Search

Despite the emergence of antimicrobial-resistant (AMR) strains of Neisseria gonorrhoeae, the treatment of gonorrhea remains empiric and according to standardized guidelines, which are informed by the national prevalence of resistant strains. Yet, the prevalence of AMR varies substantially across geographic and demographic groups. We investigated whether data from the national surveillance system of AMR gonorrhea in the US could be used to personalize the empiric treatment of gonorrhea. We used data from the Gonococcal Isolate Surveillance Project collected between 2000-2010 to train and validate machine learning models to identify resistance to ciprofloxacin (CIP), one of the recommended first-line antibiotics until 2007. We used these models to personalize empiric treatments based on sexual behavior and geographic location and compared their performance with standardized guidelines, which recommended treatment with CIP, ceftriaxone (CRO), or cefixime (CFX) between 2005-2006, and either CRO or CFX between 2007-2010. Compared with standardized guidelines, the personalized treatments could have replaced 33% of CRO and CFX use with CIP while ensuring that 98% of patients were prescribed effective treatment during 2005-2010. The models maintained their performance over time and across geographic regions. Predictive models trained on data from national surveillance systems of AMR gonorrhea could be used to personalize the empiric treatment of gonorrhea based on patients' basic characteristics at the point of care. This approach could reduce the unnecessary use of newer antibiotics while maintaining the effectiveness of first-line therapy.

BackgroundSince common diagnostic tests for gonorrhea do not provide information about susceptibility to antibiotics, treatment of gonorrhea remains empiric. Antibiotics used for empiric therapy are usually changed once resistance prevalence exceeds a certain threshold (e.g., 5%). A low switch threshold is intended to increase the probability that an infection is successfully treated with the first-line antibiotic, but it could also increase the pace at which recommendations are switched to newer antibiotics. Little is known about the impact of changing the switch threshold on the incidence of gonorrhea, the rate of treatment failure, and the overall cost and quality-adjusted life-years (QALYs) associated with gonorrhea.Methods and findingsWe developed a transmission model of gonococcal infection with multiple resistant strains to project gonorrhea-associated costs and loss in QALYs under different switch thresholds among men who have sex with men (MSM) in the United States. We accounted for the costs and disutilities associated with symptoms, diagnosis, treatment, and sequelae, and combined costs and QALYs in a measure of net health benefit (NHB). Our results suggest that under a scenario where 3 antibiotics are available over the next 50 years (2 suitable for the first-line therapy of gonorrhea and 1 suitable only for the retreatment of resistant infections), changing the switch threshold between 1% and 10% does not meaningfully impact the annual number of gonorrhea cases, total costs, or total QALY losses associated with gonorrhea. However, if a new antibiotic is to become available in the future, choosing a lower switch threshold could improve the population NHB. If in addition, drug-susceptibility testing (DST) is available to inform retreatment regimens after unsuccessful first-line therapy, setting the switch threshold at 1% to 2% is expected to maximize the population NHB. A limitation of our study is that our analysis only focuses on the MSM population and does not consider the influence of interventions such as vaccine and common use of rapid drugs susceptibility tests to inform first-line therapy.ConclusionsChanging the switch threshold for first-line antibiotics may not substantially change the health and financial outcomes associated with gonorrhea. However, the switch threshold could be reduced when newer antibiotics are expected to become available soon or when in addition to future novel antibiotics, DST is also available to inform retreatment regimens.

Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census' American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census' American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system.

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of morbidity and mortality. Colonization by MRSA increases the risk of infection and transmission, underscoring the importance of decolonization efforts. However, success of these decolonization protocols varies, raising the possibility that some MRSA strains may be more persistent than others. Here, we studied how the persistence of MRSA colonization correlates with genomic presence of antibiotic resistance genes. Our analysis using a Bayesian mixed effects survival model found that genetic determinants of high-level resistance to mupirocin was strongly associated with failure of the decolonization protocol. However, we did not see a similar effect with genetic resistance to chlorhexidine or other antibiotics. Including strain-specific random effects improved the predictive performance, indicating that some strain characteristics other than resistance also contributed to persistence. Study subject-specific random effects did not improve the model. Our results highlight the need to consider the properties of the colonizing MRSA strain when deciding which treatments to include in the decolonization protocol.

Background: The combined impact of immunity and SARS-CoV-2 variants on viral kinetics during infections has been unclear. Methods: We characterized 1,280 infections from the National Basketball Association occupational health cohort identified between June 2020 and January 2022 using serial RT-qPCR testing. Logistic regression and semi-mechanistic viral RNA kinetics models were used to quantify the effect of age, variant, symptom status, infection history, vaccination status and antibody titer to the founder SARS-CoV-2 strain on the duration of potential infectiousness and overall viral kinetics. The frequency of viral rebounds was quantified under multiple cycle threshold (Ct) value-based definitions. Results: Among individuals detected partway through their infection, 51.0% (95% credible interval [CrI]: 48.3–53.6%) remained potentially infectious (Ct

Understanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use-resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use-resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Outpatient use of all 5 classes and resistance in inpatient and outpatient isolates in 9 of 15 species-antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate (FDR) < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species-antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.

In the absence of point-of-care gonorrhea diagnostics that report antibiotic susceptibility, gonorrhea treatment is empiric and determined by standardized guidelines. These guidelines are informed by estimates of resistance prevalence from national surveillance systems. We examined whether guidelines informed by local, rather than national, surveillance data could reduce the incidence of gonorrhea and increase the effective lifespan of antibiotics used in treatment guidelines. We used a transmission dynamic model of gonorrhea among men who have sex with men (MSM) in 16 U.S. metropolitan areas to determine whether spatially adaptive treatment guidelines based on local estimates of resistance prevalence can extend the effective lifespan of hypothetical antibiotics. The rate of gonorrhea cases in these metropolitan areas was 5,548 cases per 100,000 MSM in 2017. Under the current strategy of updating the treatment guideline when the prevalence of resistance exceeds 5%, we showed that spatially adaptive guidelines could reduce the annual rate of gonorrhea cases by 200 cases (95% uncertainty interval: 169, 232) per 100,000 MSM population while extending the use of a first-line antibiotic by 0.75 (0.55, 0.95) years. One potential strategy to reduce the incidence of gonorrhea while extending the effective lifespan of antibiotics is to inform treatment guidelines based on local, rather than national, resistance prevalence.

SARS-CoV-2 infections are characterized by viral proliferation and clearance phases and can be followed by low-level persistent viral RNA shedding. The dynamics of viral RNA concentration, particularly in the early stages of infection, can inform clinical measures and interventions such as test-based screening. We used prospective longitudinal quantitative reverse transcription PCR testing to measure the viral RNA trajectories for 68 individuals during the resumption of the 2019-2020 National Basketball Association season. For 46 individuals with acute infections, we inferred the peak viral concentration and the duration of the viral proliferation and clearance phases. According to our mathematical model, we found that viral RNA concentrations peaked an average of 3.3 days (95% credible interval [CI] 2.5, 4.2) after first possible detectability at a cycle threshold value of 22.3 (95% CI 20.5, 23.9). The viral clearance phase lasted longer for symptomatic individuals (10.9 days [95% CI 7.9, 14.4]) than for asymptomatic individuals (7.8 days [95% CI 6.1, 9.7]). A second test within 2 days after an initial positive PCR test substantially improves certainty about a patient's infection stage. The effective sensitivity of a test intended to identify infectious individuals declines substantially with test turnaround time. These findings indicate that SARS-CoV-2 viral concentrations peak rapidly regardless of symptoms. Sequential tests can help reveal a patient's progress through infection stages. Frequent, rapid-turnaround testing is needed to effectively screen individuals before they become infectious.

Background: The impact of variable infection risk by race and ethnicity on the dynamics of SARS-CoV-2 spread is largely unknown. Methods: Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk change across groups. Results: A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites. Conclusions: Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection. Funding: K.C.M. was supported by National Science Foundation GRFP grant DGE1745303. Y.H.G. and M.L. were funded by the Morris-Singer Foundation. M.L. was supported by SeroNet cooperative agreement U01 CA261277.

Establishing how many people have been infected by SARS-CoV-2 remains an urgent priority for controlling the COVID-19 pandemic. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies have been unclear. We developed a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that seropositivity indicates immune protection, we propagated estimates and uncertainty through dynamical models to assess uncertainty in the epidemiological parameters needed to evaluate public health interventions and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize serosurvey design given test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.

Genotype-based diagnostics for antibiotic resistance represent a promising alternative to empiric therapy, reducing inappropriate antibiotic use. However, because such assays infer resistance based on known genetic markers, their utility will wane with the emergence of novel resistance. Maintenance of these diagnostics will therefore require surveillance to ensure early detection of novel resistance variants, but efficient strategies to do so remain undefined. We evaluate the efficiency of targeted sampling approaches informed by patient and pathogen characteristics in detecting antibiotic resistance and diagnostic escape variants in Neisseria gonorrhoeae, a pathogen associated with a high burden of disease and antibiotic resistance and the development of genotype-based diagnostics. We show that patient characteristic-informed sampling is not a reliable strategy for efficient variant detection. In contrast, sampling informed by pathogen characteristics, such as genomic diversity and genomic background, is significantly more efficient than random sampling in identifying genetic variants associated with resistance and diagnostic escape.

BackgroundThe rise of gonococcal antimicrobial resistance highlights the need for strategies that extend the clinically useful life span of antibiotics. Because there is limited evidence to support the current practice of switching empiric first-line antibiotic when resistance exceeds 5% in the population, our objective was to compare the impact of alternative strategies on the effective life spans of antibiotics and the overall burden of gonorrhea.Methods and findingsWe developed and calibrated a mathematical model of gonorrhea transmission among men who have sex with men (MSM) in the United States. We calibrated the model to the estimated prevalence of gonorrhea, the rate of gonorrhea cases, and the proportion of cases presenting symptoms among MSM in the US. We used this model to project the effective life span of antibiotics and the number of gonorrhea cases expected under current and alternative surveillance strategies over a 50-year simulation period. We demonstrate that compared to the current practice, a strategy that uses quarterly (as opposed to yearly) surveillance estimates and incorporates both the estimated prevalence of resistance and the trend in the prevalence of resistance to determine treatment guidelines could extend the effective life span of antibiotics by 0.83 years. This is equivalent to successfully treating an additional 80.1 (95% uncertainty interval: [47.7, 111.9]) gonorrhea cases per 100,000 MSM population each year with the first-line antibiotics without worsening the burden of gonorrhea. If the annual number of isolates tested for drug susceptibility is doubled, this strategy could increase the effective life span of antibiotics by 0.94 years, which is equivalent to successfully treating an additional 91.1 (54.3, 127.3) gonorrhea cases per 100,000 MSM population each year without increasing the incidence of gonorrhea. Study limitations include that our conclusions might not be generalizable to other settings because our model describes the transmission of gonorrhea among the US MSM population, and, to better capture uncertainty in the characteristics of current and future antibiotics, we chose to model hypothetical drugs with characteristics similar to the antibiotics commonly used in gonorrhea treatment.ConclusionsOur results suggest that use of data from surveillance programs could be expanded to prolong the clinical effectiveness of antibiotics without increasing the burden of the disease. This highlights the importance of maintaining effective surveillance systems and the engagement of policy makers to turn surveillance findings into timely and effective decisions.

The relationship between antibiotic stewardship and population levels of antibiotic resistance remains unclear. In order to better understand shifts in selective pressure due to stewardship, we use publicly available data to estimate the effect of changes in prescribing on exposures to frequently used antibiotics experienced by potentially pathogenic bacteria that are asymptomatically colonizing the microbiome. We quantify this impact under four hypothetical stewardship strategies. In one scenario, we estimate that elimination of all unnecessary outpatient antibiotic use could avert 6% to 48% (IQR: 17% to 31%) of exposures across pairwise combinations of sixteen common antibiotics and nine bacterial pathogens. All scenarios demonstrate that stewardship interventions, facilitated by changes in clinician behavior and improved diagnostics, have the opportunity to broadly reduce antibiotic exposures across a range of potential pathogens. Concurrent approaches, such as vaccines aiming to reduce infection incidence, are needed to further decrease exposures occurring in ‘necessary’ contexts.

Increasing Neisseria gonorrhoeae resistance to ceftriaxone, the last antibiotic recommended for empiric gonorrhea treatment, poses an urgent public health threat. However, the genetic basis of reduced susceptibility to ceftriaxone is not completely understood: while most ceftriaxone resistance in clinical isolates is caused by target site mutations in penA, some isolates lack these mutations. We show that penA-independent ceftriaxone resistance has evolved multiple times through distinct mutations in rpoB and rpoD. We identify five mutations in these genes that each increase resistance to ceftriaxone, including one mutation that arose independently in two lineages, and show that clinical isolates from multiple lineages are a single nucleotide change from ceftriaxone resistance. These RNA polymerase mutations cause large-scale transcriptional changes without altering susceptibility to other antibiotics, reducing growth rate, or deranging cell morphology. These results underscore the unexpected diversity of pathways to resistance and the importance of continued surveillance for novel resistance mutations.

Unusually large outbreaks of mumps across the United States in 2016 and 2017 raised questions about the extent of mumps circulation and the relationship between these and prior outbreaks. We paired epidemiological data from public health investigations with analysis of mumps virus whole genome sequences from 201 infected individuals, focusing on Massachusetts university communities. Our analysis suggests continuous, undetected circulation of mumps locally and nationally, including multiple independent introductions into Massachusetts and into individual communities. Despite the presence of these multiple mumps virus lineages, the genomic data show that one lineage has dominated in the US since at least 2006. Widespread transmission was surprising given high vaccination rates, but we found no genetic evidence that variants arising during this outbreak contributed to vaccine escape. Viral genomic data allowed us to reconstruct mumps transmission links not evident from epidemiological data or standard single-gene surveillance efforts and also revealed connections between apparently unrelated mumps outbreaks.

The human pathogens N. gonorrhoeae and N. meningitidis display robust intra- and interstrain glycan diversity associated with their O-linked protein glycosylation (pgl) systems. In an effort to better understand the evolution and function of protein glycosylation operating there, we aimed to determine if other human-restricted, Neisseria species similarly glycosylate proteins and if so, to assess the levels of glycoform diversity. Comparative genomics revealed the conservation of a subset of genes minimally required for O-linked protein glycosylation glycan and established those pgl genes as core genome constituents of the genus. In conjunction with mass spectrometric-based glycan phenotyping, we found that extant glycoform repertoires in N. gonorrhoeae, N. meningitidis and the closely related species N. polysaccharea and N. lactamica reflect the functional replacement of a progenitor glycan biosynthetic pathway. This replacement involved loss of pgl gene components of the primordial pathway coincident with the acquisition of two exogenous glycosyltransferase genes. Critical to this discovery was the identification of a ubiquitous but previously unrecognized glycosyltransferase gene (pglP) that has uniquely undergone parallel but independent pseudogenization in N. gonorrhoeae and N. meningitidis. We suggest that the pseudogenization events are driven by processes of compositional epistasis leading to gene decay. Additionally, we documented instances where inter-species recombination influences pgl gene status and creates discordant genetic interactions due ostensibly to the multi-locus nature of pgl gene networks. In summary, these findings provide a novel perspective on the evolution of protein glycosylation systems and identify phylogenetically informative, genetic differences associated with Neisseria species.

The sensitivity of genotype-based diagnostics that predict antimicrobial susceptibility is limited by the extent to which they detect genes and alleles that lead to resistance. As novel resistance variants are expected to emerge, such sensitivity is expected to decline unless the new variants are detected and incorporated into the diagnostic. Here, we present a mathematical framework to define how many diagnostic failures may be expected under varying surveillance regimes and thus quantify the surveillance needed to maintain the sensitivity of genotype-based diagnostics.

Antibiotic-resistant Staphylococcus aureus remains a leading cause of antibiotic resistance-associated mortality in the United States. Given the reality of multi-drug resistant infections, it is imperative that we establish and maintain a pipeline of new compounds to replace or supplement our current antibiotics. A first step towards this goal is to prioritize targets by identifying the genes most consistently required for survival across the S. aureus phylogeny. Here we report the first direct comparison of multiple strains of S. aureus via transposon sequencing. We show that mutant fitness varies by strain in key pathways, underscoring the importance of using more than one strain to differentiate between core and strain-dependent essential genes. We treated the libraries with daptomycin to assess whether the strain-dependent differences impact pathways important for survival. Despite baseline differences in gene importance, several pathways, including the lipoteichoic acid pathway, consistently promote survival under daptomycin exposure, suggesting core vulnerabilities that can be exploited to resensitize daptomycin-nonsusceptible isolates. We also demonstrate the merit of using transposons with outward-facing promoters capable of overexpressing nearby genes for identifying clinically-relevant gain-of-function resistance mechanisms. Together, the daptomycin vulnerabilities and resistance mechanisms support a mode of action with wide-ranging effects on the cell envelope and cell division. This work adds to a growing body of literature demonstrating the nuanced insights gained by comparing Tn-Seq results across multiple bacterial strains.

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.

We are writing to reply to the comment by Pouwels et al., 2019 about our recent study (Olesen et al., 2018) on antibiotic use and antibiotic resistance.

Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.

Antibiotic use is a primary driver of antibiotic resistance. However, antibiotic use can be distributed in different ways in a population, and the association between the distribution of use and antibiotic resistance has not been explored. Here, we tested the hypothesis that repeated use of antibiotics has a stronger association with population-wide antibiotic resistance than broadly-distributed, low-intensity use. First, we characterized the distribution of outpatient antibiotic use across US states, finding that antibiotic use is uneven and that repeated use of antibiotics makes up a minority of antibiotic use. Second, we compared antibiotic use with resistance for 72 pathogen-antibiotic combinations across states. Finally, having partitioned total use into extensive and intensive margins, we found that intense use had a weaker association with resistance than extensive use. If the use-resistance relationship is causal, these results suggest that reducing total use and selection intensity will require reducing broadly distributed, low-intensity use.

The Antimicrobial Resistance - Genomes, Big Data and Emerging Technologies Conference explored key topics including measuring the burden of AMR, global public health pathogen genomics infrastructure and surveillance, translation and implementation of genomics for AMR control, use of techniques such as wastewater surveillance, mathematical and statistical modelling, and Artificial Intelligence (AI) to aid understanding of AMR. This report describes research presented during plenary sessions and discussions, keynote presentations and posters.

Abstract New, first-in-class oral antibiotics like zoliflodacin, developed in a public–private partnership, require an optimal introduction strategy while ensuring antibiotic stewardship. Zoliflodacin, given as a single dose for uncomplicated urogenital gonorrhoea, recently demonstrated non-inferiority to ceftriaxone plus azithromycin and safety in a phase 3 randomised controlled trial. Following regulatory approval, zoliflodacin could improve sexually transmitted infection (STI) management and help address the threat of untreatable gonorrhoea, as levels of resistance to current first-line treatments increase. The Global Antibiotic Research & Development Partnership (GARDP) convened an expert meeting during the 2023 STI and HIV World Congress to discuss key questions about the introduction of zoliflodacin in low- and middle-income countries (LMICs). The questions included: which patients to treat in which situations, the timing of introduction, and what additional evidence is needed to change policy for the use of new antibiotics for gonorrhoea. Recommendations from the expert group included: the generation of evidence for the role of a drug like zoliflodacin in clinical treatment failures; the need for additional antimicrobial resistance surveillance; investigation of the role of novel diagnostic approaches, such as point-of-care tests, to improve stewardship; study of preferences and values among the population in need; and modelling of the emergence of N. gonorrhoeae resistance and transmission in different scenarios. Forthcoming World Health Organization (WHO) global guidelines could outline recommendations for a new oral antibiotic like zoliflodacin based on existing evidence, and rational approaches for certain populations or use cases, while the evidence base is further strengthened.

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.

Abstract The impact of a prior SARS-CoV-2 infection on the progression of subsequent infections has been unclear. Using a convenience sample of 94,812 longitudinal RT-qPCR measurements from anterior nares and oropharyngeal swabs, we identified 71 individuals with two well-sampled SARS-CoV-2 infections between March 11th, 2020, and July 28th, 2022. We compared the SARS-CoV-2 viral kinetics of first vs. second infections in this group, adjusting for viral variant, vaccination status, and age. Relative to first infections, second infections usually featured a faster clearance time. Furthermore, a person’s relative (rank-order) viral clearance time, compared to others infected with the same variant, was roughly conserved across first and second infections, so that individuals who had a relatively fast clearance time in their first infection also tended to have a relatively fast clearance time in their second infection (Spearman correlation coefficient: 0.30, 95% credible interval (0.12, 0.46)). These findings provide evidence that, like vaccination, immunity from a prior SARS-CoV-2 infection shortens the duration of subsequent acute SARS-CoV-2 infections principally by reducing viral clearance time. Additionally, there appears to be an inherent element of the immune response, or some other host factor, that shapes a person’s relative ability to clear SARS-CoV-2 infection that persists across sequential infections.

Public health surveillance for pathogens presents an optimization problem: we require enough sampling to identify intervention-triggering shifts in pathogen epidemiology, such as new introductions or sudden increases in prevalence, but not so much that costs due to surveillance itself outweigh those from pathogen-associated illness. To determine this optimal sampling frequency, we developed a general mathematical model for the introduction of a new pathogen that, once introduced, increases in prevalence exponentially. Given the relative cost of infection vs. sampling, we derived equations for the expected combined cost per unit time of disease burden and surveillance for a specified sampling frequency, and thus the sampling frequency for which the expected total cost per unit time is lowest.

Abstract Background The World Health Organization recommends changing the first-line antimicrobial treatment for gonorrhoea when ≥ 5% of Neisseria gonorrhoeae cases fail treatment or are resistant. Susceptibility to ceftriaxone, the last remaining treatment option has been decreasing in many countries. We used antimicrobial resistance surveillance data and developed mathematical models to project the time to reach the 5% threshold for resistance to first-line antimicrobials used for N. gonorrhoeae. Methods We used data from the Gonococcal Resistance to Antimicrobials Surveillance Programme (GRASP) in England and Wales from 2000–2018 about minimum inhibitory concentrations (MIC) for ciprofloxacin, azithromycin, cefixime and ceftriaxone and antimicrobial treatment in two groups, heterosexual men and women (HMW) and men who have sex with men (MSM). We developed two susceptible-infected-susceptible models to fit these data and produce projections of the proportion of resistance until 2030. The single-step model represents the situation in which a single mutation results in antimicrobial resistance. In the multi-step model, the sequential accumulation of resistance mutations is reflected by changes in the MIC distribution. Results The single-step model described resistance to ciprofloxacin well. Both single-step and multi-step models could describe azithromycin and cefixime resistance, with projected resistance levels higher with the multi-step than the single step model. For ceftriaxone, with very few observed cases of full resistance, the multi-step model was needed to describe long-term dynamics of resistance. Extrapolating from the observed upward drift in MIC values, the multi-step model projected ≥ 5% resistance to ceftriaxone could be reached by 2030, based on treatment pressure alone. Ceftriaxone resistance was projected to rise to 13.2% (95% credible interval [CrI]: 0.7–44.8%) among HMW and 19.6% (95%CrI: 2.6–54.4%) among MSM by 2030. Conclusions New first-line antimicrobials for gonorrhoea treatment are needed. In the meantime, public health authorities should strengthen surveillance for AMR in N. gonorrhoeae and implement strategies for continued antimicrobial stewardship. Our models show the utility of long-term representative surveillance of gonococcal antimicrobial susceptibility data and can be adapted for use in, and for comparison with, other countries.

Background: Mathematical modeling of infectious diseases is an important decision-making tool for outbreak control. However, in Africa, limited expertise reduces the use and impact of these tools on policy. Therefore, there is a need to build capacity in Africa for the use of mathematical modeling to inform policy. Here we describe our experience implementing a mathematical modeling training program for public health professionals in East Africa. Methods: We used a deliverable-driven and learning-by-doing model to introduce trainees to the mathematical modeling of infectious diseases. The training comprised two two-week in-person sessions and a practicum where trainees received intensive mentorship. Trainees evaluated the content and structure of the course at the end of each week, and this feedback informed the strategy for subsequent weeks. Findings: Out of 875 applications from 38 countries, we selected ten trainees from three countries – Rwanda (6), Kenya (2), and Uganda (2) – with guidance from an advisory committee. Nine trainees were based at government institutions and one at an academic organization. Participants gained skills in developing models to answer questions of interest and critically appraising modeling studies. At the end of the training, trainees prepared policy briefs summarizing their modeling study findings. These were presented at a dissemination event to policymakers, researchers, and program managers. All trainees indicated they would recommend the course to colleagues and rated the quality of the training with a median score of 9/10. Conclusions: Mathematical modeling training programs for public health professionals in Africa can be an effective tool for research capacity building and policy support to mitigate infectious disease burden and forecast resources. Overall, the course was successful, owing to a combination of factors, including institutional support, trainees’ commitment, intensive mentorship, a diverse trainee pool, and regular evaluations.

ABSTRACT Shiga toxin-producing Escherichia coli (STEC) can give rise to a range of clinical outcomes from diarrhea to the life-threatening systemic condition hemolytic-uremic syndrome (HUS). Although STEC O157:H7 is the serotype most frequently associated with HUS, a major outbreak of HUS occurred in 2011 in Germany and was caused by a rare serotype, STEC O104:H4. Prior to 2011 and since the outbreak, STEC O104:H4 strains have only rarely been associated with human infections. From 2012 to 2020, intensified STEC surveillance was performed in Germany where the subtyping of ~8,000 clinical isolates by molecular methods, including whole-genome sequencing, was carried out. A rare STEC serotype, O181:H4, associated with HUS was identified, and like the STEC O104:H4 outbreak strain, this strain belongs to sequence type 678 (ST678). Genomic and virulence comparisons revealed that the two strains are phylogenetically related and differ principally in the gene cluster encoding their respective lipopolysaccharide O-antigens but exhibit similar virulence phenotypes. In addition, five other serotypes belonging to ST678 from human clinical infection, such as OX13:H4, O127:H4, OgN-RKI9:H4, O131:H4, and O69:H4, were identified from diverse locations worldwide. IMPORTANCE Our data suggest that the high-virulence ensemble of the STEC O104:H4 outbreak strain remains a global threat because genomically similar strains cause disease worldwide but that the horizontal acquisition of O-antigen gene clusters has diversified the O-antigens of strains belonging to ST678. Thus, the identification of these highly pathogenic strains is masked by diverse and rare O-antigens, thereby confounding the interpretation of their potential risk.

Emergence of a novel pathogen drives the urgent need for diagnostic tests that can aid in defining disease prevalence. The limitations associated with rapid development and deployment of these tests result in a dilemma: In efforts to optimize prevalence estimates, would tests be better used in the lab to reduce uncertainty in test characteristics or to increase sample size in field studies? Here, we provide a framework to address this question through a joint Bayesian model that simultaneously analyzes lab validation and field survey data, and we define the impact of test allocation on inferences of sensitivity, specificity, and prevalence. In many scenarios, prevalence estimates can be most improved by apportioning additional effort towards validation rather than to the field. The joint model provides superior estimation of prevalence, sensitivity, and specificity, compared with typical analyses that model lab and field data separately, and it can be used to inform sample allocation when testing is limited.

Abstract Background Antimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. Methods Here we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch ( https://pathogen.watch/ngonorrhoeae ). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. Results AMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. Conclusions The N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.

Abstract Background Antimicrobial resistance in Neisseria gonorrhoeae is a global health concern. Strains from two internationally circulating sequence types, ST-7363 and ST-1901, have acquired resistance to third-generation cephalosporins, mainly due to mosaic penA alleles. These two STs were first detected in Japan; however, the timeline, mechanism, and process of emergence and spread of these mosaic penA alleles to other countries remain unknown. Methods We studied the evolution of penA alleles by obtaining the complete genomes from three Japanese ST-1901 clinical isolates harboring mosaic penA allele 34 (penA-34) dating from 2005 and generating a phylogenetic representation of 1075 strains sampled from 35 countries. We also sequenced the genomes of 103 Japanese ST-7363 N. gonorrhoeae isolates from 1996 to 2005 and reconstructed a phylogeny including 88 previously sequenced genomes. Results Based on an estimate of the time-of-emergence of ST-1901 (harboring mosaic penA-34) and ST-7363 (harboring mosaic penA-10), and > 300 additional genome sequences of Japanese strains representing multiple STs isolated in 1996–2015, we suggest that penA-34 in ST-1901 was generated from penA-10 via recombination with another Neisseria species, followed by recombination with a gonococcal strain harboring wildtype penA-1. Following the acquisition of penA-10 in ST-7363, a dominant sub-lineage rapidly acquired fluoroquinolone resistance mutations at GyrA 95 and ParC 87-88, by independent mutations rather than horizontal gene transfer. Data in the literature suggest that the emergence of these resistance determinants may reflect selection from the standard treatment regimens in Japan at that time. Conclusions Our findings highlight how antibiotic use and recombination across and within Neisseria species intersect in driving the emergence and spread of drug-resistant gonorrhea.

ABSTRACT Protein glycosylation systems are widely recognized in bacteria, including members of the genus Neisseria. In most bacterial species, the molecular mechanisms and evolutionary contexts underpinning target protein selection and the glycan repertoire remain poorly understood. Broad-spectrum O-linked protein glycosylation occurs in all human-associated species groups within the genus Neisseria, but knowledge of their individual glycoprotein repertoires is limited. Interestingly, PilE, the pilin subunit of the type IV pilus (Tfp) colonization factor, is glycosylated in Neisseria gonorrhoeae and Neisseria meningitidis but not in the deeply branching species N. elongata subsp. glycolytica. To examine this in more detail, we assessed PilE glycosylation status across the genus and found that PilEs of commensal clade species are not modified by the gonococcal PglO oligosaccharyltransferase. Experiments using PglO oligosaccharyltransferases from across the genus expressed in N. gonorrhoeae showed that although all were capable of broad-spectrum protein glycosylation, those from a deep-branching group of commensals were unable to support resident PilE glycosylation. Further glycoproteomic analyses of these strains using immunoblotting and mass spectrometry revealed other proteins differentially targeted by otherwise remarkably similar oligosaccharyltransferases. Finally, we generated pglO allelic chimeras that begin to localize PglO protein domains associated with unique substrate targeting activities. These findings reveal previously unappreciated differences within the protein glycosylation systems of highly related bacterial species. We propose that the natural diversity manifest in the neisserial protein substrates and oligosaccharyltransferases has significant potential to inform the structure-function relationships operating in these and related bacterial protein glycosylation systems. IMPORTANCE Although general protein glycosylation systems have been well recognized in prokaryotes, the processes governing their distribution, function, and evolution remain poorly understood. Here, we have begun to address these gaps in knowledge by comparative analyses of broad-spectrum O-linked protein glycosylation manifest in species within the genus Neisseria that strictly colonize humans. Using N. gonorrhoeae as a well-defined model organism in conjunction with comparative genomics, intraspecies gene complementation, and glycoprotein phenotyping, we discovered clear differences in both glycosylation susceptibilities and enzymatic targeting activities of otherwise largely conserved proteins. These findings reveal previously unappreciated differences within the protein glycosylation systems of highly related bacterial species. We propose that the natural diversity manifest within Neisseria species has significant potential to elucidate the structure-function relationships operating in these and related systems and to inform novel approaches to applied glycoengineering strategies.

The mechanisms underlying resistance of Neisseria gonorrhoeae to the antibiotic azithromycin are incompletely understood. Here, Ma et al. conduct a conditional genome-wide association study to identify new resistance mutations and experimentally confirm that a mutation in ribosomal protein L4 confers increased resistance.

New York City is one of the areas most affected by the SARS-CoV-2 pandemic in the United States, and there has been large variation in rates of hospitalisation and death by city borough. Here, the authors show that boroughs with the largest reduction in daily commutes also had the lowest SARS-CoV-2 prevalence.

Antibiotic resistance in Neisseria gonorrhoeae is rising, yet sometimes strains emerge that have reverted to susceptibility. Here, the authors find that selective pressures from the host may influence susceptibility through loss-of-function mutations in genes that encode for efflux pumps.

Here, Williamson et al. combine epidemiological and genomic analysis of 2,186 Neisseria gonorrhoeae isolates from Australia and show that men who have sex with men and women are a possible ‘bridging’ population between men who have sex with men and heterosexuals.

Using a US nationwide survey, we measured disparities in antimicrobial drug acquisition by race/ethnicity for 2014–2015. White persons reported twice as many antimicrobial drug prescription fills per capita as persons of other race/ethnicities. Characterizing antimicrobial drug use by demographic might improve antimicrobial drug stewardship and help address antimicrobial drug resistance.

ABSTRACT Mosaic interspecifically acquired alleles of the multiple transferable resistance (mtr) efflux pump operon correlate with increased resistance to azithromycin in Neisseria gonorrhoeae in epidemiological studies. However, whether and how these alleles cause resistance is unclear. Here, we use population genomics, transformations, and transcriptional analyses to dissect the relationship between variant mtr alleles and azithromycin resistance. We find that the locus encompassing the mtrR transcriptional repressor and the mtrCDE pump is a hot spot of interspecific recombination introducing alleles from Neisseria meningitidis and Neisseria lactamica into N. gonorrhoeae, with multiple rare haplotypes in linkage disequilibrium at mtrD and the mtr promoter region. Transformations demonstrate that resistance to azithromycin, as well as to other antimicrobial compounds such as polymyxin B and crystal violet, is mediated through epistasis between these two loci and that the full-length mosaic mtrD allele is required. Gene expression profiling reveals the mechanism of resistance in mosaics couples novel mtrD alleles with promoter mutations that increase expression of the pump. Overall, our results demonstrate that epistatic interactions at mtr gained from multiple neisserial species has contributed to increased gonococcal resistance to diverse antimicrobial agents. IMPORTANCE Neisseria gonorrhoeae is the sexually transmitted bacterial pathogen responsible for more than 100 million cases of gonorrhea worldwide each year. The incidence of resistance to the macrolide azithromycin has increased in the past decade; however, a large proportion of the genetic basis of resistance remains unexplained. This study is the first to conclusively demonstrate the acquisition of macrolide resistance through mtr alleles from other Neisseria species, demonstrating that commensal Neisseria bacteria are a reservoir for antibiotic resistance to macrolides, extending the role of interspecies mosaicism in resistance beyond what has been previously described for cephalosporins. Ultimately, our results emphasize that future fine-mapping of genome-wide interspecies mosaicism may be valuable in understanding the pathways to antimicrobial resistance. Our results also have implications for diagnostics and public health surveillance and control, as they can be used to inform the development of sequence-based tools to monitor and control the spread of antibiotic-resistant gonorrhea.

ABSTRACT The devastating Haitian cholera outbreak that began in October 2010 is the first known cholera epidemic in this island nation. Epidemiological and genomic data have provided strong evidence that United Nations security forces from Nepal introduced toxigenic Vibrio cholerae O1, the cause of epidemic cholera, to Haiti shortly before the outbreak arose. However, some have contended that indigenous V. cholerae contributed to the outbreak. In a recent paper (mBio 4:e00398-13, 2013), L. S. Katz et al. explored the nature and rate of changes in this ancient pathogen’s genome during an outbreak, based on whole-genome sequencing of 23 Haitian V. cholerae clinical isolates obtained over a 20-month period. Notably, they detected point mutations, deletions, and inversions but found no insertion of horizontally transmitted DNA, arguing strongly against the idea that autochthonous V. cholerae donated DNA to the outbreak strain. Furthermore, they found that Haitian epidemic V. cholerae isolates were virtually untransformable. Comparative genomic analyses revealed that the Haitian isolates were nearly identical to isolates from Nepal and that the Nepalese-Haitian isolates were distinguishable from isolates circulating elsewhere in the world. Reconstruction of the phylogeny of the Haitian isolates was consistent with a single introduction of V. cholerae to Haiti sometime between late July and late October 2010, dates remarkably concordant with epidemiological observations. In aggregate, this paper provides additional compelling evidence that the V. cholerae strain responsible for the Haitian cholera epidemic came from Nepal and illustrates the power of whole-genome-based analyses for epidemiology, pathogen evolution, and forensics.

ABSTRACT The large outbreak of diarrhea and hemolytic uremic syndrome (HUS) caused by Shiga toxin-producing Escherichia coli O104:H4 in Europe from May to July 2011 highlighted the potential of a rarely identified E. coli serogroup to cause severe disease. Prior to the outbreak, there were very few reports of disease caused by this pathogen and thus little known of its diversity and evolution. The identification of cases of HUS caused by E. coli O104:H4 in France and Turkey after the outbreak and with no clear epidemiological links raises questions about whether these sporadic cases are derived from the outbreak. Here, we report genome sequences of five independent isolates from these cases and results of a comparative analysis with historical and 2011 outbreak isolates. These analyses revealed that the five isolates are not derived from the outbreak strain; however, they are more closely related to the outbreak strain and each other than to isolates identified prior to the 2011 outbreak. Over the short time scale represented by these closely related organisms, the majority of genome variation is found within their mobile genetic elements: none of the nine O104:H4 isolates compared here contain the same set of plasmids, and their prophages and genomic islands also differ. Moreover, the presence of closely related HUS-associated E. coli O104:H4 isolates supports the contention that fully virulent O104:H4 isolates are widespread and emphasizes the possibility of future food-borne E. coli O104:H4 outbreaks. IMPORTANCE In the summer of 2011, a large outbreak of bloody diarrhea with a high rate of severe complications took place in Europe, caused by a previously rarely seen Escherichia coli strain of serogroup O104:H4. Identification of subsequent infections caused by E. coli O104:H4 raised questions about whether these new cases represented ongoing transmission of the outbreak strain. In this study, we sequenced the genomes of isolates from five recent cases and compared them with historical isolates. The analyses reveal that, in the very short term, evolution of the bacterial genome takes place in parts of the genome that are exchanged among bacteria, and these regions contain genes involved in adaptation to local environments. We show that these recent isolates are not derived from the outbreak strain but are very closely related and share many of the same disease-causing genes, emphasizing the concern that these bacteria may cause future severe outbreaks.

Early in the COVID-19 pandemic, outpatient clinics throughout the US shifted toward virtual care to limit viral transmission in the office. However, as health care facilities have reopened, evidence about the risk of acquiring respiratory viral infections in medical office settings remains limited. To inform policy for reopening outpatient care settings, we analyzed rates of potential airborne disease transmission in medical office settings, focusing on influenza-like illness. We quantified whether exposed patients (that is, those seen in a medical office after a patient with influenza-like illness) were more likely to return with a similar illness in the next two weeks compared with nonexposed patients seen earlier in the day. Patients exposed to influenza-like illness in the medical office setting were more likely than nonexposed patients to revisit with a similar illness within two weeks (adjusted absolute difference: 0.7 per 1,000 patients). Similar patterns were not observed for exposure to urinary tract infection and back pain as noncontagious control conditions. These results highlight the potential threat of reopening outpatient clinics during the pandemic and the value of virtual visits for patients with suspected respiratory infections.