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Background: Immunocompromised patients receiving B-cell-depleting therapies are at increased risk of persistent SARS-CoV-2 infection, with many experiencing fatal outcomes. We report a successful outcome in a patient with rheumatoid arthritis (RA) on rituximab diagnosed with COVID-19 in July 2020 with persistent infection for over 245 days. Results: The patient received numerous treatment courses for persistent COVID-19 infection, including remdesivir, baricitinib, immunoglobulin and high doses of corticosteroids followed by a prolonged taper due to persistent respiratory symptoms and cryptogenic organizing pneumonia. Her clinical course was complicated by Pseudomonas aeruginosa sinusitis with secondary bacteremia, and cytomegalovirus (CMV) viremia and pneumonitis. SARS-CoV-2 positive RNA samples were extracted from two nasopharyngeal swabs and sequenced using targeted amplicon Next-Generation Sequencing which were analyzed for virus evolution over time. Viral sequencing indicated lineage B.1.585.3 SARS-CoV-2 accumulated Spike protein mutations associated with immune evasion and resistance to therapeutics. Upon slowly decreasing the patient's steroids, she had resolution of her symptoms and had a negative nasopharyngeal SARS-CoV-2 PCR and serum CMV PCR in March 2021. Conclusion: A patient with RA on B-cell depleting therapy developed persistent SARS-CoV-2 infection allowing for virus evolution and had numerous complications, including viral and bacterial co-infections with opportunistic pathogens. Despite intra-host evolution with a more immune evasive SARS-CoV-2 lineage, it was cleared after 245 days with reconstitution of the patient's immune system.
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OBJECTIVE: We studied the extent of carbapenemase-producing Enterobacteriaceae (CPE) sink contamination and transmission to patients in a nonoutbreak setting. METHODS: During 2017-2019, 592 patient-room sinks were sampled in 34 departments. Patient weekly rectal swab CPE surveillance was universally performed. Repeated sink sampling was conducted in 9 departments. Isolates from patients and sinks were characterized using pulsed-field gel electrophoresis (PFGE), and pairs of high resemblance were sequenced by Oxford Nanopore and Illumina. Hybrid assembly was used to fully assemble plasmids, which are shared between paired isolates. RESULTS: In total, 144 (24%) of 592 CPE-contaminated sinks were detected in 25 of 34 departments. Repeated sampling (n = 7,123) revealed that 52%-100% were contaminated at least once during the sampling period. Persistent contamination for >1 year by a dominant strain was common. During the study period, 318 patients acquired CPE. The most common species were Klebsiella pneumoniae, Escherichia coli, and Enterobacter spp. In 127 (40%) patients, a contaminated sink was the suspected source of CPE acquisition. For 20 cases with an identical sink-patient strain, temporal relation suggested sink-to-patient transmission. Hybrid assembly of specific sink-patient isolates revealed that shared plasmids were structurally identical, and SNP differences between shared pairs, along with signatures for potential recombination events, suggests recent sharing of the plasmids. CONCLUSIONS: CPE-contaminated sinks are an important source of transmission to patients. Although traditionally person-to-person transmission has been considered the main route of CPE transmission, these data suggest a change in paradigm that may influence strategies of preventing CPE dissemination.
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Enterobacteriaceae Resistentes a los Carbapenémicos , Infecciones por Enterobacteriaceae , Humanos , Enterobacteriaceae Resistentes a los Carbapenémicos/genética , Enterobacteriaceae , beta-Lactamasas/genética , Proteínas Bacterianas/genética , Klebsiella pneumoniae/genética , Escherichia coli , Infecciones por Enterobacteriaceae/epidemiologíaRESUMEN
Researchers and policymakers have proposed systems to detect novel pathogens earlier than existing surveillance systems by monitoring samples from hospital patients, wastewater, and air travel, in order to mitigate future pandemics. How much benefit would such systems offer? We developed, empirically validated, and mathematically characterized a quantitative model that simulates disease spread and detection time for any given disease and detection system. We find that hospital monitoring could have detected COVID-19 in Wuhan 0.4 weeks earlier than it was actually discovered, at 2,300 cases (standard error: 76 cases) compared to 3,400 (standard error: 161 cases). Wastewater monitoring would not have accelerated COVID-19 detection in Wuhan, but provides benefit in smaller catchments and for asymptomatic or long-incubation diseases like polio or HIV/AIDS. Air travel monitoring does not accelerate outbreak detection in most scenarios we evaluated. In sum, early detection systems can substantially mitigate some future pandemics, but would not have changed the course of COVID-19.
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Viaje en Avión , COVID-19 , Humanos , COVID-19/diagnóstico , COVID-19/epidemiología , COVID-19/prevención & control , Pandemias/prevención & control , SARS-CoV-2 , Aguas ResidualesRESUMEN
Identifying and interrupting transmission chains is important for controlling infectious diseases. One way to identify transmission pairs - two hosts in which infection was transmitted from one to the other - is using the variation of the pathogen within each single host (within-host variation). However, the role of such variation in transmission is understudied due to a lack of experimental and clinical datasets that capture pathogen diversity in both donor and recipient hosts. In this work, we assess the utility of deep-sequenced genomic surveillance (where genomic regions are sequenced hundreds to thousands of times) using a mouse transmission model involving controlled spread of the pathogenic bacterium Citrobacter rodentium from infected to naïve female animals. We observe that within-host single nucleotide variants (iSNVs) are maintained over multiple transmission steps and present a model for inferring the likelihood that a given pair of sequenced samples are linked by transmission. In this work we show that, beyond the presence and absence of within-host variants, differences arising in the relative abundance of iSNVs (allelic frequency) can infer transmission pairs more precisely. Our approach further highlights the critical role bottlenecks play in reserving the within-host diversity during transmission.
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Variación Genética , Genómica , Animales , Femenino , Frecuencia de los Genes , Bacterias , Secuenciación de Nucleótidos de Alto RendimientoRESUMEN
Researchers and policymakers have proposed systems to detect novel pathogens earlier than existing surveillance systems by monitoring samples from hospital patients, wastewater, and air travel, in order to mitigate future pandemics. How much benefit would such systems offer? We developed, empirically validated, and mathematically characterized a quantitative model that simulates disease spread and detection time for any given disease and detection system. We find that hospital monitoring could have detected COVID-19 in Wuhan 0.4 weeks earlier than it was actually discovered, at 2,300 cases (standard error: 76 cases) compared to 3,400 (standard error: 161 cases). Wastewater monitoring would not have accelerated COVID-19 detection in Wuhan, but provides benefit in smaller catchments and for asymptomatic or long-incubation diseases like polio or HIV/AIDS. Monitoring of air travel provides little benefit in most scenarios we evaluated. In sum, early detection systems can substantially mitigate some future pandemics, but would not have changed the course of COVID-19.
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While some studies have previously estimated lives saved by COVID-19 vaccination, we estimate how many deaths could have been averted by vaccination in the US but were not because of a failure to vaccinate. We used a simple method based on a nationally representative dataset to estimate the preventable deaths among unvaccinated individuals in the US from May 30, 2021 to September 3, 2022 adjusted for the effects of age and time. We estimated that at least 232,000 deaths could have been prevented among unvaccinated adults during the 15 months had they been vaccinated with at least a primary series. While uncertainties exist regarding the exact number of preventable deaths and more granular data are needed on other factors causing differences in death rates between the vaccinated and unvaccinated groups to inform these estimates, this method is a rapid assessment on vaccine-preventable deaths due to SARS-CoV-2 that has crucial public health implications. The same rapid method can be used for future public health emergencies.
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COVID-19 , Adulto , Estados Unidos/epidemiología , Humanos , COVID-19/prevención & control , Vacunas contra la COVID-19 , SARS-CoV-2 , Vacunación , Salud PúblicaRESUMEN
Areal spatial misalignment, which occurs when data on multiple variables are collected using mismatched boundary definitions, is a ubiquitous obstacle to data analysis in public health and social science research. As one example, the emerging sub-field studying the links between political context and health in the United States faces significant spatial misalignment-related challenges, as the congressional districts (CDs) over which political metrics are measured and administrative units, e.g., counties, for which health data are typically released, have a complex misalignment structure. Standard population-weighted data realignment procedures can induce measurement error and invalidate inference, which has prompted the development of fully model-based approaches for analyzing spatially misaligned data. One such approach, atom-based regression models (ABRM), holds particular promise but has scarcely been used in practice due to the lack of appropriate software or examples of implementation. ABRM use "atoms", the areas created by intersecting all sets of units on which variables of interest are measured, as the units of analysis and build models for the atom-level data, treating the atom-level variables (generally unmeasured) as latent variables. In this paper, we demonstrate the feasibility and strengths of the ABRM in a case study of the association between political representatives' voting behavior (CD-level) and COVID-19 mortality rates (county-level) in a post-vaccine period. The adjusted ABRM results suggest that more conservative voting record is associated with an increase in COVID-19 mortality rates, with estimated associations smaller in magnitude but consistent in direction with those of standard realignment methods. The results also indicate that ABRM may enable more robust confounding adjustment and more realistic uncertainty estimates, properly representing the uncertainties arising from all analytic procedures. We also implement the ABRM in modern optimized Bayesian computing programs and make our code publicly available, which may enable these methods to be more widely adopted.
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We sought to explore the relationship between body mass index (BMI) and neurologic outcomes following acute COVID-19 infection. We conducted a retrospective electronic medical record-based cohort study enrolling adults with laboratory-confirmed acute COVID-19 infection who presented to 1 of 12 academic and community hospitals in Southwestern Ontario, Canada between April 1, 2020 and July 31, 2021. Primary subjective (anosmia, dysgeusia, and/or headache) and objective (aseptic meningitis, ataxia, delirium, encephalopathy, encephalitis, intracranial hemorrhage, ischemic stroke, and/or seizure) composite neurologic outcomes were assessed, comparing obese and overweight individuals to those with underweight/normal BMI indices, adjusting for baseline characteristics. Secondary outcomes (severity of illness, length of hospital stay, SARS-CoV-2 viral load, mortality) were similarly analyzed. A total of 1437 enrolled individuals, of whom 307 (21%), 456 (32%), and 674 (47%) were underweight/normal, overweight, and obese, respectively. On multivariable analysis, there was no association between BMI category and the composite outcome for subjective (odds ratio [OR] 1.17, 95% CI 0.84-1.64, Bonferroni p = 1.00 for obese; OR 1.02, 95% CI 0.70-1.48; Bonferroni p = 1.00 for overweight) and objective (OR 0.74, 95% CI 0.42-1.30, p = 0.29 for obese; OR = 0.80, 95% CI 0.45-1.43, p = 0.45 for overweight) neurologic manifestations. There was no association between BMI category and any secondary outcome measure and no evidence of effect modification by age or sex. This study demonstrates the absence of an association between BMI and neurologic manifestations following acute COVID-19 illness. Prospective studies using standardized data collection tools and direct measures of body fat are warranted to obtain more valid effect estimates.
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COVID-19 , Sobrepeso , Adulto , Humanos , Índice de Masa Corporal , Sobrepeso/complicaciones , COVID-19/complicaciones , Estudios Retrospectivos , Delgadez/complicaciones , Estudios Prospectivos , Estudios de Cohortes , SARS-CoV-2 , Obesidad/complicacionesRESUMEN
BACKGROUND: The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly transmissible in vaccinated and unvaccinated populations. The dynamics that govern its establishment and propensity toward fixation (reaching 100% frequency in the SARS-CoV-2 population) in communities remain unknown. Here, we describe the dynamics of Omicron at 3 institutions of higher education (IHEs) in the greater Boston area. METHODS: We use diagnostic and variant-specifying molecular assays and epidemiological analytical approaches to describe the rapid dominance of Omicron following its introduction into 3 IHEs with asymptomatic surveillance programs. RESULTS: We show that the establishment of Omicron at IHEs precedes that of the state and region and that the time to fixation is shorter at IHEs (9.5-12.5 days) than in the state (14.8 days) or region. We show that the trajectory of Omicron fixation among university employees resembles that of students, with a 2- to 3-day delay. Finally, we compare cycle threshold values in Omicron vs Delta variant cases on college campuses and identify lower viral loads among college affiliates who harbor Omicron infections. CONCLUSIONS: We document the rapid takeover of the Omicron variant at IHEs, reaching near-fixation within the span of 9.5-12.5 days despite lower viral loads, on average, than the previously dominant Delta variant. These findings highlight the transmissibility of Omicron, its propensity to rapidly dominate small populations, and the ability of robust asymptomatic surveillance programs to offer early insights into the dynamics of pathogen arrival and spread.
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COVID-19 , Humanos , COVID-19/epidemiología , SARS-CoV-2/genética , Universidades , BostonRESUMEN
Background: After introduction of pneumococcal conjugate vaccines (PCVs), serotype replacement occurred in the population of Streptococcus pneumoniae. Predicting which pneumococcal clones and serotypes will become more common in carriage after vaccination can enhance vaccine design and public health interventions, while also improving our understanding of pneumococcal evolution. We sought to use invasive disease data to assess how well negative frequency-dependent selection (NFDS) models could explain pneumococcal carriage population evolution in the post-PCV13 epoch by weighting invasive data to approximate strain proportions in the carriage population. Methods: Invasive pneumococcal isolates were collected and sequenced during 1998-2018 by the Active Bacterial Core surveillance (ABCs) from the Centers for Disease Control and Prevention (CDC). To predict the post-PCV13 population dynamics in the carriage population using a NFDS model, all genomic data were processed under a bioinformatic pipeline of assembly, annotation, and pangenome analysis to define genetically similar sequence clusters (i.e., strains) and a set of accessory genes present in 5% to 95% of the isolates. The NFDS model predicted the strain proportion by calculating the post-vaccine strain composition in the weighted invasive disease population that would best match pre-vaccine accessory gene frequencies. To overcome the biases of invasive disease data, serotype-specific inverse-invasiveness weights were defined as the ratio of the proportion of the serotype in the carriage data to the proportion in the invasive data, using data from 1998-2001 in the United States, before conjugate vaccine introduction. The weights were applied to adjust both the observed strain proportion and the accessory gene frequencies. Results: Inverse-invasiveness weighting increased the correlation of accessory gene frequencies between invasive and carriage data with reduced residuals in linear or logit scale for pre-vaccine, post-PCV7, and post-PCV13. Similarly, weighting increased the correlation of accessory gene frequencies between different time periods in the invasive data. By weighting the invasive data, we were able to use the NFDS model to predict strain proportions in the carriage population in the post-PCV13 epoch, with the adjusted R-squared between predicted and observed strain proportions increasing from 0.176 to 0.544 after weighting. Conclusions: The weighting system adjusted the invasive disease surveillance data to better represent the carriage population of S. pneumoniae. The NFDS mechanism predicted the strain proportions in the projected carriage population as estimated from the weighted invasive disease frequencies in the post-PCV13 epoch. Our methods enrich the value of genomic sequences from invasive disease surveillance, which is readily available, easy to collect, and of direct interest to public health.
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Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.
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COVID-19 , SARS-CoV-2 , Humanos , SARS-CoV-2/genética , Aguas Residuales , ARN Viral/genética , TranscriptomaRESUMEN
Background: Scant research, including in the United States, has quantified relationships between the political ideologies of elected representatives and COVID-19 outcomes among their constituents. Methods: We analyzed observational cross-sectional data on COVID-19 mortality rates (age-standardized) and stress on hospital intensive care unit (ICU) capacity for all 435 US Congressional Districts (CDs) in a period of adult vaccine availability (April 2021-March 2022). Political metrics comprised: (1) ideological scores based on each US Representative's and Senator's concurrent overall voting record and their specific COVID-19 votes, and (2) state trifectas (Governor, State House, and State Senate under the same political party control). Analyses controlled for CD social metrics, population density, vaccination rates, the prevalence of diabetes and obesity, and voter political lean. Findings: During the study period, the higher the exposure to conservatism across several political metrics, the higher the COVID-19 age-standardized mortality rates, even after taking into account the CD's social characteristics; similar patterns occurred for stress on hospital ICU capacity for Republican trifectas and US Senator political ideology scores. For example, in models mutually adjusting for CD political and social metrics and vaccination rates, Republican trifecta and conservative voter political lean independently remained significantly associated with an 11%-26% higher COVID-19 mortality rate. Interpretation: Associations between the political ideologies of US federal elected officials and state concentrations of political party power with population health warrant greater consideration in public health analyses and monitoring dashboards. Funding: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
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Human monkeypox is a viral zoonosis endemic to West and Central Africa that has recently generated increased interest and concern on a global scale as an emerging infectious disease threat in the midst of the slowly relenting COVID-2019 disease pandemic. The hallmark of infection is the development of a flu-like prodrome followed by the appearance of a smallpox-like exanthem. Precipitous person-to-person transmission of the virus among residents of 100 countries where it is nonendemic has motivated the immediate and widespread implementation of public health countermeasures. In this review, we discuss the origins and virology of monkeypox virus, its link with smallpox eradication, its record of causing outbreaks of human disease in regions where it is endemic in wildlife, its association with outbreaks in areas where it is nonendemic, the clinical manifestations of disease, laboratory diagnostic methods, case management, public health interventions, and future directions.
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COVID-19 , Mpox , Viruela , Humanos , Monkeypox virus , Mpox/diagnóstico , Mpox/epidemiología , COVID-19/epidemiología , África Central/epidemiologíaRESUMEN
Contact tracing and genomic data, approaches often used separately, have both been important tools in understanding the nature of SARS-CoV-2 transmission. Linked analysis of contact tracing and sequence relatedness of SARS-CoV-2 genomes from a regularly sampled university environment were used to build a multilevel transmission tracing and confirmation system to monitor and understand transmission on campus. Our investigation of an 18-person cluster stemming from an athletic team highlighted the importance of linking contact tracing and genomic analysis. Through these findings, it is suggestive that certain safety protocols in the athletic practice setting reduced transmission. The linking of traditional contact tracing with rapid-return genomic information is an effective approach for differentiating between multiple plausible transmission scenarios and informing subsequent public health protocols to limit disease spread in a university environment.
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Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new therapies, and finding preventive measures. Many studies have found significant associations between the microbiome and various diseases; however, Koch's classical postulates remind us about the importance of causative reasoning when considering the relationship between microbes and a disease manifestation. Although causal discovery in observational microbiome data faces many challenges, methodological advances in causal structure learning have improved the potential of data-driven prediction of causal effects in large-scale biological systems. In this Personal View, we show the capability of existing methods for inferring causal effects from metagenomic data, and we highlight ways in which the introduction of causal structures that are more flexible than existing structures offers new opportunities for causal reasoning. Our observations suggest that microbiome research can further benefit from tools developed in the past 5 years in causal discovery and learn from their applications elsewhere.
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Microbiota , Humanos , Metagenómica/métodos , Causalidad , MetagenomaRESUMEN
BACKGROUND: Assessment of disease severity associated with a novel pathogen or variant provides crucial information needed by public health agencies and governments to develop appropriate responses. The SARS-CoV-2 omicron variant of concern (VOC) spread rapidly through populations worldwide before robust epidemiological and laboratory data were available to investigate its relative severity. Here we develop a set of methods that make use of non-linked, aggregate data to promptly estimate the severity of a novel variant, compare its characteristics with those of previous VOCs, and inform data-driven public health responses. METHODS: Using daily population-level surveillance data from the National Institute for Communicable Diseases in South Africa (March 2, 2020, to Jan 28, 2022), we determined lag intervals most consistent with time from case ascertainment to hospital admission and within-hospital death through optimisation of the distance correlation coefficient in a time series analysis. We then used these intervals to estimate and compare age-stratified case-hospitalisation and case-fatality ratios across the four epidemic waves that South Africa has faced, each dominated by a different variant. FINDINGS: A total of 3â569â621 cases, 494â186 hospitalisations, and 99â954 deaths attributable to COVID-19 were included in the analyses. We found that lag intervals and disease severity were dependent on age and variant. At an aggregate level, fluctuations in cases were generally followed by a similar trend in hospitalisations within 7 days and deaths within 15 days. We noted a marked reduction in disease severity throughout the omicron period relative to previous waves (age-standardised case-fatality ratios were consistently reduced by >50%), most substantial for age strata with individuals 50 years or older. INTERPRETATION: This population-level time series analysis method, which calculates an optimal lag interval that is then used to inform the numerator of severity metrics including the case-hospitalisation and case-fatality ratio, provides useful and timely estimates of the relative effects of novel SARS-CoV-2 VOCs, especially for application in settings where resources are limited. FUNDING: National Institute for Communicable Diseases of South Africa, South African National Government.
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COVID-19 , Enfermedades Transmisibles , COVID-19/epidemiología , Enfermedades Transmisibles/epidemiología , Humanos , Persona de Mediana Edad , SARS-CoV-2/genética , Sudáfrica/epidemiología , Factores de TiempoRESUMEN
An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.