics.

Evolutionary Analysis Provides Insight Into the Origin and Adaptation of HCV

Hepatitis C virus (HCV) belongs to the Hepacivirus genus and is genetically heterogeneous, with seven major genotypes further divided into several recognized subtypes. HCV origin was previously dated in a range between ∼200 and 1000 years ago. Hepaciviruses have been identified in several domestic and wild mammals, the largest viral diversity being observed in bats and rodents. The closest relatives of HCV were found in horses/donkeys (equine hepaciviruses, EHV). However, the origin of HCV as a human pathogen is still an unsolved puzzle. Using a selection-informed evolutionary model, we show that the common ancestor of extant HCV genotypes existed at least 3000 years ago (CI: 3192–5221 years ago), with the oldest genotypes being endemic to Asia. EHV originated around 1100 CE (CI: 291–1640 CE). These time estimates exclude that EHV transmission was mainly sustained by widespread veterinary practices and suggest that HCV originated from a single zoonotic event with subsequent diversification in human populations. We also describe a number of biologically important sites in the major HCV genotypes that have been positively selected and indicate that drug resistance-associated variants are significantly enriched at positively selected sites. HCV exploits several cell-surface molecules for cell entry, but only two of these (CD81 and OCLN) determine the species-specificity of infection. Herein evolutionary analyses do not support a long-standing association between primates and hepaciviruses, and signals of positive selection at CD81 were only observed in Chiroptera. No evidence of selection was detected for OCLN in any mammalian order. These results shed light on the origin of HCV and provide a catalog of candidate genetic modulators of HCV phenotypic diversity.