19 results found
Myall A, Price J, Peach R, et al., 2022, Predicting hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study, The Lancet Digital Health, Vol: 4, Pages: e573-e583, ISSN: 2589-7500
Background:Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.Methods:We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.Findings:The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88–0·90]) and similarly predictive using only contact-network variables (0·88 [0·86–0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0&middo
Myall A, Peach R, Wan Y, et al., 2022, Improved contact tracing using network analysis and spatial-temporal proximity, IMED conference, Publisher: ELSEVIER SCI LTD, Pages: S20-S20, ISSN: 1201-9712
Myall A, Price J, Peach R, et al., 2022, Prediction of hospital-onset COVID-19 using networks of patient contact: an observational study, IMED conference, Publisher: ELSEVIER SCI LTD, Pages: S109-S110, ISSN: 1201-9712
Boonyasiri A, Myall AC, Wan Y, et al., 2021, Integrated patient network and genomic plasmid analysis reveal a regional, multi-species outbreak of carbapenemase-producing Enterobacterales carrying both <i>bla</i><sub>IMP</sub> and <i>mcr-9</i> genes
<jats:title>Abstract</jats:title><jats:p>The incidence of carbapenemase-producing Enterobacterales (CPE) is rising globally, yet Imipenemase (IMP) carbapenemases remain relatively rare. This study describes an investigation of the emergence of IMP-encoding CPE amongst diverse Enterobacterales species between 2016 and 2019 in patients across a London regional hospital network.</jats:p><jats:p>A network analysis approach to patient pathways, using routinely collected electronic health records, identified previously unrecognised contacts between patients who were IMP CPE positive on screening, implying potential bacterial transmission events. Whole genome sequencing of 85 Enterobacterales isolates from these patients revealed that 86% (73/85) were diverse species (predominantly <jats:italic>Klebsiella</jats:italic> spp, <jats:italic>Enterobacter</jats:italic> spp, <jats:italic>E. coli</jats:italic>) and harboured an IncHI2 plasmid, which carried both <jats:italic>bla</jats:italic><jats:sub>IMP</jats:sub> and the putative mobile colistin resistance gene <jats:italic>mcr-9</jats:italic>. Detailed phylogenetic analysis identified two distinct IncHI2 plasmid lineages, A and B, both of which showed significant association with patient movements between four hospital sites and across medical specialities.</jats:p><jats:p>Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of <jats:italic>bla</jats:italic><jats:sub>IMP</jats:sub>CPE, which remained unidentified during standard microbiology and infection control investigations. With whole genome sequencing (WGS) technologies and large-data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Analysing outbreaks at the plasmid level reveal
Ming DKY, Myall A, Hernandez B, et al., 2021, Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin, BMC Infectious Diseases, Vol: 21, ISSN: 1471-2334
Background:To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making.Methods:Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT. Pairwise comparisons were performed between: (i) those with or without significant bacterial growth from cultures, and (ii) those who survived or died in hospital.Results:CRP concentrations were higher over time in COVID-19 patients with positive microbiology (day 9: 236 vs 123 mg/L, p < 0.0001) and in those who died (day 8: 226 vs 152 mg/L, p < 0.0001) but only after day 7 of COVID-related symptom onset. Failure for CRP to reduce in the first week of hospital admission was associated with significantly higher odds of death. PCT concentrations were higher in patients with COVID-19 and positive microbiology or in those who died, although these differences were not statistically significant.Conclusions:Both the absolute CRP concentration and the trajectory during the first week of hospital admission are important factors predicting microbiology culture positivity and outcome in patients hospitalised with COVID-19. Further work is needed to describe the role of PCT for co-infection. Understanding relationships of these biomarkers can support development of risk models and inform optimal antimicrobial strategies.
Myall AC, Peach RL, Weiße AY, et al., 2021, Network memory in the movement of hospital patients carrying drug-resistant bacteria, Applied Network Science, Vol: 6, ISSN: 2364-8228
Hospitals constitute highly interconnected systems that bring into contact anabundance of infectious pathogens and susceptible individuals, thus makinginfection outbreaks both common and challenging. In recent years, there hasbeen a sharp incidence of antimicrobial-resistance amongsthealthcare-associated infections, a situation now considered endemic in manycountries. Here we present network-based analyses of a data set capturing themovement of patients harbouring drug-resistant bacteria across three largeLondon hospitals. We show that there are substantial memory effects in themovement of hospital patients colonised with drug-resistant bacteria. Suchmemory effects break first-order Markovian transitive assumptions andsubstantially alter the conclusions from the analysis, specifically on noderankings and the evolution of diffusive processes. We capture variable lengthmemory effects by constructing a lumped-state memory network, which we then useto identify overlapping communities of wards. We find that these communities ofwards display a quasi-hierarchical structure at different levels of granularitywhich is consistent with different aspects of patient flows related to hospitallocations and medical specialties.
Myall A, Peach RL, Wan Y, et al., 2021, Characterising contact in disease outbreaks via a network model of spatial-temporal proximity
<jats:title>ABSTRACT</jats:title><jats:p>Contact tracing is a key tool in epidemiology to identify and control outbreaks of infectious diseases. Existing contact tracing methodologies produce contact maps of individuals based on a binary definition of contact which can be hampered by missing data and indirect contacts. Here, we present a Spatial-temporal Epidemiological Proximity (StEP) model to recover contact maps in disease outbreaks based on movement data. The StEP model accounts for imperfect data by considering probabilistic contacts between individuals based on spatial-temporal proximity of their movement trajectories, creating a robust movement network despite possible missing data and unseen transmission routes. Using real-world data we showcase the potential of StEP for contact tracing with outbreaks of multidrug-resistant bacteria and COVID-19 in a large hospital group in London, UK. In addition to the core structure of contacts that can be recovered using traditional methods of contact tracing, the StEP model reveals missing contacts that connect seemingly separate outbreaks. Comparison with genomic data further confirmed that these recovered contacts indeed improve characterisation of disease transmission and so highlights how the StEP framework can inform effective strategies of infection control and prevention.</jats:p>
Price JR, Mookerjee S, Dyakova E, et al., 2021, Development and delivery of a real-time hospital-onset COVID-19 surveillance system using network analysis, Clinical Infectious Diseases, Vol: 72, Pages: 82-89, ISSN: 1058-4838
BackgroundUnderstanding nosocomial acquisition, outbreaks and transmission chains in real-time will be fundamental to ensuring infection prevention measures are effective in controlling COVID-19 in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions.MethodsThe study took place in a large teaching hospital group in London, UK. All patients tested for SARS-CoV-2 between 4th March and 14th April 2020 were included. Utilising data routinely collected through electronic healthcare systems we developed a novel surveillance system for determining and reporting HOCI incidence and providing real-time network analysis. We provided daily reports on incidence and trends over time to support HOCI investigation, and generated geo-temporal reports using network analysis to interrogate admission pathways for common epidemiological links to infer transmission chains. By working with stakeholders the reports were co-designed for end users.ResultsReal-time surveillance reports revealed: changing rates of HOCI throughout the course of the COVID-19 epidemic; key wards fuelling probable transmission events; HOCIs over-represented in particular specialities managing high-risk patients; the importance of integrating analysis of individual prior pathways; and the value of co-design in producing data visualisation. Our surveillance system can effectively support national surveillance.ConclusionsThrough early analysis of the novel surveillance system we have provided a description of HOCI rates and trends over time using real-time shifting denominator data. We demonstrate the importance of including the analysis of patient pathways and networks in characterising risk of transmission and targeting infection control interventions.
Nikolados E-M, Weiße AY, Oyarzún DA, 2021, Prediction of cellular burden with host-circuit models., Methods in Molecular Biology, Publisher: Springer, Pages: 267-291
Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.
Otter JA, Mookerjee S, Davies F, et al., 2020, Detecting carbapenemase-producing Enterobacterales (CPE): an evaluation of an enhanced CPE infection control and screening programme in acute care, JOURNAL OF ANTIMICROBIAL CHEMOTHERAPY, Vol: 75, Pages: 2670-2676, ISSN: 0305-7453
- Author Web Link
- Citations: 4
Nikolados E, Weisse A, Ceroni F, et al., 2019, Growth defects and loss-of-function in synthetic gene circuits, ACS Synthetic Biology, Vol: 8, Pages: 1231-1240, ISSN: 2161-5063
Synthetic gene circuits perturb the physiology of their cellular host. The extra load on endogenous processes shifts the equilibrium of resource allocation in the host, leading to slow growth and reduced biosynthesis. Here we built integrated host-circuit models to quantify growth defects caused by synthetic gene circuits. Simulations reveal a complex relation between circuit output and cellular capacity for gene expression. For weak induction of heterologous genes, protein output can be increased at the expense of growth defects. Yet for stronger induction, cellular capacity reaches a tipping point, beyond which both gene expression and growth rate drop sharply. Extensive simulations across various growth conditions and large regions of the design space suggest that the critical capacity is a result of ribosomal scarcity. We studied the impact of growth defects on various gene circuits and transcriptional logic gates, which highlights the extent to which cellular burden can limit, shape, and even break down circuit function. Our approach offers a comprehensive framework to assess the impact of host-circuit interactions in silico, with wide-ranging implications for the design and optimization of bacterial gene circuits.
Thomas P, Terradot G, Danos V, et al., 2018, Sources, propagation and consequences of stochasticity in cellular growth, Nature Communications, Vol: 9, ISSN: 2041-1723
Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.
Terradot G, Beica A, Weisse A, et al., 2018, Survival of the Fattest: Evolutionary Trade-offs in Cellular Resource Storage, ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE, Vol: 335, Pages: 91-112, ISSN: 1571-0661
Weisse AY, Mannan AA, Oyarzun DA, 2016, Signaling tug-of-war delivers the whole message, Cell Systems, Vol: 3, Pages: 414-46, ISSN: 2405-4720
How do cells transmit biochemical signals accurately? It turns out,pushing and pulling can go a long way.
Weisse AY, Oyarzun DA, Danos V, et al., 2015, Mechanistic links between cellular trade-offs, gene expression, and growth, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 112, Pages: E1038-E1047, ISSN: 0027-8424
- Author Web Link
- Open Access Link
- Citations: 215
Karwacki-Neisius V, Goeke J, Osorno R, et al., 2013, Reduced Oct4 Expression Directs a Robust Pluripotent State with Distinct Signaling Activity and Increased Enhancer Occupancy by Oct4 and Nanog, CELL STEM CELL, Vol: 12, Pages: 531-545, ISSN: 1934-5909
- Author Web Link
- Citations: 129
Oyarzun DA, Lopez-Caamal F, Garcia MR, et al., 2013, Cumulative signal transmission in nonlinear reaction-diffusion networks, Plos One, Vol: 5
Weiße AY, Middleton RH, Huisinga W, 2010, Quantifying uncertainty, variability and likelihood for ordinary differential equation models, BMC Systems Biology, Vol: 4
Véron N, Bauer H, Weiße AY, et al., 2009, Retention of gene products in syncytial spermatids promotes non-Mendelian inheritance as revealed by the <i>t complex responder</i>, Genes & Development, Vol: 23, Pages: 2705-2710, ISSN: 0890-9369
<jats:p>The <jats:italic>t complex responder</jats:italic> (<jats:italic>Tcr</jats:italic>) encoded by the mouse <jats:italic>t</jats:italic> haplotype is able to cause phenotypic differences between <jats:italic>t</jats:italic> and + sperm derived from <jats:italic>t</jats:italic>/+ males, leading to non-Mendelian inheritance. This capability of <jats:italic>Tcr</jats:italic> contradicts the concept of phenotypic equivalence proposed for sperm cells, which develop in a syncytium and actively share gene products. By analyzing a <jats:italic>Tcr</jats:italic> minigene in hemizygous transgenic mice, we show that <jats:italic>Tcr</jats:italic> gene products are post-meiotically expressed and are retained in the haploid sperm cells. The wild-type allele of <jats:italic>Tcr</jats:italic>, <jats:italic>sperm motility kinase-1</jats:italic> (<jats:italic>Smok1</jats:italic>), behaves in the same manner, suggesting that <jats:italic>Tcr</jats:italic>/<jats:italic>Smok</jats:italic> reveal a common mechanism prone to evolve non-Mendelian inheritance in mammals.</jats:p>
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.