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The SARS-CoV-2 lineage B.1.1.7, designated variant of concern (VOC) 202012/01 by Public Health England1, was first identified in the UK in late summer to early autumn 20202. Whole-genome SARS-CoV-2 sequence data collected from community-based diagnostic testing for COVID-19 show an extremely rapid expansion of the B.1.1.7 lineage during autumn 2020, suggesting that it has a selective advantage. Here we show that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S gene target failures (SGTF) in community-based diagnostic PCR testing. Analysis of trends in SGTF and non-SGTF case numbers in local areas across England shows that B.1.1.7 has higher transmissibility than non-VOC lineages, even if it has a different latent period or generation time. The SGTF data indicate a transient shift in the age composition of reported cases, with cases of B.1.1.7 including a larger share of under 20-year-olds than non-VOC cases. We estimated time-varying reproduction numbers for B.1.1.7 and co-circulating lineages using SGTF and genomic data. The best-supported models did not indicate a substantial difference in VOC transmissibility among different age groups, but all analyses agreed that B.1.1.7 has a substantial transmission advantage over other lineages, with a 50% to 100% higher reproduction number.

Objective The aim of this study was to investigate introductions and spread of different HIV-1 subtypes in the Netherlands. Design We identified distinct HIV-1 transmission chains in the Netherlands within the global epidemic context through viral phylogenetic analysis of partial HIV-1 polymerase sequences from individuals enrolled in the ATHENA national HIV cohort of all persons in care since 1996, and publicly available international background sequences. Methods Viral lineages circulating in the Netherlands were identified through maximum parsimony phylogeographic analysis. The proportion of HIV-1 infections acquired in-country among heterosexuals and MSM was estimated from phylogenetically observed, national transmission chains using a branching process model that accounts for incomplete sampling. Results As of 1 January 2019, 2589 (24%) of 10 971 (41%) HIV-1 sequenced individuals in ATHENA had non-B subtypes (A1, C, D, F, G) or circulating recombinant forms (CRF01AE, CRF02AG, CRF06-cpx). The 1588 heterosexuals were in 1224, and 536 MSM in 270 phylogenetically observed transmission chains. After adjustments for incomplete sampling, most heterosexual (75%) and MSM (76%) transmission chains were estimated to include only the individual introducing the virus (size = 1). Onward transmission occurred mostly in chains size 2-5 amongst heterosexuals (62%) and in chains size at least 10 amongst MSM (64%). Considering some chains originated in-country from other risk-groups, 40% (95% confidence interval: 36-44) of non-B-infected heterosexuals and 62% (95% confidence interval: 49-73) of MSM-acquired infection in-country. Conclusion Although most HIV-1 non-B introductions showed no or very little onward transmission, a considerable proportion of non-B infections amongst both heterosexuals and MSM in the Netherlands have been acquired in-country.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were rising during early summer 2021 in many countries as a result of the Delta variant. We assessed reverse transcription polymerase chain reaction swab positivity in the Real-time Assessment of Community Transmission–1 (REACT-1) study in England. During June and July 2021, we observed sustained exponential growth with an average doubling time of 25 days, driven by complete replacement of the Alpha variant by Delta and by high prevalence at younger, less-vaccinated ages. Prevalence among unvaccinated people [1.21% (95% credible interval 1.03%, 1.41%)] was three times that among double-vaccinated people [0.40% (95% credible interval 0.34%, 0.48%)]. However, after adjusting for age and other variables, vaccine effectiveness for double-vaccinated people was estimated at between ~50% and ~60% during this period in England. Increased social mixing in the presence of Delta had the potential to generate sustained growth in infections, even at high levels of vaccination.

We identify amino acid variants within dominant SARS-CoV-2 T-cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T-cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T-cell responsiveness was seen due to Q213K in the A*01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215, due to P13L, P13S and P13T in the B*27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17, and due to T362I and P365S in the A*03:01/A*11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T-cell lines unable to recognise variant epitopes have diverse T-cell receptor repertoires. These data demonstrate the potential for T-cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T-cell as well as humoral immunity., Graphical Abstract

We report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike ΔH69/V70 in multiple independent lineages, often occurring after acquisition of receptor binding motif replacements such as N439K and Y453F, known to increase binding affinity to the ACE2 receptor and confer antibody escape. In vitro, we show that, although ΔH69/V70 itself is not an antibody evasion mechanism, it increases infectivity associated with enhanced incorporation of cleaved spike into virions. ΔH69/V70 is able to partially rescue infectivity of spike proteins that have acquired N439K and Y453F escape mutations by increased spike incorporation. In addition, replacement of the H69 and V70 residues in the Alpha variant B.1.1.7 spike (where ΔH69/V70 occurs naturally) impairs spike incorporation and entry efficiency of the B.1.1.7 spike pseudotyped virus. Alpha variant B.1.1.7 spike mediates faster kinetics of cell-cell fusion than wild-type Wuhan-1 D614G, dependent on ΔH69/V70. Therefore, as ΔH69/V70 compensates for immune escape mutations that impair infectivity, continued surveillance for deletions with functional effects is warranted., Graphical abstract, Meng et al. report that the SARS-CoV-2 spike ΔH69/V70 has arisen multiple times. The deletion increases entry efficiency associated with increased cleaved spike in virions and can compensate for loss of infectivity. The B.1.1.7 spike requires ΔH69/V70 for efficient cell entry and cell-cell fusion activity.

An important component of pathogen evolution at the population level is evolution within hosts. Unless evolution within hosts is very slow compared to the duration of infection, the composition of pathogen genotypes within a host is likely to change during the course of an infection, thus altering the composition of genotypes available for transmission as infection progresses. We develop a nested modeling approach that allows us to follow the evolution of pathogens at the epidemiological level by explicitly considering within-host evolutionary dynamics of multiple competing strains and the timing of transmission. We use the framework to investigate the impact of short-sighted within-host evolution on the evolution of virulence of human immunodeficiency virus (HIV), and find that the topology of the within-host adaptive landscape determines how virulence evolves at the epidemiological level. If viral reproduction rates increase significantly during the course of infection, the viral population will evolve a high level of virulence even though this will reduce the transmission potential of the virus. However, if reproduction rates increase more modestly, as data suggest, our model predicts that HIV virulence will be only marginally higher than the level that maximizes the transmission potential of the virus.

A new variant of SARS-CoV-2 has emerged which is increasing in frequency, primarily in the South East of England (lineage B.1.1.7 (1); VUI-202012/01). One potential hypothesis is that infection with the new variant results in higher viral loads, which in turn may make the virus more transmissible. We found higher (sequence derived) viral loads in samples from individuals infected with the new variant with median inferred viral loads were three-fold higher in individuals with the new variant. Most of the new variants were sampled in Kent and Greater London. We observed higher viral loads in Kent compared to Greater London for both the new variant and other circulating lineages. Outside Greater London, the variant has higher viral loads, whereas within Greater London, the new variant does not have significantly higher viral loads compared to other circulating lineages. Higher variant viral loads outside Greater London could be due to demographic effects, such as a faster variant growth rate compared to other lineages or concentration in particular age-groups. However, our analysis does not exclude a causal link between infection with the new variant and higher viral loads. This is a preliminary analysis and further work is needed to investigate any potential causal link between infection with this new variant and higher viral loads, and whether this results in higher transmissibility, severity of infection, or affects relative rates of symptomatic and asymptomatic infectionDocument Description and PurposeThis is an updated report submitted to NERVTAG in December 2020 as part of urgent investigations into the new variant of SARS-COV-2 (VUI-202012/01). It makes full use of (and is restricted to) all sequence data and associated metadata available to us at the time this original report was submitted and remains provisional. Under normal circumstances more genomes and metadata would be obtained and included before making this report public. We will update this preprint when more genomes and metadata are available and before submitting for peer review.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant., Graphical Abstract, Highlights • Increasing frequency of SARS-CoV-2 D614G is consistent with a selective advantage • Phylodynamic analyses do not show significantly different growth of D614G clusters • There is no association of D614G replacement with greater severity of infection • The D614G replacement is associated with higher viral loads and younger patient age, Analysis of the spread and frequency of SARS-CoV-2 D614G in the United Kingdom suggests a selective advantage for this strain that is associated with higher viral loads in younger patients but not higher COVID-19 clinical severity or mortality.

Objectives HIV-1 integrase inhibitors are recommended as first-line therapy by WHO, though efficacy and resistance data for non-B subtypes are limited. Two recent trials have identified the integrase L74I mutation to be associated with integrase inhibitor treatment failure in HIV-1 non-B subtypes. We sought to define the prevalence of integrase resistance mutations, including L74I, in West Africa. Methods We studied a Nigerian cohort of recipients prior to and during receipt of second-line PI-based therapy, who were integrase inhibitor-naive. Illumina next-generation sequencing with target enrichment was used on stored plasma samples. Drug resistance was interpreted using the Stanford Resistance Database and the IAS-USA 2019 mutation lists. Results Of 115 individuals, 59.1% harboured CRF02_AG HIV-1 and 40.9% harboured subtype G HIV-1. Four participants had major IAS-USA integrase resistance-associated mutations detected at low levels (2%–5% frequency). Two had Q148K minority variants and two had R263K (one of whom also had L74I). L74I was detected in plasma samples at over 2% frequency in 40% (46/115). Twelve (26.1%) had low-level minority variants of between 2% and 20% of the viral population sampled. The remaining 34 (73.9%) had L74I present at >20% frequency. L74I was more common among those with subtype G infection (55.3%, 26/47) than those with CRF02_AG infection (29.4%, 20/68) (P = 0.005). Conclusions HIV-1 subtypes circulating in West Africa appear to have very low prevalence of major integrase mutations, but significant prevalence of L74I. A combination of in vitro and clinical studies is warranted to understand the potential implications.

Recombination is an important feature of HIV evolution, occurring both within and between the major branches of diversity (subtypes). The Ugandan epidemic is primarily composed of two subtypes, A1 and D, that have been co-circulating for 50 years, frequently recombining in dually infected patients. Here, we investigate the frequency of recombinants in this population and the location of breakpoints along the genome. As part of the PANGEA-HIV consortium, 1,472 consensus genome sequences over 5 kb have been obtained from 1,857 samples collected by the MRC/UVRI & LSHTM Research unit in Uganda, 465 (31.6 per cent) of which were near full-length sequences (>8 kb). Using the subtyping tool SCUEAL, we find that of the near full-length dataset, 233 (50.1 per cent) genomes contained only one subtype, 30.8 per cent A1 (n = 143), 17.6 per cent D (n = 82), and 1.7 per cent C (n = 8), while 49.9 per cent (n = 232) contained more than one subtype (including A1/D (n = 164), A1/C (n = 13), C/D (n = 9); A1/C/D (n = 13), and 33 complex types). K-means clustering of the recombinant A1/D genomes revealed a section of envelope (C2gp120-TMgp41) is often inherited intact, whilst a generalized linear model was used to demonstrate significantly fewer breakpoints in the gag–pol and envelope C2-TM regions compared with accessory gene regions. Despite similar recombination patterns in many recombinants, no clearly supported circulating recombinant form (CRF) was found, there was limited evidence of the transmission of breakpoints, and the vast majority (153/164; 93 per cent) of the A1/D recombinants appear to be unique recombinant forms. Thus, recombination is pervasive with clear biases in breakpoint location, but CRFs are not a significant feature, characteristic of a complex, and diverse epidemic. 1. Introduction 2. Methods 2.1 Sample collection 2.2 Sequencing and alignment 2.3 Subtyping 2.4 Identification of transmitted breakpoints 2.5 Recombination pattern classification 2.6 Breakpoint and genome location model framework 3. Results 3.1 Subtype distribution 3.2 Identification of CRFs and transmitted breakpoints 3.3 Recombinant groupings 3.4 Breakpoint distribution 4. Discussion

Across sub-Saharan Africa, key populations with elevated HIV-1 incidence and/or prevalence have been identified, but their contribution to disease spread remains unclear. We performed viral deep-sequence phylogenetic analyses to quantify transmission dynamics between the general population (GP), fisherfolk communities (FF), and women at high risk of infection and their clients (WHR) in central and southwestern Uganda. Between August 2014 and August 2017, 6185 HIV-1 positive individuals were enrolled in 3 GP and 10 FF communities, 3 WHR enrollment sites. A total of 2531 antiretroviral therapy (ART) na&iuml, ve participants with plasma viral load &gt, 1000 copies/mL were deep-sequenced. One hundred and twenty-three transmission networks were reconstructed, including 105 phylogenetically highly supported source&ndash, recipient pairs. Only one pair involved a WHR and male participant, suggesting that improved population sampling is needed to assess empirically the role of WHR to the transmission dynamics. More transmissions were observed from the GP communities to FF communities than vice versa, with an estimated flow ratio of 1.56 (95% CrI 0.68&ndash, 3.72), indicating that fishing communities on Lake Victoria are not a net source of transmission flow to neighboring communities further inland. Men contributed disproportionally to HIV-1 transmission flow regardless of age, suggesting that prevention efforts need to better aid men to engage with and stay in care.

Objectives To evaluate the performance of a high-throughput research assay for HIV drug resistance testing based on whole genome next-generation sequencing (NGS) that also quantifies HIV viral load. Methods Plasma samples (n = 145) were obtained from HIV-positive MSM (HPTN 078). Samples were analysed using clinical assays (the ViroSeq HIV-1 Genotyping System and the Abbott RealTime HIV-1 Viral Load assay) and a research assay based on whole-genome NGS (veSEQ-HIV). Results HIV protease and reverse transcriptase sequences (n = 142) and integrase sequences (n = 138) were obtained using ViroSeq. Sequences from all three regions were obtained for 100 (70.4%) of the 142 samples using veSEQ-HIV; results were obtained more frequently for samples with higher viral loads (93.5% for 93 samples with >5000 copies/mL; 50.0% for 26 samples with 1000–5000 copies/mL; 0% for 23 samples with Conclusions The NGS-based veSEQ-HIV method provided results for most samples with higher viral loads, was accurate for detecting major DRMs, and detected mutations at lower levels compared with a method based on population sequencing. The veSEQ-HIV method also provided HIV viral load data.

Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain, estimated by an NFDS-based model at the time the vaccine is introduced, enables us to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force., Predicting how pathogen populations will change over time is challenging. This study predicts population changes in human pathogen Streptococcus pneumoniae after vaccination, using a model of negative frequency-dependent selection acting on accessory gene products.

Methicillin-resistant Staphylococcus aureus (MRSA) transmission in the hospital setting has been a frequent subject of investigation using bacterial genomes, but previous approaches have not yet fully utilised the extra deductive power provided when multiple pathogen samples are acquired from each host. Here, we used a large dataset of MRSA sequences from multiply-sampled patients to reconstruct colonisation of individuals in a high-transmission setting in a hospital in Thailand. We reconstructed transmission trees for MRSA. We also investigated transmission between anatomical sites on the same individual, finding that this either occurs repeatedly or involves a wide transmission bottleneck. We examined the between-subject bottleneck, finding considerable variation in the amount of diversity transmitted. Finally, we compared our approach to the simpler method of identifying transmission pairs using single nucleotide polymorphism (SNP) counts. This suggested that the optimum threshold for identifying a pair is 39 SNPs, if sensitivities and specificities are equally weighted.

A diverse set of mobile genetic elements (MGEs) transmit between Streptococcus pneumoniae cells, but many isolates remain uninfected. The best-characterised defences against horizontal transmission of MGEs are restriction-modification systems (RMSs), of which there are two phase-variable examples in S. pneumoniae. Additionally, the transformation machinery has been proposed to limit vertical transmission of chromosomally integrated MGEs. This work describes how these mechanisms can act in concert. Experimental data demonstrate RMS phase variation occurs at a sub-maximal rate. Simulations suggest this may be optimal if MGEs are sometimes vertically inherited, as it reduces the probability that an infected cell will switch between RMS variants while the MGE is invading the population, and thereby undermine the restriction barrier. Such vertically inherited MGEs can be deleted by transformation. The lack of between-strain transformation hotspots at known prophage att sites suggests transformation cannot remove an MGE from a strain in which it is fixed. However, simulations confirmed that transformation was nevertheless effective at preventing the spread of MGEs into a previously uninfected cell population, if a recombination barrier existed between co-colonising strains. Further simulations combining these effects of phase variable RMSs and transformation found they synergistically inhibited MGEs spreading, through limiting both vertical and horizontal transmission.

Resistance frequencies are best explained as the outcome of selection, irrespective of the rate at which resistance is acquired., The extent to which evolution is constrained by the rate at which horizontal gene transfer (HGT) allows DNA to move between genetic lineages is an open question, which we address in the context of antibiotic resistance in Streptococcus pneumoniae. We analyze microbiological, genomic, and epidemiological data from the largest-to-date sequenced pneumococcal carriage study in 955 infants from a refugee camp on the Thailand-Myanmar border. Using a unified framework, we simultaneously test prior hypotheses on rates of HGT and a key evolutionary covariate (duration of carriage) as determinants of resistance frequencies. We conclude that in this setting, there is little evidence of HGT playing a major role in determining resistance frequencies. Instead, observed resistance frequencies are best explained as the outcome of selection acting on a pool of variants, irrespective of the rate at which resistance determinants move between genetic lineages.

Understanding which HIV-1 variants are most likely to be transmitted is important for vaccine design and predicting virus evolution. Since most infections are founded by single variants, it has been suggested that selection at transmission has a key role in governing which variants are transmitted. We show that the composition of the viral population within the donor at the time of transmission is also important. To support this argument, we developed a probabilistic model describing HIV-1 transmission in an untreated population, and parameterised the model using both within-host next generation sequencing data and population-level epidemiological data on heterosexual transmission. The most basic HIV-1 transmission models cannot explain simultaneously the low probability of transmission and the non-negligible proportion of infections founded by multiple variants. In our model, transmission can only occur when environmental conditions are appropriate (e.g. abrasions are present in the genital tract of the potential recipient), allowing these observations to be reconciled. As well as reproducing features of transmission in real populations, our model demonstrates that, contrary to expectation, there is not a simple link between the number of viral variants and the number of viral particles founding each new infection. These quantities depend on the timing of transmission, and infections can be founded with small numbers of variants yet large numbers of particles. Including selection, or a bias towards early transmission (e.g. due to treatment) acts to enhance this conclusion. In addition, we find that infections initiated by multiple variants are most likely to have derived from donors with intermediate set-point viral loads, and not from individuals with high set-point viral loads as might be expected. We therefore emphasise the importance of considering viral diversity in donors, and the timings of transmissions, when trying to discern the complex factors governing single or multiple variant transmission.

Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain estimated by an NFDS-based model at the time the vaccine is introduced enables to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.

High-throughput viral genetic sequencing is needed to monitor the spread of drug resistance, direct optimal antiretroviral regimes, and to identify transmission dynamics in generalised HIV epidemics. Public health efforts to sequence HIV genomes at scale face three major technical challenges: (i) minimising assay cost and protocol complexity, (ii) maximising sensitivity, and (iii) recovering accurate and unbiased sequences of both the genome consensus and the within-host viral diversity. Here we present a novel, high-throughput, virus-enriched sequencing method and computational pipeline tailored specifically to HIV (veSEQ-HIV), which addresses all three technical challenges, and can be used directly on leftover blood drawn for routine CD4 testing. We demonstrate its performance on 1,620 plasma samples collected from consenting individuals attending 10 large urban clinics in Zambia, partners of HPTN 071 (PopART). We show that veSEQ-HIV consistently recovers complete HIV genomes from the majority of samples of different subtypes, and is also quantitative: the number of HIV reads per sample obtained by veSEQ-HIV estimates viral load without the need for additional testing. Both quantitativity and sensitivity were assessed on a subset of 126 samples with clinically measured viral loads, and with standardized quantification controls (VL 100 – 5,000,000 RNA copies/ml). Complete HIV genomes were recovered from 93% (85/91) of samples when viral load was over 1,000 copies per ml. The quantitative nature of the assay implies that variant frequencies estimated with veSEQ-HIV are representative of true variant frequencies in the sample. Detection of minority variants can be exploited for epidemiological analysis of transmission and drug resistance, and we show how the information contained in individual reads of a veSEQ-HIV sample can be used to detect linkage between multiple mutations associated with resistance to antiretroviral therapy. Less than 2% of reads obtained by veSEQ-HIV were identified as in silico contamination events using updates to the phyloscanner software (phyloscanner clean) that we show to be 95% sensitive and 99% specific at ‘decontaminating’ NGS data. The cost of the assay — approximately 45 USD per sample — compares favourably with existing VL and HIV genotyping tests, and provides the additional value of viral load quantification and inference of drug resistance with a single test. veSEQ-HIV is well suited to large public health efforts and is being applied to all ∼9000 samples collected for the HPTN 071-2 (PopART Phylogenetics) study.

There are global increases in the use of HIV antiretroviral therapy (ART), guided by clinical benefits of early ART initiation, and the efficacy of treatment as prevention of transmission. UNAIDS has set forth an ambitious 90-90-90 strategy for 2020. Separately, it has been shown theoretically and empirically that HIV virulence can evolve over time. Observed virulence levels may reflect an adaptive balance between infected lifespan and per-contact transmission rate. Critically, the potential effects of widespread ART usage on HIV virulence are unknown. To predict these effects, we used an agent-based stochastic model to simulate changes in HIV virulence, using set point viral load as a virulence proxy. We calibrated our epidemic model to the prevalence and incidence trends of South Africa. We also repeated our analysis using a separate model calibration, as a sensitivity analysis, and found that predicted impact of ART on virulence was relatively insensitive to calibration. We explored two distinct ART scenarios, at increasing coverage levels: ART initiation based on patients reaching a CD4+ T cell count threshold, or based on time elapsed since infection (a scenario that mimics the "universal testing and treatment" aspirations in the 90-90-90 UNAIDS targets). We found that HIV virulence is generally unchanged in scenarios of CD4-based initiation. However, with ART initiation based on time since infection (universal test and treat), virulence can increase moderately within several years of ART rollout, under high coverage levels and early treatment initiation albeit within the context of epidemics that are rapidly decreasing in size. We also show that, compared to non-evolutionary HIV epidemic models, allowing for evolving virulence predicts greater declines in HIV incidence after ART rollout. Our modeling study suggests that increasing virulence driven by universal test and treat is likely not a major public health concern, but could be monitored in sentinel surveillance, in a manner similar to transmitted drug resistance.

BackgroundWe evaluated use of phylogenetic methods to predict the direction of human immunodeficiency virus (HIV) transmission.MethodsFor 33 pairs of HIV-infected patients (hereafter, “index patients”) and their partners who acquired genetically linked HIV infection during the study, samples were collected from partners and index patients close to the time when the partner seroconverted (hereafter, “SC samples”); for 31 pairs, samples collected from the index patient at an earlier time point (hereafter, “early index samples”) were also available. Phylogenies were inferred using env next-generation sequences (1 tree per pair/subtype). The direction of transmission (DoT) predicted from each tree was classified as correct or incorrect on the basis of which sequences (those from the index patient or the partner) were closest to the root. DoT was also assessed using maximum parsimony to infer ancestral node states for 100 bootstrap trees.ResultsDoT was predicted correctly for both single-pair and subtype-specific trees in 22 pairs (67%) by using SC samples and in 23 pairs (74%) by using early index samples. DoT was predicted incorrectly for 4 pairs (15%) by using SC or early index samples. In the bootstrap analysis, DoT was predicted correctly for 18 pairs (55%) by using SC samples and for 24 pairs (73%) by using early index samples. DoT was predicted incorrectly for 7 pairs (21%) by using SC samples and for 4 pairs (13%) by using early index samples.ConclusionsPhylogenetic methods based solely on the tree topology of HIV env sequences, particularly without consideration of phylogenetic uncertainty, may be insufficient for determining DoT.

For the production of viral genomic RNA, HIV-1 is dependent on an early viral protein, Tat, which is required for high-level transcription. The quantity of viral RNA detectable in blood of HIV-1 infected individuals varies dramatically, and a factor involved could be the efficiency of Tat protein variants to stimulate RNA transcription. HIV-1 virulence, measured by set-point viral load, has been observed to increase over time in the Netherlands and elsewhere. Investigation of tat gene evolution in clinical isolates could discover a role of Tat in this changing virulence. A dataset of 291 Dutch HIV-1 subtype B tat genes, derived from full-length HIV-1 genome sequences from samples obtained between 1985–2012, was used to analyse the evolution of Tat. Twenty-two patient-derived tat genes, and the control TatHXB2 were analysed for their capacity to stimulate expression of an LTR-luciferase reporter gene construct in diverse cell lines, as well as for their ability to complement a tat-defective HIV-1LAI clone. Analysis of 291 historical tat sequences from the Netherlands showed ample amino acid (aa) variation between isolates, although no specific mutations were selected for over time. Of note, however, the encoded protein varied its length over the years through the loss or gain of stop codons in the second exon. In transmission clusters, a selection against the shorter Tat86 ORF was apparent in favour of the more common Tat101 version, likely due to negative selection against Tat86 itself, although random drift, transmission bottlenecks, or linkage to other variants could also explain the observation. There was no correlation between Tat length and set-point viral load; however, the number of non-intermediate variants in our study was small. In addition, variation in the length of Tat did not significantly change its capacity to stimulate transcription. From 1985 till 2012, variation in the length of the HIV-1 subtype B tat gene is increasingly found in the Dutch epidemic. However, as Tat proteins did not differ significantly in their capacity to stimulate transcription elongation in vitro, the increased HIV-1 virulence seen in recent years could not be linked to an evolving viral Tat protein.

A central feature of pathogen genomics is that different infectious particles (virions, bacterial cells, etc.) within an infected individual may be genetically distinct, with patterns of relatedness amongst infectious particles being the result of both within-host evolution and transmission from one host to the next. Here we present a new software tool, phyloscanner, which analyses pathogen diversity from multiple infected hosts. phyloscanner provides unprecedented resolution into the transmission process, allowing inference of the direction of transmission from sequence data alone. Multiply infected individuals are also identified, as they harbour subpopulations of infectious particles that are not connected by within-host evolution, except where recombinant types emerge. Low-level contamination is flagged and removed. We illustrate phyloscanner on both viral and bacterial pathogens, namely HIV-1 sequenced on Illumina and Roche 454 platforms, HCV sequenced with the Oxford Nanopore MinION platform, and Streptococcus pneumoniae with sequences from multiple colonies per individual. phyloscanner is available from https://github.com/BDI-pathogens/phyloscanner.

Heterogeneity in individual-level transmissibility can be quantified by the dispersion parameter k of the offspring distribution. Quantifying heterogeneity is important as it affects other parameter estimates, it modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. Aggregated data such as incidence time series are often not sufficiently informative to estimate k. Incorporating phylogenetic analysis can help to estimate k concurrently with other epidemiological parameters. We have developed an inference framework that uses particle Markov Chain Monte Carlo to estimate k and other epidemiological parameters using both incidence time series and the pathogen phylogeny. Using the framework to fit a modified compartmental transmission model that includes the parameter k to simulated data, we found that more accurate and less biased estimates of the reproductive number were obtained by combining epidemiological and phylogenetic analyses. However, k was most accurately estimated using pathogen phylogeny alone. Accurately estimating k was necessary for unbiased estimates of the reproductive number, but it did not affect the accuracy of reporting probability and epidemic start date estimates. We further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. Finally, we used the inference framework to estimate transmission parameters from epidemiological and genetic data collected during a poliovirus outbreak. Despite the large degree of phylogenetic uncertainty, we demonstrated that incorporating phylogenetic data in parameter inference improved the accuracy and precision of estimates.

Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often cocirculate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, because the changes in population structure associated with the recent introduction of partial-coverage vaccines have substantially reduced pneumococcal disease. Here we show that pneumococcal lineages from multiple populations each have a distinct combination of intermediate-frequency genes. Functional analysis suggested that these loci may be subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Bayesian Computation generated reproducible estimates of the influence of NFDS on pneumococcal evolution, the strength of which varied between loci. Simulations replicated the stable frequency of lineages unperturbed by vaccination, patterns of serotype switching and clonal replacement. This framework highlights how bacterial ecology affects the impact of clinical interventions.Accessory loci are shown to have similar frequencies in diverse Streptococcus pneumoniae populations, suggesting negative frequency-dependent selection drives post-vaccination population restructuring.

HIV-1 set-point viral load—the approximately stable value of viraemia in the first years of chronic infection—is a strong predictor of clinical outcome and is highly variable across infected individuals. To better understand HIV-1 pathogenesis and the evolution of the viral population, we must quantify the heritability of set-point viral load, which is the fraction of variation in this phenotype attributable to viral genetic variation. However, current estimates of heritability vary widely, from 6% to 59%. Here we used a dataset of 2,028 seroconverters infected between 1985 and 2013 from 5 European countries (Belgium, Switzerland, France, the Netherlands and the United Kingdom) and estimated the heritability of set-point viral load at 31% (CI 15%–43%). Specifically, heritability was measured using models of character evolution describing how viral load evolves on the phylogeny of whole-genome viral sequences. In contrast to previous studies, (i) we measured viral loads using standardized assays on a sample collected in a strict time window of 6 to 24 months after infection, from which the viral genome was also sequenced; (ii) we compared 2 models of character evolution, the classical “Brownian motion” model and another model (“Ornstein–Uhlenbeck”) that includes stabilising selection on viral load; (iii) we controlled for covariates, including age and sex, which may inflate estimates of heritability; and (iv) we developed a goodness of fit test based on the correlation of viral loads in cherries of the phylogenetic tree, showing that both models of character evolution fit the data well. An overall heritability of 31% (CI 15%–43%) is consistent with other studies based on regression of viral load in donor–recipient pairs. Thus, about a third of variation in HIV-1 virulence is attributable to viral genetic variation., Author summary The severity of the outcome of infection by a pathogen depends on many distinct factors. These include the environment and the genetic sequences of both the host and the pathogen, among others. The fraction of variability in disease outcome explained by pathogen genetic factors is termed “heritability”, because these factors are “inherited” by the new host upon infection. Quantifying heritability is key to understanding the development of the disease and the evolution of the virus. Here, we determined heritability of set-point viral load (SPVL) in HIV-1. SPVL is the stable value of viraemia in asymptomatic infection and it is a strong predictor of disease severity. While heritability of SPVL has been estimated using comparisons of viral genome sequences, this has resulted in widely variable estimates of heritability. Using a large dataset of patients living in Europe, standardised viral loads measures, and new methods, we obtain a more definitive estimate of HIV-1 SPVL heritability in Europe at about 30%. Thus, a significant amount of the variation in disease outcome is explained by the genetics of the virus.

Diversity of the polysaccharide capsule in Streptococcus pneumoniae—main surface antigen and the target of the currently used pneumococcal vaccines—constitutes a major obstacle in eliminating pneumococcal disease. Such diversity is genetically encoded by almost 100 variants of the capsule biosynthesis locus, cps. However, the evolutionary dynamics of the capsule remains not fully understood. Here, using genetic data from 4,519 bacterial isolates, we found cps to be an evolutionary hotspot with elevated substitution and recombination rates. These rates were a consequence of relaxed purifying selection and positive, diversifying selection acting at this locus, supporting the hypothesis that the capsule has an increased potential to generate novel diversity compared with the rest of the genome. Diversifying selection was particularly evident in the region of wzd/wze genes, which are known to regulate capsule expression and hence the bacterium’s ability to cause disease. Using a novel, capsule-centered approach, we analyzed the evolutionary history of 12 major serogroups. Such analysis revealed their complex diversification scenarios, which were principally driven by recombination with other serogroups and other streptococci. Patterns of recombinational exchanges between serogroups could not be explained by serotype frequency alone, thus pointing to nonrandom associations between co-colonizing serotypes. Finally, we discovered a previously unobserved mosaic serotype 39X, which was confirmed to carry a viable and structurally novel capsule. Adding to previous discoveries of other mosaic capsules in densely sampled collections, these results emphasize the strong adaptive potential of the bacterium by its ability to generate novel antigenic diversity by recombination.

The coalescent has been used to infer from gene genealogies the population dynamics of biological systems, such as the prevalence of an infectious disease. The offspring distribution affects the relationship between population dynamics and the genealogy, and for infectious diseases, the offspring distribution is often highly overdispersed. Here, we provide a general formula for the coalescent rate for populations with time-varying sizes and any offspring distribution. The formula is valid in the same large population limit as Kingman’s original derivation. By relating our derivation to existing formulations of the coalescent, we show that differences in the coalescent rate derived for many population models may be explained by differences in the offspring distribution. The coalescent derivations presented here could be used to quantify the overdispersion in the offspring distribution of infectious diseases, which is useful for accurate modelling disease outbreaks.

The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics. ispartof: Nature vol:544 issue:7650 pages:309- ispartof: location:England status: published

It is a truism that antimicrobial drugs select for resistance, but explaining pathogen- and population-specific variation in patterns of resistance remains an open problem. Like other common commensals, Streptococcus pneumoniae has demonstrated persistent coexistence of drug-sensitive and drug-resistant strains. Theoretically, this outcome is unlikely. We modelled the dynamics of competing strains of S. pneumoniae to investigate the impact of transmission dynamics and treatment-induced selective pressures on the probability of stable coexistence. We find that the outcome of competition is extremely sensitive to structure in the host population, although coexistence can arise from age-assortative transmission models with age-varying rates of antibiotic use. Moreover, we find that the selective pressure from antibiotics arises not so much from the rate of antibiotic use per se but from the frequency of treatment: frequent antibiotic therapy disproportionately impacts the fitness of sensitive strains. This same phenomenon explains why serotypes with longer durations of carriage tend to be more resistant. These dynamics may apply to other potentially pathogenic, microbial commensals and highlight how population structure, which is often omitted from models, can have a large impact.

Capsular diversity ofStreptococcus pneumoniaeconstitutes a major obstacle in eliminating the pneumococcal disease. Such diversity is genetically encoded by almost 100 variants of the capsule polysaccharide locus (cps). However, the evolutionary dynamics of the capsule – the target of the currently used vaccines – remains not fully understood. Here, using genetic data from 4,469 bacterial isolates, we foundcpsto be an evolutionary hotspot with elevated substitution and recombination rates. These rates were a consequence of altered selection at this locus, supporting the hypothesis that the capsule has an increased potential to generate novel diversity compared to the rest of the genome. Analysis of twelve serogroups revealed their complex evolutionary history, which was principally driven by recombination with other serogroups and other streptococci. We observed significant variation in recombination rates between different serogroups. This variation could only be partially explained by the lineage-specific recombination rate, the remaining factors being likely driven by serogroup-specific ecology and epidemiology. Finally, we discovered two previously unobserved mosaic serotypes in the densely sampled collection from Mae La, Thailand, here termed 10X and 21X. Our results thus emphasise the strong adaptive potential of the bacterium by its ability to generate novel serotypes by recombination.

Understanding how changes in antibiotic consumption affect the prevalence of antibiotic resistance in bacterial pathogens is important for public health. In a number of bacterial species, including Streptococcus pneumoniae, the prevalence of resistance has remained relatively stable despite prolonged selection pressure from antibiotics. The evolutionary processes allowing the robust coexistence of antibiotic sensitive and resistant strains are not fully understood. While allelic diversity can be maintained at a locus by direct balancing selection, there is no evidence for such selection acting in the case of resistance. In this work, we propose a mechanism for maintaining coexistence at the resistance locus: linkage to a second locus that is under balancing selection and that modulates the fitness effect of resistance. We show that duration of carriage plays such a role, with long duration of carriage increasing the fitness advantage gained from resistance. We therefore predict that resistance will be more common in strains with a long duration of carriage and that mechanisms maintaining diversity in duration of carriage will also maintain diversity in antibiotic resistance. We test these predictions in S. pneumoniae and find that the duration of carriage of a serotype is indeed positively correlated with the prevalence of resistance in that serotype. These findings suggest heterogeneity in duration of carriage is a partial explanation for the coexistence of sensitive and resistant strains and that factors determining bacterial duration of carriage will also affect the prevalence of resistance.

Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom—a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch coloring approach can incorporate a variable number of unique colors to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte–Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

The authors acknowledge financial support from the UKCRC Translational Infection Research (TIR) Initiative and the Medical Research Council (Grant number G1000803), with contributions to the grant from the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research on behalf of the Department of Health, and the Chief Scientist Office of the Scottish Government Health Directorate (to Professor Peacock); from Wellcome Trust grant number 098051 awarded to the Wellcome Trust Sanger Institute; and the NIHR Cambridge Biomedical Research Centre (to Professor Peacock). S.Y.C.T. is an Australian National Health and Medical Research Council Career Development Fellow (1065736). Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of nosocomial infection. Whole-genome sequencing of MRSA has been used to define phylogeny and transmission in well-resourced healthcare settings, yet the greatest burden of nosocomial infection occurs in resource-restricted settings where barriers to transmission are lower. Here, we study the flux and genetic diversity of MRSA on ward and individual patient levels in a hospital where transmission was common. We repeatedly screened all patients on two intensive care units for MRSA carriage over a 3-mo period. All MRSA belonged to multilocus sequence type 239 (ST 239). We defined the population structure and charted the spread of MRSA by sequencing 79 isolates from 46 patients and five members of staff, including the first MRSA-positive screen isolates and up to two repeat isolates where available. Phylogenetic analysis identified a flux of distinct ST 239 clades over time in each intensive care unit. In total, five main clades were identified, which varied in the carriage of plasmids encoding antiseptic and antimicrobial resistance determinants. Sequence data confirmed intra- and interwards transmission events and identified individual patients who were colonized by more than one clade. One patient on each unit was the source of numerous transmission events, and deep sampling of one of these cases demonstrated colonization with a "cloud" of related MRSA variants. The application of whole-genome sequencing and analysis provides novel insights into the transmission of MRSA in under-resourced healthcare settings and has relevance to wider global health. Publisher PDF

Next-generation sequencing has yet to be widely adopted for HIV. The difficulty of accurately reconstructing the consensus sequence of a quasispecies from reads (short fragments of DNA) in the presence of rapid between- and within-host evolution may have presented a barrier. In particular, mapping (aligning) reads to a reference sequence leads to biased loss of information; this bias can distort epidemiological and evolutionary conclusions.De novoassembly avoids this bias by effectively aligning the reads to themselves, producing a set of sequences called contigs. However contigs provide only a partial summary of the reads, misassembly may result in their having an incorrect structure, and no information is available at parts of the genome where contigs could not be assembled. To address these problems we developed the toolshiverto preprocess reads for quality and contamination, then map them to a reference tailored to the sample using corrected contigs supplemented with existing reference sequences. Run with two commands per sample, it can easily be used for large heterogeneous data sets. We useshiverto reconstruct the consensus sequence and minority variant information from paired-end short-read data produced with the Illumina platform, for 65 existing publicly available samples and 50 new samples. We show the systematic superiority of mapping toshiver’s constructed reference over mapping the same reads to the standard reference HXB2: an average of 29 bases per sample are called differently, of which 98.5% are supported by higher coverage. We also provide a practical guide to working with imperfect contigs.

Central and Eastern African HIV sequence data have been most critical in understanding the establishment and evolution of the global HIV pandemic. Here we report on the extent of publicly available HIV genetic sequence data in the Los Alamos National Laboratory Sequence Database sampled from 1959 to 2013 from six African countries: Uganda, Kenya, Tanzania, Burundi, the Democratic Republic of Congo, and Rwanda. We have summarized these data, including HIV subtypes, the years sampled, and the genomic regions sequenced. We also provide curated alignments for this important geographic area in five HIV genomic regions with substantial coverage.

HIV molecular epidemiology studies analyse viral pol gene sequences due to their availability, but whole genome sequencing allows to use other genes. We aimed to determine what gene(s) provide(s) the best approximation to the real phylogeny by analysing a simulated epidemic (created as part of the PANGEA_HIV project) with a known transmission tree. We sub-sampled a simulated dataset of 4662 sequences into different combinations of genes (gag-pol-env, gag-pol, gag, pol, env and partial pol) and sampling depths (100%, 60%, 20% and 5%), generating 100 replicates for each case. We built maximum-likelihood trees for each combination using RAxML (GTR + Γ), and compared their topologies to the corresponding true tree’s using CompareTree. The accuracy of the trees was significantly proportional to the length of the sequences used, with the gag-pol-env datasets showing the best performance and gag and partial pol sequences showing the worst. The lowest sampling depths (20% and 5%) greatly reduced the accuracy of tree reconstruction and showed high variability among replicates, especially when using the shortest gene datasets. In conclusion, using longer sequences derived from nearly whole genomes will improve the reliability of phylogenetic reconstruction. With low sample coverage, results can be highly variable, particularly when based on short sequences.

Evolutionary theory hypothesizes that intermediate virulence maximizes pathogen fitness as a result of a trade-off between virulence and transmission, but empirical evidence remains scarce. We bridge this gap using data from a large and long-standing HIV-1 prospective cohort, in Uganda. We use an epidemiological-evolutionary model parameterised with this data to derive evolutionary predictions based on analysis and detailed individual-based simulations. We robustly predict stabilising selection towards a low level of virulence, and rapid attenuation of the virus. Accordingly, set-point viral load, the most common measure of virulence, has declined in the last 20 years. Our model also predicts that subtype A is slowly outcompeting subtype D, with both subtypes becoming less virulent, as observed in the data. Reduction of set-point viral loads should have resulted in a 20% reduction in incidence, and a three years extension of untreated asymptomatic infection, increasing opportunities for timely treatment of infected individuals. DOI: http://dx.doi.org/10.7554/eLife.20492.001

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.

Seasonal influenza has considerable impact around the world, both economically and in mortality among risk groups, but there is considerable uncertainty as to the essential mechanisms and their parametrization. In this paper, we identify a number of characteristic features of influenza incidence time series in temperate regions, including ranges of annual attack rates and outbreak durations. By constraining the output of simple models to match these characteristic features, we investigate the role played by population heterogeneity, multiple strains, cross-immunity and the rate of strain evolution in the generation of incidence time series. Results indicate that an age-structured model with non-random mixing and co-circulating strains are both required to match observed time-series data. Our work gives estimates of the seasonal peak basic reproduction number, R 0 , in the range 1.6–3. Estimates of R 0 are strongly correlated with the timescale for waning of immunity to current circulating seasonal influenza strain, which we estimate is between 3 and 8 years. Seasonal variation in transmissibility is largely confined to 15–30% of its mean value. While population heterogeneity and cross-immunity are required mechanisms, the degree of heterogeneity and cross-immunity is not tightly constrained. We discuss our findings in the context of other work fitting to seasonal influenza data.

The evolution of antibiotic resistance in opportunistic pathogens such as Streptococcus pneumoniae , Escherichia coli or Staphylococcus aureus is a major public health problem, as infection with resistant strains leads to prolonged hospital stay and increased risk of death. Here, we develop a new model of the evolution of antibiotic resistance in a commensal bacterial population adapting to a heterogeneous host population composed of untreated and treated hosts, and structured in different host classes with different antibiotic use. Examples of host classes include age groups and geographic locations. Explicitly modelling the antibiotic treatment reveals that the emergence of a resistant strain is favoured by more frequent but shorter antibiotic courses, and by higher transmission rates. In addition, in a structured host population, localized transmission in host classes promotes both local adaptation of the bacterial population and the global maintenance of coexistence between sensitive and resistant strains. When transmission rates are heterogeneous across host classes, resistant strains evolve more readily in core groups of transmission. These findings have implications for the better management of antibiotic resistance: reducing the rate at which individuals receive antibiotics is more effective to reduce resistance than reducing the duration of treatment. Reducing the rate of treatment in a targeted class of the host population allows greater reduction in resistance, but determining which class to target is difficult in practice.

The rise of antimicrobial resistance in many pathogens presents a major challenge to the treatment and control of infectious diseases. Furthermore, the observation that drug-resistant strains have risen to substantial prevalence but have not replaced drug-susceptible strains despite continuing (and even growing) selective pressure by antimicrobial use presents an important problem for those who study the dynamics of infectious diseases. While simple competition models predict the exclusion of one strain in favour of whichever is ‘fitter’, or has a higher reproduction number, we argue that in the case of Streptococcus pneumoniae there has been persistent coexistence of drug-sensitive and drug-resistant strains, with neither approaching 100 per cent prevalence. We have previously proposed that models seeking to understand the origins of coexistence should not incorporate implicit mechanisms that build in stable coexistence ‘for free’. Here, we construct a series of such ‘structurally neutral’ models that incorporate various features of bacterial spread and host heterogeneity that have been proposed as mechanisms that may promote coexistence. We ask to what extent coexistence is a typical outcome in each. We find that while coexistence is possible in each of the models we consider, it is relatively rare, with two exceptions: (i) allowing simultaneous dual transmission of sensitive and resistant strains lets coexistence become a typical outcome, as does (ii) modelling each strain as competing more strongly with itself than with the other strain, i.e. self-immunity greater than cross-immunity. We conclude that while treatment and contact heterogeneity can promote coexistence to some extent, the in-host interactions between strains, particularly the interplay between coinfection, multiple infection and immunity, play a crucial role in the long-term population dynamics of pathogens with drug resistance.

Many of the studies on emerging epidemics (such as SARS and pandemic flu) use mass action models to estimate reproductive numbers and the needed control measures. In reality, transmission patterns are more complex due to the presence of various social networks. One level of complexity can be accommodated by considering a community of households. Our study of transmission dynamics in a community of households emphasizes five types of reproductive numbers for the epidemic spread: household-to-household reproductive number, leaky vaccine-associated reproductive numbers, perfect vaccine reproductive number, growth rate reproductive number, and the individual reproductive number. Each of those carries different information about the transmission dynamics and the required control measures, and often some of those can be estimated from the data while others cannot. Simulations have shown that under certain scenarios there is an ordering for those reproductive numbers. We have proven a number of ordering inequalities under general assumptions about the individual infectiousness profiles. Those inequalities allow, for instance, to estimate the needed vaccine coverage and other control measures without knowing the various transmission parameters in the models. Along the way, we have also shown that in choosing between increasing vaccine efficacy and increasing coverage levels by the same factor, preference should go to efficacy.

Recombining Resistance Homologous recombination is frequent in many bacteria, but few studies have addressed whether subpopulations within a species are more or less likely to undergo this process and whether it has consequences for their evolution. Taking a large data set from the pathogen Streptococcus pneumoniae , Hanage et al. (p. 1454 ) discovered a group of strains characterized by an anomalous sequence of housekeeping genes. This sequence appeared to have been horizontally acquired from other pneumococci and related species and was associated with resistance to all classes of antibiotics for which data are available. Thus, hyper-recombination (in contrast to hypermutation) is important in the evolution and spread of antibiotic resistance and may play a role in determining the emergence of species clusters and the phenotypes associated with them.

The Bacteria and Archaea are the most genetically diverse superkingdoms of life, and techniques for exploring that diversity are only just becoming widespread. Taxonomists classify these organisms into species in much the same way as they classify eukaryotes, but differences in their biology—including horizontal gene transfer between distantly related taxa and variable rates of homologous recombination—mean that we still do not understand what a bacterial species is. This is not merely a semantic question; evolutionary theory should be able to explain why species exist at all levels of the tree of life, and we need to be able to define species for practical applications in industry, agriculture, and medicine. Recent studies have emphasized the need to combine genetic diversity and distinct ecology in an attempt to define species in a coherent and convincing fashion. The resulting data may help to discriminate among the many theories of prokaryotic species that have been produced to date.

Key Points Mathematical analysis and modelling is an important part of infectious disease epidemiology. Application of mathematical models to disease surveillance data can be used to address both scientific hypotheses and disease-control policy questions.The link between the biology of an infectious disease, the process of transmission and the mathematics that are used to describe them is not always clear in published research. An understanding of this link is needed to critically interpret these publications and the policy recommendations and scientific conclusions that are contained within them.This Review describes the biology of the transmission process and how it can be represented mathematically. It shows how this representation leads to a mathematical model of infectious disease epidemics as a function of underlying disease natural history and ecology. The mathematical description of disease epidemics immediately leads to several useful results, including the expected size of an epidemic and the critical level that is needed for an intervention to achieve effective disease control.Statistical methods to fit mathematical models of disease surveillance data are outlined and the fundamental importance of the concept of likelihood is highlighted. The fit of mathematical models to surveillance data can provide estimates of key model parameters that determine a disease's natural history or the impact of an intervention, and are crucially dependent on the appropriate choice of mathematical model.The Review ends with four outstanding challenges in mathematical infectious disease epidemiology that are essential for progress in our understanding of the ecology and evolution of infectious diseases. This understanding could lead to improvements in disease control., The dynamics of infectious diseases are complex, so developing models that can capture key features of the spread of infection is important. Grassly and Fraser provide an introduction to the mathematical analysis and modelling of disease transmission, which, in addition to informing public health disease control measures, is also important for understanding pathogen evolution and ecology., Mathematical analysis and modelling is central to infectious disease epidemiology. Here, we provide an intuitive introduction to the process of disease transmission, how this stochastic process can be represented mathematically and how this mathematical representation can be used to analyse the emergent dynamics of observed epidemics. Progress in mathematical analysis and modelling is of fundamental importance to our growing understanding of pathogen evolution and ecology. The fit of mathematical models to surveillance data has informed both scientific research and health policy. This Review is illustrated throughout by such applications and ends with suggestions of open challenges in mathematical epidemiology.

The duration over which antibody responses persist following HPV vaccination is unknown. To estimate the longevity of responses induced by HPV-16 vaccination, two models were fitted to serum anti-HPV-16 levels measured during a 48-month study period. The first was a conventional model of antibody decay and the second was a modified model that accounts for long-lived immune memory. Using the antibody decay model, it was estimated that following administration of a three-dose regimen of HPV-16 vaccine in women aged 16-23 years, anti-HPV-16 levels will remain above those induced naturally by HPV-16 infection for 12 years, and above detectable levels for 32 years in 50% of vaccinees. With the modified model, which fitted the data better (p

Background The HIV-1 subtype B epidemic amongst men who have sex with men (MSM) is resurgent in many countries despite the widespread use of effective combination antiretroviral therapy (cART). In this combined mathematical and phylogenetic study of observational data, we aimed to find out the extent to which the resurgent epidemic is the result of newly introduced strains or of growth of already circulating strains. Methods and Findings As of November 2011, the ATHENA observational HIV cohort of all patients in care in the Netherlands since 1996 included HIV-1 subtype B polymerase sequences from 5,852 patients. Patients who were diagnosed between 1981 and 1995 were included in the cohort if they were still alive in 1996. The ten most similar sequences to each ATHENA sequence were selected from the Los Alamos HIV Sequence Database, and a phylogenetic tree was created of a total of 8,320 sequences. Large transmission clusters that included ≥10 ATHENA sequences were selected, with a local support value ≥ 0.9 and median pairwise patristic distance below the fifth percentile of distances in the whole tree. Time-varying reproduction numbers of the large MSM-majority clusters were estimated through mathematical modeling. We identified 106 large transmission clusters, including 3,061 (52%) ATHENA and 652 Los Alamos sequences. Half of the HIV sequences from MSM registered in the cohort in the Netherlands (2,128 of 4,288) were included in 91 large MSM-majority clusters. Strikingly, at least 54 (59%) of these 91 MSM-majority clusters were already circulating before 1996, when cART was introduced, and have persisted to the present. Overall, 1,226 (35%) of the 3,460 diagnoses among MSM since 1996 were found in these 54 long-standing clusters. The reproduction numbers of all large MSM-majority clusters were around the epidemic threshold value of one over the whole study period. A tendency towards higher numbers was visible in recent years, especially in the more recently introduced clusters. The mean age of MSM at diagnosis increased by 0.45 years/year within clusters, but new clusters appeared with lower mean age. Major strengths of this study are the high proportion of HIV-positive MSM with a sequence in this study and the combined application of phylogenetic and modeling approaches. Main limitations are the assumption that the sampled population is representative of the overall HIV-positive population and the assumption that the diagnosis interval distribution is similar between clusters. Conclusions The resurgent HIV epidemic amongst MSM in the Netherlands is driven by several large, persistent, self-sustaining, and, in many cases, growing sub-epidemics shifting towards new generations of MSM. Many of the sub-epidemics have been present since the early epidemic, to which new sub-epidemics are being added., Daniela Bezemer and colleagues investigate the extent to which the resurgent HIV epidemic in the Netherlands is the result of newly introduced strains, or of growth of already circulating strains., Editors' Summary Background Since the first recorded case of AIDS in 1981, the number of people infected with HIV, the virus that causes AIDS, has risen steadily. Now, three and a half decades later, about 35 million people (more than half of whom are women) are infected with HIV, the virus that causes AIDS. HIV is most often spread by having unprotected sex with an infected partner, and, globally, most sexual transmission of HIV occurs during heterosexual sex. Nevertheless, many new HIV infections still occur in men who have sex with men (MSM; homosexual, bisexual, and transgender men, and heterosexual men who sometimes have sex with men), and, in some countries, HIV/AIDS still predominantly affects the MSM community. In the US, for example, 78% of new HIV infections occurred among MSM in 2010 although MSM represent only 4% of the total population, and, in 2011, 54% of all people living with HIV were MSM. Indeed, despite HIV-positive individuals being diagnosed earlier these days and having access to effective combination antiretroviral therapy (cART), which both halts disease progression and reduces the risk of HIV transmission, the HIV epidemic among MSM is resurgent (growing again) in many Western countries. Why Was This Study Done? To control this resurgent epidemic, it is important to know as much as possible about HIV transmission among MSM so that effective prevention strategies can be designed. Here, the researchers use phylogenetic analysis and mathematical modeling to ask whether the introduction of new strains or the spread of already circulating strains is responsible for the resurgent HIV-1 subtype B epidemic occurring among MSM in the Netherlands. Viral phylogenetic analysis infers evolutionary relationships between viral strains by examining their genetic relatedness and can be used to identify HIV transmission clusters. HIV-1 viruses are classified into subtypes based on their genetic sequence and geographical distribution. HIV-1 subtype B is a common subtype that is found in west and central Europe, the Americas, and several other regions. What Did the Researchers Do and Find? The researchers built a phylogenetic tree for the HIV epidemic in MSM in the Netherlands by analyzing HIV-1 subtype B polymerase gene sequences found in 5,852 participants (73% of whom were MSM) in the ATHENA cohort, an observational cohort of all HIV-1-infected patients in care in the Netherlands since 1996 (when cART became available). Examination of this tree identified 106 large transmission clusters (groups of ten or more closely related subtype B HIV-1 strains). Half of the HIV-1 polymerase sequences from HIV-1-positive MSM registered in the ATHENA cohort in the Netherlands were included in 91 MSM-majority clusters: large transmission clusters in which more than half the related sequences originated from MSM. At least 54 of the MSM-majority clusters were circulating before 1996 and have persisted until the present day. Moreover, about a third of new HIV infections diagnosed among MSM since 1996 involve viruses included in these long-lived clusters. The researchers then used mathematical modeling to estimate that the effective reproduction number (the number of secondary infections per primary infection) for all the MSM-majority clusters was around one for the whole study period. Thus, these clusters were self-sustaining and not contracting. Notably, MSM-majority clusters (particularly the newer clusters) tended to have higher reproduction numbers in recent years. Moreover, although the average age at diagnosis within each of the MSM-majority clusters increased over the study period at a rate of 0.45 years/year, the average age at diagnosis was lower at initiation of new clusters and only increased by 0.28 years/year. What Do These Findings Mean? These findings suggest that several large, persistent, and self-sustaining sub-epidemics, many of which have been present since early in the AIDS epidemic, are driving the resurgent HIV epidemic among MSM in the Netherlands, despite the widespread availability of treatment, increasing rates of diagnosis, and earlier treatment initiation. Importantly, however, these findings also suggest that some sub-epidemics have emerged more recently and that some sub-epidemics, particularly the newer ones, are growing and may be preferentially affecting younger MSM. The accuracy of these findings may be limited by some aspects of the study. For example, the reproduction number estimates assume that the time from diagnosis of a case to the diagnoses of secondary cases is similar across clusters. Nevertheless, the new insights provided by this study should help guide the development of strategies to curb the resurgent HIV epidemic that is currently affecting MSM in the Netherlands and elsewhere. Additional Information This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001898. The US Centers for Disease Control and Prevention provides information on all aspects of HIV/AIDS, including information on HIV/AIDS among MSM (in English and Spanish) NAM/aidsmap provides basic information about HIV/AIDS and summaries of recent research findings, including information on HIV and MSM and personal stories from MSM living with HIV Information is available from Avert, an international AIDS charity on many aspects of HIV/AIDS, including MSM and HIV; Avert also provides personal stories about living with HIV/AIDS The World Health Organization provides information on all aspects of HIV/AIDS, including HIV/AIDS and MSM (in several languages) The UNAIDS Fast-Track Strategy to End the AIDS Epidemic by 2030 provides up-to-date information about the AIDS epidemic and efforts to halt it A 2011 World Bank Report The Global HIV Epidemics among Men Who Have Sex with Men is available A PLOS Computational Biology Topic Page (a review article that is a published copy of record of a dynamic version of an article in Wikipedia) about viral phylodynamics is available