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Transmission trees can be established through detailed contact histories, statistical or phylogenetic inference, or a combination of methods. Each approach has its limitations, and the extent to which they succeed in revealing a ‘true’ transmission history remains unclear. In this study, we compared the transmission trees obtained through contact tracing investigations and various inference methods to identify the contribution and value of each approach. We studied eighty-six sequenced cases reported in Guinea between March and November 2015. Contact tracing investigations classified these cases into eight independent transmission chains. We inferred the transmission history from the genetic sequences of the cases (phylogenetic approach), their onset date (epidemiological approach), and a combination of both (combined approach). The inferred transmission trees were then compared to those from the contact tracing investigations. Inference methods using individual data sources (i.e. the phylogenetic analysis and the epidemiological approach) were insufficiently informative to accurately reconstruct the transmission trees and the direction of transmission. The combined approach was able to identify a reduced pool of infectors for each case and highlight likely connections among chains classified as independent by the contact tracing investigations. Overall, the transmissions identified by the contact tracing investigations agreed with the evolutionary history of the viral genomes, even though some cases appeared to be misclassified. Therefore, collecting genetic sequences during outbreak is key to supplement the information contained in contact tracing investigations. Although none of the methods we used could identify one unique infector per case, the combined approach highlighted the added value of mixing epidemiological and genetic information to reconstruct who infected whom.

The Philippines has had a rapidly growing HIV epidemic with a shift in the prevalent subtype from B to CRF01_AE. However, the phylodynamic history of CRF01_AE in the Philippines has yet to be reconstructed. We conducted a descriptive retrospective study reconstructing the history of HIV-1 CRF01_AE transmissions in the Philippines through molecular epidemiology. Partial polymerase sequences (n= 1429) collected between 2008 and 2018 from 13 Philippine regions were collated from the RITM drug resistance genotyping database. Subsampling was performed on these Philippine and Los Alamos National Laboratory HIV international sequences followed by estimation of the time to the most recent common ancestor (tMRCA), effective reproductive number (Re), effective viral population size (Ne), relative migration rates and geographic spread of CRF01_AE with BEAST.ReandNewere compared between CRF01_AE and B. Most CRF01_AE sequences formed a single clade with a tMRCA of 1999 [95% HPD: 1996, 2001]. Exponential growth ofNewas observed from 1999 to 2013. TheRereached peaks of 3.71 [95% HPD: 1.71, 6.14] in 2009 and 2.87 [95% HPD: 1.78, 4.22] between 2012 and 2015. A transient decrease to 0.398 [95% HPD: 0.0105, 2.28] occurred between 2010 and 2012. The epidemic most likely started in Luzon in the National Capital Region, which then spread diffusely to the rest of the country. Both CRF01_AE and subtype B exhibited similar but unsynchronized patterns ofReover time. These results characterize the subtype-specific phylodynamic history of the CRF01_AE epidemic in the Philippines, which contextualizes and may inform past, present, and future public health measures toward controlling the HIV epidemic in the Philippines.

Small ruminant lentiviruses (SRLVs) cause chronic, persistent infections in populations of domestic sheep (Ovis aries) and goats (Capra hircus) worldwide. The vast majority of SRLV infections involve two genotypes (A and B) that spread in association with the emergence of global livestock trade. However, SRLVs have likely been present in Eurasian ruminant populations since at least the early Neolithic period. Here, we use phylogenetic and phylogeographic approaches to reconstruct the origin of pandemic SRLV strains and infer their historical pattern of global spread. We constructed an open computational resource (‘Lentivirus-GLUE’) via which an up-to-date database of published SRLV sequences, multiple sequence alignments (MSAs), and sequence-associated metadata can be maintained. We used data collated in Lentivirus-GLUE to perform a comprehensive phylogenetic investigation of global SRLV diversity. Phylogenies reconstructed from genome-length alignments reveal that the deep divisions in the SRLV phylogeny are consistent with an ancient split into Eastern (A-like) and Western (B-like) lineages as agricultural systems disseminated out of domestication centres during the Neolithic period. These findings are also consistent with historical and phylogeographic evidence linking the early 20th century emergence of SRLV-A to the international export of Central Asian Karakul sheep. Investigating the global diversity of SRLVs can help reveal how anthropogenic factors have impacted the ecology and evolution of livestock diseases. The open resources generated in our study can expedite these studies and can also serve more broadly to facilitate the use of genomic data in SRLV diagnostics and research.

BackgroundCOVID-19, caused by SARS-CoV-2, is one of the deadliest pandemics over the last 100 years. Sequencing is playing an important role in monitoring the evolution of the virus, including the detection of new viral variants. This study describes the genomic epidemiology of SARS-CoV-2 infections in The Gambia.MethodsNasopharyngeal and/or oropharyngeal swabs collected from suspected cases and travellers were tested for SARS-CoV-2 using standard RT-PCR methods. SARS-CoV-2 positive samples were sequenced following standard library preparation and sequencing protocols. Bioinformatic analysis was done using ARTIC pipelines and lineages assigned using Pangolin.FindingsBetween March 2020 to January 2022, there were almost 12,000 SARS-CoV-2 confirmed cases distributed into four waves, each of them lasting between 4 weeks and 4 months, with more cases during the rainy seasons (July-October). As shown by the 1643 sequenced samples, each wave occurred after new viral variants and/or lineages were introduced in The Gambia, generally those already established in Europe and/or in other African countries. Local transmission was higher during the first and third wave, with mostly B.1.416/Senegal/Gambian lineage and AY.34.1/Delta subtype, respectively. The second wave was driven by two variants, namely Alpha and Eta and B.1.1.420 lineage. The Omicron/fourth wave was the shortest.InterpretationEfficient surveillance, including strengthening entry points and screening asymptomatic individuals especially during the rainy seasons would be important to promptly detect and control future waves in The Gambia and the subregion.FundingMedical Research Unit The Gambia at LSHTM, UK Research and Innovation funding (grant reference MC_PC_19084), MRC/UKRI MC_PC_19084 and World Health Organisation.

Zika virus (ZIKV) has caused large, brief outbreaks in isolated populations, however ZIKV can also persist at low levels over multiple years. The reasons for these diverse transmission dynamics remain poorly understood. In Fiji, which has experienced multiple large single-season dengue epidemics, there was evidence of multi-year transmission of ZIKV between 2013 and 2017. To identify factors that could explain these differences in dynamics between closely related mosquito-borne flaviviruses, we jointly fit a transmission dynamic model to surveillance, serological and molecular data. We estimate that the observed dynamics of ZIKV were the result of two key factors: strong seasonal effects, which created an ecologically optimal time of year for outbreaks; and introduction of ZIKV after this optimal time, which allowed ZIKV transmission to persist over multiple seasons. The ability to jointly fit to multiple data sources could help identify a similar range of possible outbreak dynamics in other settings., Dengue and Zika virus are closely related flaviviruses but can have contrasting transmission dynamics in the same populations. Here, the authors use a model combining serological, surveillance and viral sequence data to explain differences in transmission dynamics in Fiji.

Background: The ability of SARS-CoV-2 vaccines to protect against infection and onward transmission determines whether immunisation can control global circulation. We estimated the effectiveness of Pfizer-BioNTech mRNA vaccine (BNT162b2) and Oxford AstraZeneca adenovirus vector vaccine (ChAdOx1) vaccines against acquisition and transmission of the Alpha and Delta variants in a prospective household study in England. Methods: Households were recruited based on adult purported index cases testing positive after reverse transcription-quantitative (RT-q)PCR testing of oral-nasal swabs. Purported index cases and their household contacts took oral-nasal swabs on days 1, 3 and 7 after enrolment and a subset of the PCR-positive swabs underwent genomic sequencing conducted on a subset. We used Bayesian logistic regression to infer vaccine effectiveness against acquisition and transmission, adjusted for age, vaccination history and variant. Results: Between 2 February 2021 and 10 September 2021, 213 index cases and 312 contacts were followed up. After excluding households lacking genomic proximity (N=2) or with unlikely serial intervals (N=16), 195 households with 278 contacts remained, of whom 113 (41%) became PCR positive. Delta lineages had 1.53 times the risk (95% Credible Interval: 1.04 – 2.20) of transmission than Alpha; contacts older than 18 years old were 1.48 (1.20 – 1.91) and 1.02 (0.93 – 1.16) times more likely to acquire an Alpha or Delta infection than children. Effectiveness of two doses of BNT162b2 against transmission of Delta was 36% (-1%, 66%) and 49% (18%, 73%) for ChAdOx1, similar to their effectiveness for Alpha. Protection against infection with Alpha was higher than for Delta, 69% (9%, 95%) vs. 18% (-11%, 59%), respectively, for BNT162b2 and 24% (-41%, 72%) vs. 9% (-15%, 42%), respectively, for ChAdOx1. Conclusions: BNT162b2 and ChAdOx1 reduce transmission of the Delta variant from breakthrough infections in the household setting, although their protection against infection within this setting is low.

Background: National immunisation programmes globally are at risk of suspension due to the severe health system constraints and physical distancing measures in place to mitigate the ongoing COVID-19 pandemic. We aimed to compare the health benefits of sustaining routine childhood immunisation in Africa with the risk of acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through visiting routine vaccination service delivery points. Methods: We considered a high-impact scenario and a low-impact scenario to approximate the child deaths that could be caused by immunisation coverage reductions during COVID-19 outbreaks. In the high-impact scenario, we used previously reported country-specific child mortality impact estimates of childhood immunisation for diphtheria, tetanus, pertussis, hepatitis B, Haemophilus influenzae type b, Streptococcus pneumoniae, rotavirus, measles, meningitis A, rubella, and yellow fever to approximate the future deaths averted before 5 years of age by routine childhood vaccination during a 6-month COVID-19 risk period without catch-up campaigns. In the low-impact scenario, we approximated the health benefits of sustaining routine childhood immunisation on only the child deaths averted from measles outbreaks during the COVID-19 risk period. We assumed that contact-reducing interventions flattened the outbreak curve during the COVID-19 risk period, that 60% of the population will have been infected by the end of that period, that children can be infected by either vaccinators or during transport, and that upon child infection the whole household will be infected. Country-specific household age structure estimates and age-dependent infection-fatality rates were applied to calculate the number of deaths attributable to the vaccination clinic visits. We present benefit–risk ratios for routine childhood immunisation, with 95% uncertainty intervals (UIs) from a probabilistic sensitivity analysis. Findings: In the high-impact scenario, for every one excess COVID-19 death attributable to SARS-CoV-2 infections acquired during routine vaccination clinic visits, 84 (95% UI 14–267) deaths in children could be prevented by sustaining routine childhood immunisation in Africa. The benefit–risk ratio for the vaccinated children is 85 000 (4900–546 000), for their siblings (60 years) is 96 (14–307). In the low-impact scenario that approximates the health benefits to only the child deaths averted from measles outbreaks, the benefit–risk ratio to the households of vaccinated children is 3 (0–10); if the risk to only the vaccinated children is considered, the benefit–risk ratio is 3000 (182–21 000). Interpretation: The deaths prevented by sustaining routine childhood immunisation in Africa outweigh the excess risk of COVID-19 deaths associated with vaccination clinic visits, especially for the vaccinated children. Routine childhood immunisation should be sustained in Africa as much as possible, while considering other factors such as logistical constraints, staff shortages, and reallocation of resources during the COVID-19 pandemic. Funding: Gavi, the Vaccine Alliance; Bill & Melinda Gates Foundation.

Summary Many infection prevention and control (IPC) interventions have been adopted by hospitals to limit nosocomial transmission of SARS-CoV-2. The aim of this systematic review is to identify evidence on the effectiveness of these interventions. We conducted a literature search of five databases (OVID MEDLINE, Embase, CENTRAL, COVID-19 Portfolio(pre-print), Web of Science). SWIFT ActiveScreener software was used to screen English titles and abstracts published between 1st January 2020 and 6th April 2021. Intervention studies, defined by Cochrane Effective Practice and Organisation of Care, that evaluated IPC interventions with an outcome of SARS-CoV-2 infection in either patients or healthcare workers were included. Personal protective equipment (PPE) was excluded as this intervention had been previously reviewed. Risks of bias were assessed using the Cochrane tool for randomised trials (RoB2) and non-randomized studies of interventions (ROBINS-I). From 23,156 screened articles, we identified seven articles that met the inclusion criteria, all of which evaluated interventions to prevent infections in healthcare workers and the majority of which were focused on effectiveness of prophylaxes. Due to heterogeneity in interventions, we did not conduct a meta-analysis. All agents used for prophylaxes have little to no evidence of effectiveness against SARS-CoV-2 infections. We did not find any studies evaluating the effectiveness of interventions including but not limited to screening, isolation and improved ventilation. There is limited evidence from interventional studies, excluding PPE, evaluating IPC measures for SARS-CoV-2. This review calls for urgent action to implement such studies to inform policies to protect our most vulnerable populations and healthcare workers.

International audience; Drug resistance mutations appear in HIV under treatment pressure. Resistant variants can be transmitted to treatmentnaive individuals, which can lead to rapid virological failure and can limit treatment options. Consequently, quantifying the prevalence, emergence and transmission of drug resistance is critical to effectively treating patients and to shape health policies. We review recent bioinformatics developments and in particular describe: (1) the machine learning approaches intended to predict and explain the level of resistance of HIV variants from their sequence data; (2) the phylogenetic methods used to survey the emergence and dynamics of resistant HIV transmission clusters; (3) the impact of deep sequencing in studying within-host and between-host genetic diversity of HIV variants, notably regarding minority resistant variants.

BACKGROUND: The COVID-19 pandemic has resulted in unprecedented disruption to society, which indirectly affects infectious disease dynamics. We aimed to assess the effects of COVID-19-related disruption on dengue, a major expanding acute public health threat, in southeast Asia and Latin America.METHODS: We assembled data on monthly dengue incidence from WHO weekly reports, climatic data from ERA5, and population variables from WorldPop for 23 countries between January, 2014 and December, 2019 and fit a Bayesian regression model to explain and predict seasonal and multi-year dengue cycles. We compared model predictions with reported dengue data January to December, 2020, and assessed if deviations from projected incidence since March, 2020 are associated with specific public health and social measures (from the Oxford Coronavirus Government Response Tracer database) or human movement behaviours (as measured by Google mobility reports).FINDINGS: We found a consistent, prolonged decline in dengue incidence across many dengue-endemic regions that began in March, 2020 (2·28 million cases in 2020 vs 4·08 million cases in 2019; a 44·1% decrease). We found a strong association between COVID-19-related disruption (as measured independently by public health and social measures and human movement behaviours) and reduced dengue risk, even after taking into account other drivers of dengue cycles including climatic and host immunity (relative risk 0·01-0·17, pINTERPRETATION: In most countries, COVID-19-related disruption led to historically low dengue incidence in 2020. Continuous monitoring of dengue incidence as COVID-19-related restrictions are relaxed will be important and could give new insights into transmission processes and intervention options.FUNDING: National Key Research and Development Program of China and the Medical Research Council.

Genetic diversity of HIV-2 groups A and B has not yet been fully described, especially in a few Western Africa countries such as Ivory-Coast or Mali. We collected 444 pol, 152 vif, 129 env, and 74 LTR sequences from patients of the French ANRS CO5 HIV-2 cohort completed by 221 pol, 18 vif, 377 env, and 63 LTR unique sequences from public databases. We performed phylogenetic reconstructions and revealed two distinct lineages within HIV-2 group A, herein called A1 and A2, presenting non-negligible genetic distances and distinct geographic distributions as A1 is related to coastal Western African countries and A2 to inland Western countries. Estimated early diversification times for groups A and B in human populations were 1940 [95% higher probability densitiy: 1935–53] and 1961 [1952–70]. A1 experienced an early diversification in 1942 [1937–58] with two distinct early epidemics in Guinea-Bissau or Senegal, raising the possibility of group A emergence in those countries from an initial introduction from Ivory-Coast to Senegal, two former French colonies. Changes in effective population sizes over time revealed that A1 exponentially grew concomitantly to Guinea-Bissau independence war, but both A2 and B lineages experienced a latter growth, starting during the 80s economic crisis. This large HIV-2 genetic analysis provides the existence of two distinct subtypes within group A and new data about HIV-2 early spreading patterns and recent epidemiologic evolution for which data are scarce outside Guinea-Bissau.

Background Targeting interventions to areas that have recently experienced cases of disease is one strategy to contain outbreaks of infectious disease. Such case-area targeted interventions (CATI) have become an increasingly popular approach for dengue control but there is little evidence to suggest how precisely targeted or how recent cases need to be, to mount an effective response. The growing interest in the development of prophylactic and therapeutic drugs for dengue has also given new relevance for CATI strategies to interrupt transmission or deliver early treatment. Methods/Principal findings Here we develop a patch-based mathematical model of spatial dengue spread and fit it to spatiotemporal datasets from Singapore. Simulations from this model suggest CATI strategies could be effective, particularly if used in lower density areas. To maximise effectiveness, increasing the size of the radius around an index case should be prioritised even if it results in delays in the intervention being applied. This is partially because large intervention radii ensure individuals receive multiple and regular rounds of drug dosing or vector control, and thus boost overall coverage. Given equivalent efficacy, CATIs using prophylactic drugs are predicted to be more effective than adult mosquito-killing vector control methods and may even offer the possibility of interrupting individual chains of transmission if rapidly deployed. CATI strategies quickly lose their effectiveness if baseline transmission increases or case detection rates fall. Conclusions/Significance These results suggest CATI strategies can play an important role in dengue control but are likely to be most relevant for low transmission areas where high coverage of other non-reactive interventions already exists. Controlled field trials are needed to assess the field efficacy and practical constraints of large operational CATI strategies., Author summary In resource limited settings there is a pressing need for more efficient, more targeted ways of controlling transmission and preventing outbreaks. One option is to use case-area targeted interventions (CATI) that are focussed on areas that have recently reported disease cases. The effectiveness of such CATI strategies is highly dependent on how the disease spreads. Despite CATI strategies being widely used to control the vector-transmitted disease dengue, little evidence underpins its effectiveness. In this analysis we formulate a mathematical model designed to test the effectiveness of CATI strategies for dengue control in Singapore- a best case test scenario for the approach. Simulation from this model suggested CATI are likely to be effective for dengue, but need to have large (250m+) radii around index cases and may not be suitable in higher transmission areas. These results, when combined with limited field evidence of efficacy, suggest that CATI strategies are unlikely to be universally applicable dengue control tools. Only once high coverage with other (non-reactive) interventions has been achieved and comprehensive rapid disease surveillance has been established are CATI strategies likely to become efficient methods of disease control.

Structured abstractObjectivesTo characterise within-hospital SARS-CoV-2 transmission across two waves of the COVID-19 pandemic.DesignA retrospective Bayesian modelling study to reconstruct transmission chains amongst 2181 patients and healthcare workers using combined viral genomic and epidemiological data.SettingA large UK NHS Trust with over 1400 beds and employing approximately 17,000 staff.Participants780 patients and 522 staff testing SARS-CoV-2 positive between 1st March 2020 and 25th July 2020 (Wave 1); and 580 patients and 299 staff testing SARS-CoV-2 positive between 30th November 2020 and 24th January 2021 (Wave 2).Main outcome measuresTransmission pairs including who-infected-whom; location of transmission events in hospital; number of secondary cases from each individual, including differences in onward transmission from community and hospital onset patient cases.ResultsStaff-to-staff transmission was estimated to be the most frequent transmission type during Wave 1 (31.6% of observed hospital-acquired infections; 95% CI 26.9 to 35.8%), decreasing to 12.9% (95% CI 9.5 to 15.9%) in Wave 2. Patient-to-patient transmissions increased from 27.1% in Wave 1 (95% CI 23.3 to 31.4%) to 52.1% (95% CI 48.0 to 57.1%) in Wave 2, to become the predominant transmission type. Over 50% of hospital-acquired infections were concentrated in 8/120 locations in Wave 1 and 10/93 locations in Wave 2. Approximately 40% to 50% of hospital-onset patient cases resulted in onward transmission compared to less than 4% of definite community-acquired cases.ConclusionsPrevention and control measures that evolved during the COVID-19 pandemic may have had a significant impact on reducing infections between healthcare workers, but were insufficient during the second wave to prevent a high number of patient-to-patient transmissions. As hospital-acquired cases appeared to drive most onward transmissions, more frequent and rapid identification and isolation of these cases will be required to break hospital transmission chains in subsequent pandemic waves.

SummaryBackgroundHIV-1 infections initiated by multiple founder variants are characterised by a higher viral load and a worse clinical prognosis, yet little is known about the routes of exposure through which transmission of multiple founder variants is most likely.MethodsWe conducted a systematic review of studies that estimated founder variant multiplicity in HIV-1 infection, searching MEDLINE, EMBASE and Global Health databases for papers published between 1st January 1990 and 14th September 2020 (PROSPERO study CRD42020202672). Leveraging individual patient estimates from these studies, we performed a logistic meta-regression to estimate the probability that an HIV infection is initiated by multiple founder variants. We calculated a pooled estimate using a random effects model, subsequently stratifying this estimate across nine transmission routes in a univariable analysis. We then extended our model to adjust for different study methods in a multivariable analysis, recalculating estimates across the nine transmission routes.FindingsWe included 70 publications in our analysis, comprising 1657 individual patients. Our pooled estimate of the probability that an infection is initiated by multiple founder variants was 0·25 (95% CI: 0·21-0·29), with moderate heterogeneity (Q = 132 · 3, p < 0 · 001, I2 = 64 · 2%). Our multivariable analysis uncovered differences in the probability of multiple variant infection by transmission route. Relative to a baseline of male-to-female transmission, the predicted probability for female-to-male multiple variant transmission was significantly lower at 0·13 (95% CI: 0·08-0·20), while the probabilities for people-who-inject-drugs (PWID) and men-who-have-sex-with-men (MSM) transmissions were significantly higher at 0·37 (0·24-0·53) and 0·30 (0·33-0·40), respectively. There was no significant difference in the probability of multiple variant transmission between male-to-female transmission (0·21 (0·14-0·31)), post-partum mother-to-child (0·18 (0·03-0·57)), pre-partum mother-to-child (0·17 (0·08-0·33)), intrapartum mother-to-child (0·27 (0·14-0·40)).InterpretationWe identified PWID and MSM transmissions are significantly more likely to result in an infection initiated by multiple founder variants, whilst female-to-male infections are significantly less likely. Quantifying how the routes of HIV infection impact the transmission of multiple variants allows us to better understand how the evolution and epidemiology of HIV-1 determine clinical outcomes.FundingThis study was supported by the MRC Precision Medicine Doctoral Training Programme (ref: 2259239) and an ERC Starting Grant awarded to KEA (award number 757688). The funding sources played no role in study design, data collection, data analysis, data interpretation, or writing of the report.Panel: Research in contextEvidence before this studyMost HIV-1 infections are initiated by a single, genetically homogeneous founder variant. Infections initiated by multiple founders, however, are associated with a significantly faster decline of CD4+ T cells in untreated individuals, ultimately leading to an earlier onset of AIDS. Through our systematic search of MEDLINE, EMBASE and Global Health databases, we identified 82 studies that classify the founder variant multiplicity of early HIV infections. As these studies vary in the methodology used to calculate the number of founder variants, it is difficult to evaluate the multiplicity of founder variants across routes of exposure.Added value of this studyWe estimated the probability that an HIV infection is initiated by multiple founder variants across exposure routes, leveraging individual patient data from 70 of the identified studies. Our multivariable meta-regression adjusted for heterogeneity across study methodology and uncovered differences in the probability that an infection is initiated by multiple founder variants by exposure route. While overall, we estimated that 25% of infections are initiated by multiple founder variants, our analysis found that this probability for female-to-male transmission is significantly lower than for male-to-female transmission. By contrast, this probability was significantly higher among people-who-inject-drugs (PWID) and men-who-have-sex-with-men (MSM). There was no difference in the probability of multiple founder variant transmission for mother-to-child transmission when compared with male-to-female sexual transmission.Implications of all the available evidenceBecause HIV-1 infections initiated by multiple founders are associated with a poorer prognosis, determining whether the route of exposure affects the probability with which infections are initiated by multiple variants facilitates an improved understanding of how the evolution and epidemiology of HIV-1 determine clinical progression. Our results identify that PWID and MSM transmissions are significantly more likely to result in an infection initiated by multiple founder variants compared to male-to-female. This reiterates the need for focussed public health programmes that reduce the burden of HIV-1 in these risk groups.

Inferring the transmission direction between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. Phylogenetic ancestral state reconstruction approaches infer the transmission direction by identifying the individual in whom the most recent common ancestor of the virus populations originated. However, these methods vary in their accuracy but it is unclear why. To evaluate the performance of phylogenetic ancestral state reconstruction, we inferred the transmission direction for 112 HIV transmission pairs where transmission direction was known and detailed additional information was available. We then fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic and phylogenetic factors influenced the outcome of the inference. We repeated the analysis under real-life conditions with only routinely-collected data. We found that the inference of transmission direction depends principally on the topology class and branch length characteristics of the phylogeny. Under real-life conditions, the probability of identifying the correct transmission direction increases from 52%—when a monophyletic-monophyletic or paraphyletic-polyphyletic tree topology is observed, when the sample size in both partners is small and when the tip closest to the root does not agree with the state at the root—to 93% when a paraphyletic-monophyletic topology is observed, when the sample size is large and when the tip closest to the root agrees with root state. Our results suggest that discordance between previous studies in inferring the transmission direction can be explained by differences in key phylogenetic properties that arise due to different evolutionary, epidemiological and sampling processes.Significance StatementIdentifying the direction of infectious disease transmission between individuals provides unparalleled power to understand infectious disease epidemiology. With epidemiological and clinical information typically unable to distinguish the direction, phylogenetic analysis of pathogen sequence data is an alternative approach. However, when these phylogenetic methods have been implemented, their accuracy is highly variable, and the reasons for this discordance is unknown. Here we analyse sequence data from over 100 pairs of individuals for whom both the direction of transmission of HIV is known and detailed epidemiological and sampling information is available. We find that easily quantifiable phylogenetic characteristics discriminate whether a phylogenetically-inferred transmission direction is correct. Our analysis highlights that phylogenetic approaches are unsuitable for individual-level analysis such as forensic investigations.

International audience; A unique outbreak of Ross River virus (RRV) infection was reported in Fiji in 1979. In 2013, RRV seroprevalence among residents was 46.5% (362/778). Of the residents who were seronegative in 2013 and retested in 2015, 10.9% (21/192) had seroconverted to RRV, suggesting ongoing endemic circulation of RRV in Fiji.

International audience; Zika and chikungunya viruses were first detected in Fiji in 2015. Examining surveillance and phylogenetic and serologic data, we found evidence of low-level transmission of Zika and chikungunya viruses during 2013-2017, in contrast to the major outbreaks caused by closely related virus strains in other Pacific Island countries.

Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs.We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs.When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, we did not find any significant signal of epistasis, beyond the standard resistance scheme associating major DRMs to auxiliary mutations.Author summaryAlmost all drugs to treat HIV target the Reverse Transcriptase (RT) and Drug resistance mutations (DRMs) appear in HIV under treatment pressure. Resistant strains can be transmitted and limit treatment options at the population level. Classically, multiple statistical testing is used to find DRMs, by comparing virus sequences of treated and naive populations. However, with this method, each mutation is considered individually and we cannot hope to reveal any interaction (epistasis) between them. Here, we used machine learning to discover new DRMs and study potential epistasis effects. We applied this approach to a very large UK dataset comprising≈55, 000 RT sequences. Results robustness was checked on different UK and African datasets.Six new mutations associated to resistance were found. All six have a low genetic barrier and show high correlations with known DRMs. Moreover, all these mutations are close to either the active site or the regulatory binding pocket of RT. Thus, they are good candidates for further wet experiments to establish their role in drug resistance. Importantly, our results indicate that epistasis seems to be limited to the classical scheme where primary DRMs confer resistance and associated mutations modulate the strength of the resistance and/or compensate for the fitness cost induced by DRMs.

Background: The isolation of symptomatic cases and tracing of contacts has been used as an early COVID-19 containment measure in many countries, with additional physical distancing measures also introduced as outbreaks have grown. To maintain control of infection while also reducing disruption to populations, there is a need to understand what combination of measures—including novel digital tracing approaches and less intensive physical distancing—might be required to reduce transmission. We aimed to estimate the reduction in transmission under different control measures across settings and how many contacts would be quarantined per day in different strategies for a given level of symptomatic case incidence. Methods: For this mathematical modelling study, we used a model of individual-level transmission stratified by setting (household, work, school, or other) based on BBC Pandemic data from 40 162 UK participants. We simulated the effect of a range of different testing, isolation, tracing, and physical distancing scenarios. Under optimistic but plausible assumptions, we estimated reduction in the effective reproduction number and the number of contacts that would be newly quarantined each day under different strategies. Results: We estimated that combined isolation and tracing strategies would reduce transmission more than mass testing or self-isolation alone: mean transmission reduction of 2% for mass random testing of 5% of the population each week, 29% for self-isolation alone of symptomatic cases within the household, 35% for self-isolation alone outside the household, 37% for self-isolation plus household quarantine, 64% for self-isolation and household quarantine with the addition of manual contact tracing of all contacts, 57% with the addition of manual tracing of acquaintances only, and 47% with the addition of app-based tracing only. If limits were placed on gatherings outside of home, school, or work, then manual contact tracing of acquaintances alone could have an effect on transmission reduction similar to that of detailed contact tracing. In a scenario where 1000 new symptomatic cases that met the definition to trigger contact tracing occurred per day, we estimated that, in most contact tracing strategies, 15 000–41 000 contacts would be newly quarantined each day. Interpretation: Consistent with previous modelling studies and country-specific COVID-19 responses to date, our analysis estimated that a high proportion of cases would need to self-isolate and a high proportion of their contacts to be successfully traced to ensure an effective reproduction number lower than 1 in the absence of other measures. If combined with moderate physical distancing measures, self-isolation and contact tracing would be more likely to achieve control of severe acute respiratory syndrome coronavirus 2 transmission. Funding: Wellcome Trust, UK Engineering and Physical Sciences Research Council, European Commission, Royal Society, Medical Research Council.

Background: \ud The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk.\ud \ud Methods: \ud We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies.\ud \ud Findings: \ud We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from 66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from

Sourcing HIV-1 infection HIV-1 has a multitude of strain variants, but sexual transmission of HIV-1 is assumed to result from productive infection by only one virus particle. Knowing the genetics of the virus strains that are transmitted could be crucial for developing successful vaccine strategies. Using epidemiological and genetic data from 112 pairs of sexual partners, Villabona-Arenas et al. found that individuals with acute infections are more likely to transmit multiple founder virus strains. In a phylodynamic approach that integrated phylogenetic analysis of sequence data with simulation of a transmission chain, the authors showed that multiple variant transmission is doubled during the first 3 months of infection irrespective of whether transmission was heterosexual or by men who have sex with men. Science , this issue p. 103

Antiretroviral treatment (ART) has provided substantial benefits for HIV-1-infected patients and has reduced incidence in areas with high uptake since its introduction in the late 1980s. As ART has led to shifts in the worldwide epidemiology of HIV-1, it may also have the potential to cause concomitant selective pressure on the virus population. Evidence for changes in HIV-1 virulence since the introduction of ART appears to be inconsistent. As well as reviewing both empirical and theoretical studies on the likely impact of ART on HIV-1 virulence, we developed a mathematical framework to evaluate the likely impact of ART on virulence selection under the widespread treatment programs and the future impact of recent test-and-treat recommendations. By quantifying both the relationship between virulence changes with the transmissibility through disease progression and the speed of diagnosis and treatment, we reconcile observational studies on virulence changes with the mathematical model predictions. On adoption of new test-and-treat programs—synonymous with early detection and immediate treatment—it is likely that increased virulence will be observed. Our findings highlight the potential public health consequences of mass treatment and the ensuing requirement for greater access and adherence to nullify the public health effect of these virulence changes.

Transmission trees can be established through detailed contact histories, statistical inference, phylogenetic inference, or a combination of methods. Each method has its limitations, and the extent to which they succeed in revealing a ‘true’ transmission history remains unclear. Moreover, the net value of pathogen sequencing in transmission tree reconstruction is yet to be assessed. We explored the accuracy and sensitivity to biases of a range of methods for transmission chain inference. We studied eight transmission chains determined by contact tracing, each one having more than a third of its cases sequenced (87 samples over 199 cases in total). We compared three inference methods on the selected transmission chains: (i) phylogenetic inference: the Ebola virus (EBOV) sequences derived from patients were mapped onto a dated EBOV phylogeny tree including 398 EBOV sequences sampled in Guinea between March 2014 and October 2015; (ii) statistical inference: we used the maximum likelihood framework developed by Wallinga and Teunis to infer the most likely transmitter-recipient relationships from the onset dates; (iii) combined method: we inferred probabilistic transmission events using both pathogen sequences and collection dates with the R package Outbreaker2. The cases coming from each transmission chain were mostly clustered together in the phylogenetic tree. The few misclassified cases were most likely allocated to the wrong chains of transmission because of the timing of their symptom onsets. Probabilistic transmission tree using only onset dates broadly matched the contact tracing data, but multiple potential infectors were identified for each case. The combined method showed that an a priori knowledge of the number of independent imports had an important impact on the outcome. Although cases were allocated to the correct transmission chains, discrepancies were found in identifying direct case linkage and transmission generations within a chain. Phylogenetic, epidemiological, and combined approaches for transmission chain reconstructions globally concurred in their output. Sequence data proved useful (if not necessary) to place the sampled cases in a wider context, identify transmission clusters, and misclassified cases when epidemiological chains are inferred from date of symptom onset only, and to identify links between supposedly independent chains of transmission.

Zika virus (ZIKV) has been circulating in the South Pacific since 2007, and transmission in Fiji was first confirmed in 2015. To better understand the history and transmission dynamics of ZIKV in Fiji, we combined a transmission dynamic model with serological and surveillance data from Central Division, Fiji. A longitudinal population representative of seroepidemiological data were available from participants sampled in 2013, 2015, and 2017. In addition, ZIKV case reports were available from 2015 and 2016. Using a Bayesian approach, we fitted a transmission dynamic model with a seasonally varying transmission to these data. We also estimated the virus introduction date, given the effect this has on transmission dynamics as it interacts with the observed seasonal pattern of transmission. We found evidence that the virus was introduced in October 2013 (95% credible interval: April 2013–April 2014) and that the strong seasonal transmission pattern meant the virus persisted for several years with multiple waves of infection in consecutive years. It is important to corroborate this evidence against other work done in the same area. A phylogenetic analysis was performed on 5 ZIKV strains obtained from Fiji in 2015 and 2016, which were aligned with 33 E gene sequences from the Pacific, Americas, and Africa. This analysis showed evidence of virus persistence over multiple years in Central Division, Fiji. The estimated most recent common ancestor of the group isolated from Central Division was November 2013 (95% credible interval: March 2013–July 2015). Our modeling estimate is consistent with these results despite the very different methods being used. The availability of detailed case and serology data in an island outbreak setting, combined with mathematical models, presented a unique opportunity to gain crucial insights into these infections. Our analysis provides evidence that seasonal variation in transmission, combined with other co-circulating flaviviruses, means the timing of ZIKV introduction can have a major impact on outbreak transmission dynamics.

Dengue is a major health burden, but it can be challenging to examine transmission and evaluate control measures because outbreaks depend on multiple factors, including human population structure, prior immunity and climate. We combined population-representative paired sera collected before and after the 2013/14 dengue-3 outbreak in Fiji with surveillance data to determine how such factors influence transmission and control in island settings. Our results suggested the 10–19 year-old age group had the highest risk of infection, but we did not find strong evidence that other demographic or environmental risk factors were linked to seroconversion. A mathematical model jointly fitted to surveillance and serological data suggested that herd immunity and seasonally varying transmission could not explain observed dynamics. However, the model showed evidence of an additional reduction in transmission coinciding with a vector clean-up campaign, which may have contributed to the decline in cases in the later stages of the outbreak., eLife digest Dengue fever – a disease spread by mosquitos – causes large outbreaks in Asia and the South Pacific islands. Health agencies often try to reduce the spread of the disease by removing mosquito breeding grounds, like old tires and containers that may hold standing water. But it can be difficult to tell whether these preventive measures work because dengue transmission depends on many factors, including the weather and how many people had developed immunity because of previous infections. A common way to study patterns of infection and immunity is to collect blood samples from a subset of the population before and after an outbreak. Unfortunately, large dengue outbreaks occur sporadically on islands, making it hard to set up a study like this ahead of an outbreak. During 2013 and 2014, there was a major dengue outbreak in Fiji, with over 25,000 suspected cases reported. In response, the government introduced a nationwide mosquito clean-up campaign. As luck would have it, a group of researchers had collected blood samples immediately before the outbreak for an unrelated study of typhoid fever and leptospirosis. Now, Kucharski et al. – who include the researchers who collected those pre-outbreak blood samples – show that the clean-up campaign coincided with a reduction in transmission of the disease. Participants whose blood was collected before the dengue outbreak were invited to provide another blood sample after the dengue outbreak. This allowed Kucharski et al. to identify individuals who had already developed immunity to dengue before the outbreak and those who were likely infected during the outbreak. Comparing blood samples taken before and after the outbreak revealed that children and teenagers between the ages of 10 and 19 had the greatest risk of infection during the outbreak. No other demographic or environmental factors were strongly linked to the likelihood of infection. Computer models using the data also showed that the clean-up efforts could explain the reduced dengue transmission during the outbreak. These findings suggest that studying immunity against dengue can lead to a better understanding of disease transmission. This may help health agencies to gauge the effects of efforts to control this disease, and possibly forecast future outbreaks.

International audience; Background - Increasing incidence of hepatitis C virus (HCV) infection in human immunodeficiency virus (HIV)-positive men having sex with men (MSM) has been described in recent years. Phylogenetic analyses of acute HCV infections were undertaken to characterize the dynamics during the epidemic in Paris, and associated sexually transmitted infections (STIs) were evaluated. Methods - Sanger sequencing of polymerase gene was performed. Maximum likelihood phylogenies were reconstructed using FastTree 2.1 under a GTR+CAT model. Transmission chains were defined as clades with a branch probability ≥0.80 and intraclade genetic distances

Background Human immunodeficiency virus type 1 (HIV-1) phylodynamics can be used to monitor epidemic trends and help target prevention through identification and characterization of transmission clusters. Methods We analyzed HIV-1 pol sequences sampled in North Carolina from 1997 to 2014. Putative clusters were identified using maximum-likelihood trees and dated using Bayesian Markov Chain Monte Carlo inference. Active clusters were defined as clusters including internal nodes from 2009 to 2014. Effective reproductive numbers (Re) were estimated using birth-death models for large clusters that expanded ≥2-fold from 2009 to 2014. Results Of 14 921 persons, 7508 (50%) sequences were identified in 2264 clusters. Only 288 (13%) clusters were active from 2009 to 2014; 37 were large (10-36 members). Compared to smaller clusters, large clusters were increasingly populated by men and younger persons; however, nearly 60% included ≥1 women. Clusters with ≥3 members demonstrated assortative mixing by sex, age, and sample region. Of 15 large clusters with ≥2-fold expansion, nearly all had Re approximately 1 by 2014. Conclusions Phylodynamics revealed transmission cluster expansion in this densely sampled region and allowed estimates of Re to monitor active clusters, showing the propensity for steady, onward propagation. Associations with clustering and cluster characteristics vary by cluster size. Harnessing sequence-derived epidemiologic parameters within routine surveillance could allow refined monitoring of local subepidemics.

During sexual transmission, the large genetic diversity of HIV-1 within an individual is frequently reduced to one founder variant that initiates infection1. Understanding the drivers of this bottleneck is crucial to develop effective infection control strategies2. Genetic characteristics of the potential founder viruses and events in the recipient partner are both known to contribute to this bottleneck, but little is understood about the importance of the source partner3. To test the hypothesis that the source partner affects the multiplicity of HIV founder variants, we developed a phylodynamic model calibrated using genetic and epidemiological data on all existing transmission pairs for whom the direction of transmission and the infection stage of the source partner are known. Our results demonstrate the importance of infection stage of the source partner, and not exposure route, in determining founder variant multiplicity. Specifically, acquiring infection from someone in the acute (early) stage of infection increases the risk of multiple variant transmission when compared with someone in the chronic (later) stage of infection. This study provides the first direct test of source partner characteristics to explain the low frequency of multiple founder strain infections and can inform clinical intervention study design and interpretation.