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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 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.

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.