Search

Several mathematical models of epidemic cholera have recently been proposed in response to outbreaks in Zimbabwe and Haiti. These models aim to estimate the dynamics of cholera transmission and the impact of possible interventions, with a goal of providing guidance to policy makers in deciding among alternative courses of action, including vaccination, provision of clean water, and antibiotics. Here, we discuss concerns about model misspecification, parameter uncertainty, and spatial heterogeneity intrinsic to models for cholera. We argue for caution in interpreting quantitative predictions, particularly predictions of the effectiveness of interventions. We specify sensitivity analyses that would be necessary to improve confidence in model-based quantitative prediction, and suggest types of monitoring in future epidemic settings that would improve analysis and prediction. [ABSTRACT FROM AUTHOR]

Background: Doxycycline post-exposure prophylaxis (PEP) has been shown to be efficacious for the prevention of bacterial sexually transmitted infections, but resistance implications for Neisseria gonorrhoeae remain unknown. We aimed to use a mathematical model to investigate the anticipated impact of doxycycline PEP on the burden of gonorrhoea and antimicrobial resistance dynamics in men who have sex with men (MSM) in the USA., Methods: Using a deterministic compartmental model, characterising gonorrhoea transmission in a US MSM population comprising three sexual activity groups defined by annual partner turnover rates, we introduced doxycycline PEP at various uptake levels (10-90%) among those with high sexual activity. Infections were stratified by symptom status and resistance profile (ie, susceptible, ceftriaxone-resistant, tetracycline-resistant, or dual-resistant), with ceftriaxone the treatment for active infection. As resistance to tetracycline, not doxycycline, is monitored and reported nationally, we used this as a proxy for doxycycline PEP resistance. We compared the 20-year prevalence, incidence rates, and cumulative incidence of gonococcal infection, resistance dynamics (time to 5% prevalence of ceftriaxone resistance, 5% prevalence of dual resistance, and 84% prevalence of tetracycline resistance), and antibiotic consumption with baseline (ie, no doxycycline PEP)., Findings: Uptake of doxycycline PEP resulted in substantial reductions in the prevalence and incidence of gonorrhoea, but accelerated the spread of tetracycline resistance. The maximum reduction in prevalence over 20 years compared with no uptake ranged from 40·3% (IQR 15·3-83·4) with 10% doxycycline PEP uptake to 77·4% (68·4-84·9) with 90% uptake. Similarly, the maximum reduction in the incidence rate ranged from 38·6% (14·1-83·6) with 10% uptake to 77·6% (68·1-84·7) with 90% uptake. Cumulative gonococcal infections were reduced by a median of 14·5% (IQR 8·4-21·6) with 10% uptake and up to 46·2% (26·5-59·9) with 90% uptake after 5 years, and by 6·5% (3·4-13·0) with 10% uptake and 8·7% (4·3-36·2) with 90% uptake by 20 years. In almost all scenarios explored, doxycycline PEP lost clinical effectiveness (defined as 84% prevalence of tetracycline resistance) within the 20-year period, but its lifespan ranged from a median of 12·1 years (IQR 9·9-15·7) with 10% uptake to 1·6 years (1·3-1·9) with 90% uptake. Doxycycline PEP implementation had minimal impact on extending the clinical lifespan of ceftriaxone monotherapy (5·0 years [IQR 4·0-6·2]), with the median time to 5% prevalence of resistance ranging from 4·8 years (3·9-6·0) for 90% uptake to 5·0 years (4·1-6·2) for 10% uptake. Similarly, the median time to 5% prevalence of dual resistance to ceftriaxone and tetracycline ranged from 4·8 years (3·9-6·0) for 90% uptake to 5·8 years (4·8-7·4) for 10% uptake. Median decrease in ceftriaxone consumption for high doxycycline PEP uptake levels compared with baseline ranged from 41·7% (27·0-54·3) for 50% uptake to 50·2% (29·3-62·7) for 90% uptake at 5 years, but dropped to 11·8% (6·9-32·0) for 50% uptake and 12·1% (7·0-41·6) for 90% uptake after 20 years., Interpretation: Notwithstanding the clear benefits of doxycycline PEP for other sexually transmitted infections, for N gonorrhoeae, model findings suggest that doxycycline PEP is an effective but impermanent solution for reducing infection burden, given eventual selection for resistant strains. This finding presents a challenge for policy makers considering strategies for doxycycline PEP implementation and oversight: the need to balance the clear, short-term clinical benefits with the risk of harm via antimicrobial resistance., Funding: US Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases., Competing Interests: Declaration of interests YHG has received consulting fees from GSK outside the scope of this work. ER is an employee of Analysis Group, outside the scope of this work., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Surveillance systems that monitor pathogen genome sequences are critical for rapidly detecting the introduction and emergence of pathogen variants. To evaluate how interactions between surveillance capacity, variant properties, and the epidemiological context influence the timeliness of pathogen variant detection, we developed a geographically explicit stochastic compartmental model to simulate the transmission of a novel SARS-CoV-2 variant in New York City. We measured the impact of (1) testing and sequencing volume, (2) geographic targeting of testing, (3) the timing and location of variant emergence, and (4) the relative variant transmissibility on detection speed and on the undetected disease burden. Improvements in detection times and reduction of undetected infections were driven primarily by increases in the number of sequenced samples. The relative transmissibility of the new variant and the epidemic context of variant emergence also influenced detection times, showing that individual surveillance strategies can result in a wide range of detection outcomes, depending on the underlying dynamics of the circulating variants. These findings help contextualize the design, interpretation, and trade-offs of genomic surveillance strategies of pandemic respiratory pathogens., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Doxycycline post-exposure prophylaxis (doxy-PEP) for sexually transmitted bacterial infections reduces the risk of syphilis and chlamydia, but effectiveness against gonorrhea is variable, likely attributable to varying resistance rates. As doxy-PEP is incorporated into clinical practice, an urgent unanswered question is whether increased doxycycline use will drive tetracycline-class resistance in Neisseria gonorrhoeae . Here, we report an updated RT-PCR molecular diagnostic to detect the tetM gene that confers high-level tetracycline resistance in N. gonorrhoeae .

In Staphylococcus aureus infections in men eligible for doxycycline post-exposure prophylaxis (doxy-PEP), tetracycline non-susceptibility is more prevalent than in the overall population and is associated with resistance to trimethoprim-sulfamethoxazole and clindamycin. Doxy-PEP may select for S. aureus multi-drug resistance, underscoring the importance of surveillance.

In a retrospective, ecological analysis of US medical claims, visit rates explained more of the geographic variation in outpatient antibiotic prescribing rates than per-visit prescribing. Efforts to reduce antibiotic use may benefit from addressing the factors that drive higher rates of outpatient visits, in addition to continued focus on stewardship., Competing Interests: Potential conflicts of interest . M. L. B. reports personal consulting fees from the California Department of Health Care Services and payment as an expert witness for Greylock McKinnon Associates. S. M. K. reports consulting fees paid directly to the author from Moderna Therapeutics. Y. H. G. reports consulting fees from Decoy Therapeutics and Day Zero Diagnostics and participation on a data and safety monitoring board or advisory board for Decoy Therapeutics and Day Zero Diagnostics. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Competing Interests: The project described was supported by grant number T32 AI007433 from the National Institute of Allergy and Infectious Diseases. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. This project was funded (in part) by contract 200–2016–91779 with the US Centers for Disease Control and Prevention. The findings, conclusions, and views expressed in this Comment are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. We would like to thank Samantha Palace for helpful comments on this Comment.

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., Competing Interests: RS was employed by company Siemens Healthcare Diagnostics. JV was employed by company SIGA Technologies. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lipsitch, Bassett, Brownstein, Elliott, Eyre, Grabowski, Hay, Johansson, Kissler, Larremore, Layden, Lessler, Lynfield, MacCannell, Madoff, Metcalf, Meyers, Ofori, Quinn, Bento, Reich, Riley, Rosenfeld, Samore, Sampath, Slayton, Swerdlow, Truelove, Varma and Grad.)

Background: Since 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 Findings: We 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., Conclusions: Changing 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., Competing Interests: T.L.G is employed by CDC; other authors declare no competing interests., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: Group A Streptococcus (GAS) causes an estimated 5.2 million outpatient visits for pharyngitis annually in the United States, with incidence peaking in winter, but the annual spatiotemporal pattern of GAS pharyngitis across the United States is poorly characterized., Methods: We used outpatient claims data from individuals with private medical insurance between 2010 and 2018 to quantify GAS pharyngitis visit rates across U.S. census regions, subregions, and states. We evaluated seasonal and age-based patterns of geographic spread and the association between school start dates and the summertime upward inflection in GAS visits., Results: The South had the most visits per person (yearly average, 39.11 visits per 1000 people; 95% confidence interval, 36.21-42.01) and the West had the fewest (yearly average, 17.63 visits per 1000 people; 95% confidence interval, 16.76-18.49). Visits increased earliest in the South and in school-age children. Differences in visits between the South and other regions were most pronounced in the late summer through early winter. Visits peaked earliest in central southern states, in December to January, and latest on the coasts, in March. The onset of the rise in GAS pharyngitis visits correlated with, but preceded, average school start times., Conclusions: The burden and timing of GAS pharyngitis varied across the continental United States, with the South experiencing the highest overall rates and earliest onset and peak in outpatient visits. Understanding the drivers of these regional differences in GAS pharyngitis will help in identifying and targeting prevention measures., Competing Interests: Potential conflicts of interest. L. K. W. reports consulting fees from Pacific Life Re and WHO, honoraria from Lancet Infectious Diseases, Luxembourg National Research Fund, and participation on Research Steering Group for Wellcome Trust. Y. H. G. reports grants or contracts from NIH and the Broad Institute and consulting fees from and participation on Scientific Advisory Boards for Day Zero Diagnostics and Decoy Therapeutics. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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., Competing Interests: SWO and CD are former employees of Biobot Analytics, Inc., where CD also holds stocks and bonds. YHG is a board member of Day Zero Diagnostics and an advisor/consultant to GSK, plc., (Copyright: © 2024 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2024 The Author(s).)

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., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

SARS-CoV-2 antibody levels may serve as a correlate for immunity and could inform optimal booster timing. The relationship between antibody levels and protection from infection was evaluated in vaccinated individuals from the US National Basketball Association who had antibody levels measured at a single time point from September 12, 2021, to December 31, 2021. Cox proportional hazards models were used to estimate the risk of infection within 90 days of serologic testing by antibody level (<250, 250-800, and >800 AU/mL 1 ), adjusting for age, time since last vaccine dose, and history of SARS-CoV-2 infection. Individuals were censored on date of booster receipt. The analytic cohort comprised 2323 individuals and was 78.2% male, 68.1% aged ≤40 years, and 56.4% vaccinated (primary series) with the Pfizer-BioNTech mRNA vaccine. Among the 2248 (96.8%) individuals not yet boosted at antibody testing, 77% completed their primary vaccine series 4-6 months before testing and the median (interquartile range) antibody level was 293.5 (interquartile range: 121.0-740.5) AU/mL. Those with levels <250 AU/mL (adj hazard ratio [HR]: 2.4; 95% confidence interval [CI]: 1.5-3.7) and 250-800 AU/mL (adj HR: 1.5; 95% CI: 0.98-2.4) had greater infection risk compared to those with levels >800 AU/mL. Antibody levels could inform individual COVID-19 risk and booster scheduling., (© 2024 Wiley Periodicals LLC.)

Rapid point-of-care tests that diagnose gonococcal infections and identify susceptibility to antibiotics enable individualized treatment. This could improve patient outcomes and slow the emergence and spread of antibiotic resistance. However, little is known about the long-term impact of such diagnostics on the burden of gonorrhea and the effective life span of antibiotics. We used a mathematical model of gonorrhea transmission among men who have sex with men in the United States to project the annual rate of reported gonorrhea cases and the effective life span of ceftriaxone, the recommended antibiotic for first-line treatment of gonorrhea, as well as 2 previously recommended antibiotics, ciprofloxacin and tetracycline, when a rapid drug susceptibility test that estimates susceptibility to ciprofloxacin and tetracycline is available. The use of a rapid drug susceptibility test with ≥50% sensitivity and ≥95% specificity, defined in terms of correct ascertainment of drug susceptibility and nonsusceptibility status, could increase the combined effective life span of ciprofloxacin, tetracycline, and ceftriaxone by at least 2 years over 25 years of simulation. If test specificity is imperfect, however, the increase in the effective life span of antibiotics is accompanied by an increase in the rate of reported gonorrhea cases even under perfect sensitivity., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Importance: Multiple therapies are available for outpatient treatment of COVID-19 that are highly effective at preventing hospitalization and mortality. Although racial and socioeconomic disparities in use of these therapies have been documented, limited evidence exists on what factors explain differences in use and the potential public health relevance of these differences., Objective: To assess COVID-19 outpatient treatment utilization in the Medicare population and simulate the potential outcome of allocating treatment according to patient risk for severe COVID-19., Design, Setting, and Participants: This cross-sectional study included patients enrolled in Medicare in 2022 across the US, identified with 100% Medicare fee-for-service claims., Main Outcomes and Measures: The primary outcome was any COVID-19 outpatient therapy utilization. Secondary outcomes included COVID-19 testing, ambulatory visits, and hospitalization. Differences in outcomes were estimated based on patient demographics, treatment contraindications, and a composite risk score for mortality after COVID-19 based on demographics and comorbidities. A simulation of reallocating COVID-19 treatment, particularly with nirmatrelvir, to those at high risk of severe disease was performed, and the potential COVID-19 hospitalizations and mortality outcomes were assessed., Results: In 2022, 6.0% of 20 026 910 beneficiaries received outpatient COVID-19 treatment, 40.5% of which had no associated COVID-19 diagnosis within 10 days. Patients with higher risk for severe disease received less outpatient treatment, such as 6.4% of those aged 65 to 69 years compared with 4.9% of those 90 years and older (adjusted odds ratio [aOR], 0.64 [95% CI, 0.62-0.65]) and 6.4% of White patients compared with 3.0% of Black patients (aOR, 0.56 [95% CI, 0.54-0.58]). In the highest COVID-19 severity risk quintile, 2.6% were hospitalized for COVID-19 and 4.9% received outpatient treatment, compared with 0.2% and 7.5% in the lowest quintile. These patterns were similar among patients with a documented COVID-19 diagnosis, those with no claims for vaccination, and patients who are insured with Medicare Advantage. Differences were not explained by variable COVID-19 testing, ambulatory visits, or treatment contraindications. Reallocation of 2022 outpatient COVID-19 treatment, particularly with nirmatrelvir, based on risk for severe COVID-19 would have averted 16 503 COVID-19 deaths (16.3%) in the sample., Conclusion: In this cross-sectional study, outpatient COVID-19 treatment was disproportionately accessed by beneficiaries at lower risk for severe infection, undermining its potential public health benefit. Undertreatment was not driven by lack of clinical access or treatment contraindications.

Background: The National Basketball Association (NBA) suspended operations in response to the COVID-19 pandemic in March 2020. To safely complete the 2019-20 season, the NBA created a closed campus in Orlando, Florida, known as the NBA "Bubble." More than 5000 individuals lived, worked, and played basketball at a time of high local prevalence of SARS-CoV-2., Methods: Stringent protocols governed campus life to protect NBA and support personnel from contracting COVID-19. Participants quarantined before departure and upon arrival. Medical and social protocols required that participants remain on campus, test regularly, physically distance, mask, use hand hygiene, and more. Cleaning, disinfection, and air filtration was enhanced. Campus residents were screened daily and confirmed cases of COVID-19 were investigated., Results: In the Bubble population, 148 043 COVID-19 reverse transcriptase PCR (RT-PCR) tests were performed across approximately 5000 individuals; Orlando had a 4% to 15% test positivity rate in this timeframe. There were 44 COVID-19 cases diagnosed either among persons during arrival quarantine or in non-team personnel while working on campus after testing but before receipt of a positive result. No cases of COVID-19 were identified among NBA players or NBA team staff living in the Bubble once cleared from quarantine., Conclusions: Drivers of success included the requirement for players and team staff to reside and remain on campus, well-trained compliance monitors, unified communication, layers of protection between teams and the outside, activation of high-quality laboratory diagnostics, and available mental health services. An emphasis on data management, evidence-based decision-making, and the willingness to evolve protocols were instrumental to successful operations. These lessons hold broad applicability for future pandemic preparedness efforts., (© Association for Diagnostics & Laboratory Medicine 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: S.S.H. has conducted studies in which participating nursing homes and hospitalized patients receive antiseptic and/or cleaning products from Xttrium Laboratories and Medline Industries. J.A.M. has served as a scientific consultant for Thermo Fisher Scientific and promotional speaker for Abbvie. M.R.A.S. is an employee at Day Zero Diagnostics, Boston., (Copyright: © 2023 Ojala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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 11 th , 2020, and July 28 th , 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., (© 2023. Springer Nature Limited.)

Background: Gonorrhoea is a highly prevalent sexually transmitted infection and an urgent public health concern because of increasing antibiotic resistance in Neisseria gonorrhoeae. Only ceftriaxone remains as the recommended treatment in the USA. With the prospect of new anti-gonococcal antibiotics being approved, we aimed to evaluate how to deploy a new drug to maximise its clinically useful lifespan., Methods: We used a compartmental model of gonorrhoea transmission in a US population of men who have sex with men (MSM) to compare strategies for introducing a new antibiotic for gonorrhoea treatment. The MSM population was stratified into three sexual activity groups (low, intermediate, and high) characterised by annual rates of partner change. The four introduction strategies tested were: (1) random 50-50 allocation, where each treatment-seeking infected individual had a 50% probability of receiving either drug A (current drug; a ceftriaxone-like antibiotic) or drug B (a new antibiotic), effective at time 0; (2) combination therapy of both the current drug and the new antibiotic; (3) reserve strategy, by which the new antibiotic was held in reserve until the current therapy reached a 5% threshold prevalence of resistance; and (4) gradual switch, or the gradual introduction of the new drug until random 50-50 allocation was reached. The primary outcome of interest was the time until 5% prevalence of resistance to each of the drugs (the new drug and the current ceftriaxone-like antibiotic); sensitivity of the primary outcome to the properties of the new antibiotic, specifically the probability of resistance emergence after treatment and the fitness costs of resistance, was explored. Secondary outcomes included the time to a 1% resistance threshold for each drug, as well as population-level prevalence, mean and range annual incidence, and the cumulative number of incident gonococcal infections., Findings: Under baseline model conditions, a 5% prevalence of resistance to each of drugs A and B was reached within 13·9 years with the reserve strategy, 18·2 years with the gradual switch strategy, 19·2 years with the random 50-50 allocation strategy, and 19·9 years with the combination therapy strategy. The reserve strategy was consistently inferior for mitigating antibiotic resistance under the parameter space explored and was increasingly outperformed by the other strategies as the probability of de novo resistance emergence decreased and as the fitness costs associated with resistance increased. Combination therapy tended to prolong the development of antibiotic resistance and minimise the number of annual gonococcal infections (under baseline model conditions, mean number of incident infections per year 178 641 [range 177 998-181 731] with combination therapy, 180 084 [178 011-184 405] with the reserve strategy)., Interpretation: Our study argues for rapid introduction of new anti-gonococcal antibiotics, recognising that the feasibility of each strategy must incorporate cost, safety, and other practical concerns. The analyses should be revisited once robust estimates of key parameters-ie, the likelihood of emergence of resistance and fitness costs of resistance for the new antibiotic-are available., Funding: US Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases., Competing Interests: Declaration of interests YHG reports funding for this work from the US Centers for Disease Control and Prevention (CDC; contract 200-2016-91779) and from the National Institute of Allergy and Infectious Diseases (NIAID; grants R01AI132606 and R01AI153521), as well as consulting fees from GlaxoSmithKline outside the scope of the current work. MMR and JAS report funding support from the CDC and the National Center for HIV, Viral Hepatitis, STD, and TB Prevention Epidemiologic and Economic Modeling Agreement (5NU38PS004651RY) for the current work. MMR also receives funding from WHO, the Harvard University Center for AIDS Research Developmental Grant, and Harvard Data Science Exploratory Award all outside the scope of the current work. RY reports R01AI153351 grant funding from NIAID for the current work. TLG and ER declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Pre-existing tetracycline resistance in Neisseria gonorrhoeae limits the effectiveness of post-exposure prophylaxis (PEP) with doxycycline against gonorrhea, and selection for tetracycline resistance may influence prevalence of multi-drug resistant strains. Using genomic and antimicrobial susceptibility data from N. gonorrhoeae, we assessed the near-term impact of doxycycline PEP on N. gonorrhoeae resistance., Competing Interests: Potential conflicts of interest. Y. H. G. has received consulting fees from GSK and serves on the advisory board for Day Zero Diagnostics. Y. H. G. also reports grants or contracts from Wellcome Trust and Smith Family Foundation. T. D. M. reports no potential conflicts. Both authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., Competing Interests: The authors declare no conflict of interest.

Increasing levels of antibiotic resistance in many bacterial pathogen populations are a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria are exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations., (© 2023. The Author(s).)

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., (© 2023. The Author(s).)

Background: Neisseria gonorrhoeae is a major public health problem due to increasing incidence and antimicrobial resistance. Genetic markers of reduced susceptibility have been identified; the extent to which those are representative of global antimicrobial resistance is unknown. We evaluated the performance of whole-genome sequencing (WGS) used to predict susceptibility to ciprofloxacin and other antimicrobials using a global collection of N. gonorrhoeae isolates., Methods: Susceptibility testing of common antimicrobials and the recently developed zolifodacin was performed using agar dilution to determine minimum inhibitory concentrations (MICs). We identified resistance alleles at loci known to contribute to antimicrobial resistance in N. gonorrhoeae from WGS data. We tested the ability of each locus to predict antimicrobial susceptibility., Results: A total of 481 N. gonorrhoeae isolates, collected between 2004 and 2019 and making up 457 unique genomes, were sourced from 5 countries. All isolates with demonstrated susceptibility to ciprofloxacin (MIC ≤0.06 μg/mL) had a wild-type gyrA codon 91. Multilocus approaches were needed to predict susceptibility to other antimicrobials. All isolates were susceptible to zoliflodacin, defined by an MIC ≤0.25 μg/mL., Conclusions: Single marker prediction can be used to inform ciprofloxacin treatment of N. gonorrhoeae infection. A combination of molecular markers may be needed to determine susceptibility for other antimicrobials., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The aetiological bacterial agent of gonorrhoea, Neisseria gonorrhoeae, has become resistant to each of the first-line antibiotics used to treat it, including ciprofloxacin. One diagnostic approach to identify ciprofloxacin-susceptible isolates is to determine codon 91 in the gene encoding the A subunit of DNA gyrase, gyrA, where coding for the wild-type serine (gyrA 91S ) is associated with ciprofloxacin susceptibility and phenylalanine (gyrA 91F ) with resistance. The aim of this study was to investigate the possibility of diagnostic escape from gyrA susceptibility testing., Methods: We used bacterial genetics to introduce pairwise substitutions in GyrA positions 91 (S or F) and 95 (D, G, or N), which is a second site in GyrA associated with ciprofloxacin resistance, into five clinical isolates of N gonorrhoeae. All five isolates encoded GyrA S91F, an additional substitution in GyrA at position 95, substitutions in ParC that are known to cause an increased minimum inhibitory concentration (MIC) to ciprofloxacin, and GyrB 429D, which is associated with susceptibility to zoliflodacin (a spiropyrimidinetrione-class antibiotic in phase 3 trials for treatment of gonorrhoea). We evolved these isolates to assess for the existence of pathways to ciprofloxacin resistance (MIC ≥1 μg/mL) and measured MICs for ciprofloxacin and zoliflodacin. In parallel, we searched metagenomic data for 11 355 N gonorrhoeae clinical isolates with reported ciprofloxacin MICs that were publicly available from the European Nucleotide Archive for strains that would be identified as susceptible by gyrA codon 91-based assays., Findings: Three clinical isolates of N gonorrhoeae with substitutions in GyrA position 95 associated with resistance (G or N) maintained intermediate ciprofloxacin MICs (0·125-0·5 μg/mL), which has been associated with treatment failure, despite reversion of GyrA position 91 from phenylalanine to serine. From an in-silico analysis of the 11 355 genomes from N gonorrhoeae clinical isolates, we identified 30 isolates with gyrA codon 91 encoding a serine and a ciprofloxacin resistance-associated mutation at codon 95. The reported MICs for these isolates varied from 0·023 μg/mL to 0·25 μg/mL, including four with intermediate ciprofloxacin MICs (associated with substantially increased risk of treatment failure). Finally, through experimental evolution, one clinical isolate of N gonorrhoeae bearing GyrA 91S acquired ciprofloxacin resistance through mutations in the gene encoding for the B subunit of DNA gyrase (gyrB) that also conferred reduced susceptibility to zoliflodacin (ie, MIC ≥2 μg/mL)., Interpretation: Diagnostic escape from gyrA codon 91 diagnostics could occur through either reversion of the gyrA allele or expansion of circulating lineages. N gonorrhoeae genomic surveillance efforts might benefit from including gyrB, given its potential for contributing to ciprofloxacin and zoliflodacin resistance, and diagnostic strategies that reduce the likelihood of escape, such as the incorporation of multiple target sites, should be investigated. Diagnostics that guide antibiotic therapy can have unintended consequences, including novel resistance determinants and antibiotic cross-resistance., Funding: US National Institutes of Health National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and the Smith Family Foundation., Competing Interests: Declaration of interests YHG receives or has received support from Wellcome Trust, Pfizer, and Merck; receives or has received consulting fees from GlaxoSmithKline, Quidel, and the National Basketball Association; receives or has received payment for expert testimony from Merck; and serves on the advisory board for Day Zero Diagnostics. DHFR and TDM declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: To describe the genomic analysis and epidemiologic response related to a slow and prolonged methicillin-resistant Staphylococcus aureus (MRSA) outbreak., Design: Prospective observational study., Setting: Neonatal intensive care unit (NICU)., Methods: We conducted an epidemiologic investigation of a NICU MRSA outbreak involving serial baby and staff screening to identify opportunities for decolonization. Whole-genome sequencing was performed on MRSA isolates., Results: A NICU with excellent hand hygiene compliance and longstanding minimal healthcare-associated infections experienced an MRSA outbreak involving 15 babies and 6 healthcare personnel (HCP). In total, 12 cases occurred slowly over a 1-year period (mean, 30.7 days apart) followed by 3 additional cases 7 months later. Multiple progressive infection prevention interventions were implemented, including contact precautions and cohorting of MRSA-positive babies, hand hygiene observers, enhanced environmental cleaning, screening of babies and staff, and decolonization of carriers. Only decolonization of HCP found to be persistent carriers of MRSA was successful in stopping transmission and ending the outbreak. Genomic analyses identified bidirectional transmission between babies and HCP during the outbreak., Conclusions: In comparison to fast outbreaks, outbreaks that are "slow and sustained" may be more common to units with strong existing infection prevention practices such that a series of breaches have to align to result in a case. We identified a slow outbreak that persisted among staff and babies and was only stopped by identifying and decolonizing persistent MRSA carriage among staff. A repeated decolonization regimen was successful in allowing previously persistent carriers to safely continue work duties.

Background: In the United States, children aged <5 years receive high volumes of antibiotics, which may contribute to antibiotic resistance. It has been unclear what role preventable illnesses and chronic comorbidities play in prompting antibiotic prescriptions., Methods: We conducted an observational study with a cohort of 124 759 children aged <5 years born in the United States between 2008 and 2013 with private medical insurance. Study outcomes included the cumulative number of antibiotic courses dispensed per child by age 5 and the proportion of children for whom at least 1 antibiotic course was dispensed by age 5. We identified which chronic medical conditions predicted whether a child would be among the top 20% of antibiotic recipients., Results: Children received a mean of 6.8 (95% confidence interval [CI]: 6.7-6.9) antibiotic courses by age 5, and 91% (95% CI: 90%-92%) of children had received at least 1 antibiotic course by age 5. Most antibiotic courses (71%; 95% CI: 70%-72%) were associated with respiratory infections. Presence of a pulmonary/respiratory, otologic, and/or immunological comorbidity substantially increase a child's odds of being in the top 20% of antibiotic recipients. Children with at least 1 of these conditions received a mean of 10.5 (95% CI: 10.4-10.6) antibiotic courses by age 5., Conclusions: Privately insured children in the United States receive many antibiotics early in life, largely due to respiratory infections. Antibiotic dispensing varies widely among children, with more antibiotics dispensed to children with pulmonary/respiratory, otologic, and/or immunological comorbidities., Competing Interests: Potential conflicts of interest. A. M. reports grants or contracts from the Commonwealth Fund and consulting fees and support for attending meetings and/or travel from the Pew Charitable Trust. Y. H. G. reports grants or contracts from the National Institutes of Health (to institution); consulting fees from GSK and Merck (paid to author); and is a member of the Day Zero Diagnostics Scientific and Clinical Advisory Boards. All remaining authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Neisseria gonorrhoeae is an urgent public health threat due to the emergence of antibiotic resistance. As most isolates in the United States are susceptible to at least one antibiotic, rapid molecular antimicrobial susceptibility tests (ASTs) would offer the opportunity to tailor antibiotic therapy, thereby expanding treatment options. With genome sequence and antibiotic resistance phenotype data for nearly 20,000 clinical N. gonorrhoeae isolates now available, there is an opportunity to use statistical methods to develop sequence-based diagnostics that predict antibiotic susceptibility from genotype. N. gonorrhoeae, therefore, provides a useful example illustrating how to apply machine learning models to aid in the design of sequence-based ASTs. We present an overview of this framework, which begins with establishing the assay technology, the performance criteria, the population in which the diagnostic will be used, and the clinical goals, and extends to the choices that must be made to arrive at a set of features with the desired properties for predicting susceptibility phenotype from genotype. While we focus on the example of N. gonorrhoeae, the framework generalizes to other organisms for which large-scale genotype and antibiotic resistance data can be combined to aid in diagnostics development., (© 2022 New York Academy of Sciences.)

The CLEAR Trial recently found that decolonization reduced infections and hospitalizations in MRSA carriers in the year following hospital discharge. In this secondary analysis, we explored whether decolonization had a similar benefit in the subgroup of trial participants who harbored USA300, using two different definitions for the USA300 strain-type.

The evolution of the obligate human pathogen Neisseria gonorrhoeae has been shaped by selective pressures from diverse host niche environments and antibiotics. The varying prevalence of antibiotic resistance across N. gonorrhoeae lineages suggests that underlying metabolic differences may influence the likelihood of acquisition of specific resistance mutations. We hypothesized that the requirement for supplemental CO 2 , present in approximately half of isolates, reflects one such example of metabolic variation. Here, using a genome-wide association study and experimental investigations, we show that CO 2 dependence is attributable to a single substitution in a β-carbonic anhydrase, CanB. CanB 19E is necessary and sufficient for growth in the absence of CO 2 , and the hypomorphic CanB 19G variant confers CO 2 dependence. Furthermore, ciprofloxacin resistance is correlated with CanB 19G in clinical isolates, and the presence of CanB 19G increases the likelihood of acquisition of ciprofloxacin resistance. Together, our results suggest that metabolic variation has affected the acquisition of fluoroquinolone resistance., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Incomplete selection makes it challenging to infer selection on genes at short time scales, especially for microorganisms, due to stronger linkage between loci. However, in many cases, the selective force changes with environment, time, or other factors, and it is of great interest to understand selective forces at this level to answer relevant biological questions. We developed a new method that uses the change in d N / d S , instead of the absolute value of d N / d S , to infer the dominating selective force based on sequence data across geographical scales. If a gene was under positive selection, d N / d S was expected to increase through time, whereas if a gene was under negative selection, d N / d S was expected to decrease through time. Assuming that the migration rate decreased and the divergence time between samples increased from between-continent, within-continent different-country, to within-country level, d N / d S of a gene dominated by positive selection was expected to increase with increasing geographical scales, and the opposite trend was expected in the case of negative selection. Motivated by the McDonald-Kreitman (MK) test, we developed a pairwise MK test to assess the statistical significance of detected trends in d N / d S . Application of the method to a global sample of dengue virus genomes identified multiple significant signatures of selection in both the structural and non-structural proteins. Because this method does not require allele frequency estimates and uses synonymous mutations for comparison, it is less prone to sampling error, providing a way to infer selection forces within species using publicly available genomic data from locations over broad geographical scales., (© The Author(s) 2022. Published by Oxford University Press.)

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 <30) 5 days post detection, with small differences across variants and vaccination status. Only seven viral rebounds (0.7%; N=999) were observed, with rebound defined as 3+days with Ct <30 following an initial clearance of 3+days with Ct ≥30. High antibody titers against the founder SARS-CoV-2 strain predicted lower peak viral loads and shorter durations of infection. Among Omicron BA.1 infections, boosted individuals had lower pre-booster antibody titers and longer clearance times than non-boosted individuals., Conclusions: SARS-CoV-2 viral kinetics are partly determined by immunity and variant but dominated by individual-level variation. Since booster vaccination protects against infection, longer clearance times for BA.1-infected, boosted individuals may reflect a less effective immune response, more common in older individuals, that increases infection risk and reduces viral RNA clearance rate. The shifting landscape of viral kinetics underscores the need for continued monitoring to optimize isolation policies and to contextualize the health impacts of therapeutics and vaccines., Funding: Supported in part by CDC contract #200-2016-91779, a sponsored research agreement to Yale University from the National Basketball Association contract #21-003529, and the National Basketball Players Association., Competing Interests: JH, MN, YH, DH No competing interests declared, SK SMK has a consulting agreement with the NBA, JF, NG has a consulting agreement for Tempus and receives financial support from Tempus to develop SARS-CoV-2 diagnostic tests, CM, CT, RS, SC is an employee of IQVIA, Real World Solutions, DA is co-owner of Infection Control Education for Major Sports, GK, MM, MP, SK, AM, IE, DS, TB, BH, JD, CM is an employee of Tempus Labs, JD is an employee of the NBA, YG has a consulting agreement with the NBA, (© 2022, Hay, Kissler, Fauver et al.)

Background: Estimating the cumulative incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for setting public health policies. We leveraged deidentified Massachusetts newborn screening specimens as an accessible, retrospective source of maternal antibodies for estimating statewide seroprevalence in a nontest-seeking population., Methods: We analyzed 72 117 newborn specimens collected from November 2019 through December 2020, representing 337 towns and cities across Massachusetts. Seroprevalence was estimated for the Massachusetts population after correcting for imperfect test specificity and nonrepresentative sampling using Bayesian multilevel regression and poststratification., Results: Statewide seroprevalence was estimated to be 0.03% (90% credible interval [CI], 0.00-0.11) in November 2019 and rose to 1.47% (90% CI: 1.00-2.13) by May 2020, following sustained SARS-CoV-2 transmission in the spring. Seroprevalence plateaued from May onward, reaching 2.15% (90% CI: 1.56-2.98) in December 2020. Seroprevalence varied substantially by community and was particularly associated with community percent non-Hispanic Black (β = .024; 90% CI: 0.004-0.044); i.e., a 10% increase in community percent non-Hispanic Black was associated with 27% higher odds of seropositivity. Seroprevalence estimates had good concordance with reported case counts and wastewater surveillance for most of 2020, prior to the resurgence of transmission in winter., Conclusions: Cumulative incidence of SARS-CoV-2 protective antibody in Massachusetts was low as of December 2020, indicating that a substantial fraction of the population was still susceptible. Maternal seroprevalence data from newborn screening can inform longitudinal trends and identify cities and towns at highest risk, particularly in settings where widespread diagnostic testing is unavailable., (© The Author(s) 2022. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

A consensus methodology for the pharmacometric assessment of candidate SARS-CoV-2 antiviral drugs would be useful for comparing trial results and improving trial design. The time to viral clearance, assessed by serial qPCR of nasopharyngeal swab samples, has been the most widely reported measure of virological response in clinical trials, but it has not been compared formally with other metrics, notably model-based estimates of the rate of viral clearance. We analyzed prospectively gathered viral clearance profiles from 280 infection episodes in vaccinated and unvaccinated individuals. We fitted different phenomenological pharmacodynamic models (single exponential decay, bi-exponential, penalized splines) and found that the clearance rate, estimated from a mixed effects single exponential decay model, is a robust pharmacodynamic summary of viral clearance. The rate of viral clearance, estimated from viral densities during the first week following peak viral load, provides increased statistical power (reduced type 2 error) compared with time to clearance. Antiviral effects approximately equivalent to those with currently used and recommended SARS-CoV-2 antiviral treatments, notably nirmatrelvir and molnupiravir, can be detected from randomized trials with sample sizes of only 35 to 65 patients per arm. We recommend that pharmacometric antiviral assessments should be conducted in early COVID-19 illness with serial qPCR samples taken over 1 week.

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.

Methicillin-resistant Staphylococcus aureus (MRSA) can colonize multiple body sites, and carriage is a risk factor for infection. Successful decolonization protocols reduce disease incidence; however, multiple protocols exist, comprising diverse therapies targeting multiple body sites, and the optimal protocol is unclear. Standard methods cannot infer the impact of site-specific components on successful decolonization. Here, we formulate a Bayesian coupled hidden Markov model, which estimates interactions between body sites, quantifies the contribution of each therapy to successful decolonization, and enables predictions of the efficacy of therapy combinations. We applied the model to longitudinal data from a randomized controlled trial (RCT) of an MRSA decolonization protocol consisting of chlorhexidine body and mouthwash and nasal mupirocin. Our findings (i) confirmed nares as a central hub for MRSA colonization and nasal mupirocin as the most crucial therapy and (ii) demonstrated all components contributed significantly to the efficacy of the protocol and the protocol reduced self-inoculation. Finally, we assessed the impact of hypothetical therapy improvements in silico and found that enhancing MRSA clearance at the skin would yield the largest gains. This study demonstrates the use of advanced modelling to go beyond what is typically achieved by RCTs, enabling evidence-based decision-making to streamline clinical protocols.