Imperial College London

ProfessorMauricioBarahona

Faculty of Natural SciencesDepartment of Mathematics

Director of Research, Chair in Biomathematics
 
 
 
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Contact

 

m.barahona Website

 
 
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Location

 

6M31Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

211 results found

Arnaudon A, Schindler D, Peach R, Gosztolai A, Hodges M, Schaub M, Barahona Met al., 2024, Algorithm XXX: PyGenStability, a multiscale community detection with generalized Markov Stability, ACM Transactions on Mathematical Software, ISSN: 0098-3500

Journal article

Wan Y, Myall AC, Boonyasiri A, Bolt F, Ledda A, Mookerjee S, Weiße AY, Getino M, Turton JF, Abbas H, Prakapaite R, Sabnis A, Abdolrasouli A, Malpartida-Cardenas K, Miglietta L, Donaldson H, Gilchrist M, Hopkins KL, Ellington MJ, Otter JA, Larrouy-Maumus G, Edwards AM, Rodriguez-Manzano J, Didelot X, Barahona M, Holmes AH, Jauneikaite E, Davies Fet al., 2024, Integrated analysis of patient networks and plasmid genomes reveals a regional, multi-species outbreak of carbapenemase-producing Enterobacterales carrying both blaIMP and mcr-9 genes., J Infect Dis

BACKGROUND: Carbapenemase-producing Enterobacterales (CPE) are challenging in healthcare, with resistance to multiple classes of antibiotics. This study describes the emergence of IMP-encoding CPE amongst diverse Enterobacterales species between 2016 and 2019 across a London regional network. METHODS: We performed a network analysis of patient pathways, using electronic health records, to identify contacts between IMP-encoding CPE positive patients. Genomes of IMP-encoding CPE isolates were overlayed with patient contacts to imply potential transmission events. RESULTS: Genomic analysis of 84 Enterobacterales isolates revealed diverse species (predominantly Klebsiella spp, Enterobacter spp, E. coli); 86% (72/84) harboured an IncHI2 plasmid carrying blaIMP and colistin resistance gene mcr-9 (68/72). Phylogenetic analysis of IncHI2 plasmids identified three lineages showing significant association with patient contacts and movements between four hospital sites and across medical specialities, which was missed on initial investigations. CONCLUSIONS: Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of blaIMPCPE, which remained unidentified during standard investigations. With DNA sequencing and multi-modal data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Plasmid-level outbreak analysis reveals that resistance spread may be wider than suspected, allowing more interventions to stop transmission within hospital networks.

Journal article

Wu N, Barahona M, Yaliraki S, 2024, Allosteric communication and signal transduction in proteins, Current Opinion in Structural Biology, ISSN: 0959-440X

Allostery is one of the cornerstones of biological function, as it plays a fundamental role in regulating protein activity. The modelling of allostery has gradually moved from a conformation-based frame-work, linked to structural changes, to dynamics-based allostery, whereby the effects of ligand binding propagate via signal transduction from the allosteric site to other regions of the protein via inter-residue interactions. Characterising such allosteric signalling pathways, which do not necessarily lead to conformational changes, has been pursued experimentally and complemented by computational analysis of protein networks to detect subtle dynamic propagation paths. Considering allostery from the perspective of signal transduction broadens the understanding of allosteric mechanisms, underscores the importance of protein topology, and can provide insights into allosteric drug design.

Journal article

Beaney T, Clarke J, Woodcock T, Majeed A, Barahona M, Aylin Pet al., 2024, Effect of timeframes to define long term conditions and sociodemographic factors on prevalence of multimorbidity using disease code frequency in primary care electronic health records: retrospective study., BMJ Med, Vol: 3

OBJECTIVE: To determine the extent to which the choice of timeframe used to define a long term condition affects the prevalence of multimorbidity and whether this varies with sociodemographic factors. DESIGN: Retrospective study of disease code frequency in primary care electronic health records. DATA SOURCES: Routinely collected, general practice, electronic health record data from the Clinical Practice Research Datalink Aurum were used. MAIN OUTCOME MEASURES: Adults (≥18 years) in England who were registered in the database on 1 January 2020 were included. Multimorbidity was defined as the presence of two or more conditions from a set of 212 long term conditions. Multimorbidity prevalence was compared using five definitions. Any disease code recorded in the electronic health records for 212 conditions was used as the reference definition. Additionally, alternative definitions for 41 conditions requiring multiple codes (where a single disease code could indicate an acute condition) or a single code for the remaining 171 conditions were as follows: two codes at least three months apart; two codes at least 12 months apart; three codes within any 12 month period; and any code in the past 12 months. Mixed effects regression was used to calculate the expected change in multimorbidity status and number of long term conditions according to each definition and associations with patient age, gender, ethnic group, and socioeconomic deprivation. RESULTS: 9 718 573 people were included in the study, of whom 7 183 662 (73.9%) met the definition of multimorbidity where a single code was sufficient to define a long term condition. Variation was substantial in the prevalence according to timeframe used, ranging from 41.4% (n=4 023 023) for three codes in any 12 month period, to 55.2% (n=5 366 285) for two codes at least three months apart. Younger people (eg, 50-75% probability for 18-29 years v 1-10% for ≥80 years), people of some minority ethnic groups

Journal article

Beaney T, Clarke J, Woodcock T, Majeed F, Barahona M, Aylin Pet al., 2023, Impact and inequalities in the prevalence of multimorbidity using different timeframes to define long term conditions: a retrospective study of disease code frequency in primary care electronic healthcare records, BMJ Medicine, ISSN: 2754-0413

Objective:Multimorbidity is a priority for health systems globally. We aimed to determine the extent to which the choice of timeframe used to define a long-term condition (LTC) impacts on the prevalence of multimorbidity and whether this varies with socio-demographic factors.Methods and Analysis:We used routinely collected general practice electronic healthcare record (EHR) data from Clinical Practice Research Datalink (CPRD) Aurum for patients in England aged 18 years or over registered on 01/01/2020. Multimorbidity was defined as the presence of two or more conditions from a set of 212 LTCs. We compared multimorbidity prevalence using a single code representing a disease diagnosis recorded anywhere in the EHR for each LTC, to prevalence based on four different timeframes for disease duration for 37 conditions where a single disease code could indicate an acute condition: 1) 2 codes at least 3 months apart; 2) two codes at least 12 months apart; 3) 3 codes within any 12-month period; 4) Any code in the last 12 months. We used mixed effects regression to calculate the expected change in multimorbidity status and number of LTCs according to each definition and associations with patient age, gender, ethnicity and socio-economic deprivation.Results:9,718,573 people were included in the study, of whom 73.9% met the definition of multimorbidity where a single code was sufficient to define an LTC. There was substantial variation in the prevalence according to timeframe used, ranging from 41.4% for three codes in the last twelve months, to 55.2% for two codes at least three months apart. Younger people, people of some minority ethnic groups and people living in areas of lower socioeconomic deprivation are more likely to be re-classified as not multimorbid when using definitions requiring multiple codes.Conclusions:Choice of timeframe to define LTCs has a substantial impact on the prevalence of multimorbidity in this nationally representative sample. Different timeframes im

Journal article

Laumann F, von Kuegelgen J, Park J, Scholkopf B, Barahona Met al., 2023, Kernel-based independence tests for causal structure learning on functional data, Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 25, ISSN: 1099-4300

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert–Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert–Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.

Journal article

Liu Z, Peach R, Laumann F, Vallejo Mengod S, Barahona Met al., 2023, Kernel-based joint independence tests for multivariate stationary and non-stationary time series, Royal Society Open Science, Vol: 10, ISSN: 2054-5703

Multivariate time-series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate statistical modelling and analysis of such systems. Here, we introduce kernel-based statistical tests of joint independence in multivariate time series by extending the d-variable Hilbert–Schmidt independence criterion to encompass both stationary and non-stationary processes, thus allowing broader real-world applications. By leveraging resampling techniques tailored for both single- and multiple-realization time series, we show how the method robustly uncovers significant higher-order dependencies in synthetic examples, including frequency mixing data and logic gates, as well as real-world climate, neuroscience and socio-economic data. Our method adds to the mathematical toolbox for the analysis of multivariate time series and can aid in uncovering high-order interactions in data.

Journal article

Laumann F, von Kügelgen J, Park J, Schölkopf B, Barahona Met al., 2023, Kernel-Based Independence Tests for Causal Structure Learning on Functional Data., Entropy (Basel, Switzerland), Vol: 25, ISSN: 1099-4300

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (hsic) and its d-variate version (<i>d</i>-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert-Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.

Journal article

Schindler D, Clarke J, Barahona M, 2023, Multiscale mobility patterns and the restriction of human movement, Royal Society Open Science, Vol: 10, Pages: 230405-230405, ISSN: 2054-5703

From the perspective of human mobility, the COVID-19 pandemic constituted a natural experiment of enormous reach in space and time. Here, we analyse the inherent multiple scales of human mobility using Facebook Movement maps collected before and during the first UK lockdown. Firstly, we obtain the pre-lockdown UK mobility graph and employ multiscale community detection to extract, in an unsupervised manner, a set of robust partitions into flow communities at different levels of coarseness. The partitions so obtained capture intrinsic mobility scales with better coverage than nomenclature of territorial units for statistics (NUTS) regions, which suffer from mismatches between human mobility and administrative divisions. Furthermore, the flow communities in the fine-scale partition not only match well the UK travel to work areas but also capture mobility patterns beyond commuting to work. We also examine the evolution of mobility under lockdown and show that mobility first reverted towards fine-scale flow communities already found in the pre-lockdown data, and then expanded back towards coarser flow communities as restrictions were lifted. The improved coverage induced by lockdown is well captured by a linear decay shock model, which allows us to quantify regional differences in both the strength of the effect and the recovery time from the lockdown shock.

Journal article

Beaney T, Clarke J, Salman D, Woodcock T, Majeed F, Barahona M, Aylin Pet al., 2023, Identifying potential biases in code sequences in primary care electronic healthcare records: a retrospective cohort study of the determinants of code frequency, BMJ Open, Vol: 13, ISSN: 2044-6055

Objectives To determine whether the frequency of diagnostic codes for long-term conditions (LTCs) in primary care electronic healthcare records (EHRs) is associated with (1) disease coding incentives, (2) General Practice (GP), (3) patient sociodemographic characteristics and (4) calendar year of diagnosis.Design Retrospective cohort study.Setting GPs in England from 2015 to 2022 contributing to the Clinical Practice Research Datalink Aurum dataset.Participants All patients registered to a GP with at least one incident LTC diagnosed between 1 January 2015 and 31 December 2019.Primary and secondary outcome measures The number of diagnostic codes for an LTC in (1) the first and (2) the second year following diagnosis, stratified by inclusion in the Quality and Outcomes Framework (QOF) financial incentive programme.Results 3 113 724 patients were included, with 7 723 365 incident LTCs. Conditions included in QOF had higher rates of annual coding than conditions not included in QOF (1.03 vs 0.32 per year, p<0.0001). There was significant variation in code frequency by GP which was not explained by patient sociodemographics. We found significant associations with patient sociodemographics, with a trend towards higher coding rates in people living in areas of higher deprivation for both QOF and non-QOF conditions. Code frequency was lower for conditions with follow-up time in 2020, associated with the onset of the COVID-19 pandemic.Conclusions The frequency of diagnostic codes for newly diagnosed LTCs is influenced by factors including patient sociodemographics, disease inclusion in QOF, GP practice and the impact of the COVID-19 pandemic. Natural language processing or other methods using temporally ordered code sequences should account for these factors to minimise potential bias.

Journal article

Liu Z, Peach R, Mediano P, Barahona Met al., 2023, Interaction measures, partition lattices and kernel tests for high-order interactions, NeurIPS 2023 - Thirty-seventh Conference on Neural Information Processing Systems

Models that rely solely on pairwise relationships often fail to capture the completestatistical structure of the complex multivariate data found in diverse domains,such as socio-economic, ecological, or biomedical systems. Non-trivial dependen-cies between groups of more than two variables can play a significant role in theanalysis and modelling of such systems, yet extracting such high-order interac-tions from data remains challenging. Here, we introduce a hierarchy of d-orderinteraction measures, increasingly inclusive of possible factorisations of the jointprobability distribution, and define non-parametric, kernel-based tests to establishsystematically the statistical significance of d-order interactions. We also establishmathematical links with lattice theory, which elucidate the derivation of the inter-action measures and their composite permutation tests; clarify the connection ofsimplicial complexes with kernel matrix centring; and provide a means to enhancecomputational efficiency. We illustrate our results numerically with validations onsynthetic data, and through an application to neuroimaging data.

Conference paper

Maes A, Barahona M, Clopath C, 2023, Long- and short-term history effects in a spiking network model of statistical learning, Scientific Reports, Vol: 13, Pages: 1-14, ISSN: 2045-2322

The statistical structure of the environment is often important when making decisions. There are multiple theories of howthe brain represents statistical structure. One such theory states that neural activity spontaneously samples from probabilitydistributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting fromthe neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitraryprior knowledge about the external world can both be learned and spontaneously recollected. We present a model basedupon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neuronsand biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectationsand signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoinglearning.

Journal article

Myall A, Wiedermann M, Vasikasin P, Klamser P, Wan Y, Zachariae A, Peach R, Dorigatti I, Kreitmann L, Rodgus J, Getino-Redondo M, Mookerje S, Jauneikaite E, Davies F, Weisse A, Price J, Holmes A, Barahona M, Brockmann Det al., 2023, RECONSTRUCTING AND PREDICTING THE SPATIAL EVOLUTION OF CARBAPENEMASE-PRODUCING ENTEROBACTERIACEAE OUTBREAKS, Publisher: ELSEVIER SCI LTD, Pages: S65-S65, ISSN: 1201-9712

Conference paper

Myall A, Venkatachalam I, Philip C, Yin M, Koon D, Arora S, Yue Y, Peach R, Weisse A, Tambyah P, Chow A, Price J, Cook A, Holmes A, Barahona Met al., 2023, SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROL, Publisher: ELSEVIER SCI LTD, Pages: S31-S31, ISSN: 1201-9712

Conference paper

Lamprinakou S, Barahona M, Flaxman S, Filippi S, Gandy A, McCoy EJet al., 2023, BART-based inference for Poisson processes, Computational Statistics and Data Analysis, Vol: 180, Pages: 1-25, ISSN: 0167-9473

The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non-parametric regression and classification. A BART scheme for estimating the intensity of inhomogeneous Poisson processes is introduced. Poisson intensity estimation is a vital task in various applications including medical imaging, astrophysics and network traffic analysis. The new approach enables full posterior inference of the intensity in a non-parametric regression setting. The performance of the novel scheme is demonstrated through simulation studies on synthetic and real datasets up to five dimensions, and the new scheme is compared with alternative approaches.

Journal article

August E, Barahona M, 2022, Finding positively invariant sets and proving exponential stability of limit cycles using sum-of-squares decompositions, Journal of Computational Dynamics, Vol: 10, Pages: 105-126, ISSN: 2158-2505

The dynamics of many systems from physics, economics, chemistry, and biology can be modelled through polynomial functions. In this paper, we provide a computational means to find positively invariant sets of polynomial dynamical systems by using semidefinite programming to solve sum-of-squares (SOS) programmes. With the emergence of SOS programmes, it is possible to efficiently search for Lyapunov functions that guarantee stability of polynomial systems. Yet, SOS computations often fail to find functions, such that the conditions hold in the entire state space. We show here that restricting the SOS optimisation to specific domains enables us to obtain positively invariant sets, thus facilitating the analysis of the dynamics by considering separately eachpositively invariant set. In addition, we go beyond classical Lyapunov stability analysis and use SOS decompositions to computationally implement sufficient positivity conditions that guarantee existence, uniqueness, and exponential stability of a limit cycle. Importantly, this approach is applicable to systems of any dimension and, thus, goes beyond classical methods that are restricted to two dimensional phase space. We illustrate our different results with applications to classical systems, such as the van der Pol oscillator, the Fitzhugh-Nagumo neuronal equation, and the Lorenz system.

Journal article

Sapienza R, Barahona M, Saxena D, alexis A, Yaliraki Set al., 2022, Sensitivity and spectral control of network lasers, Nature Communications, Vol: 13, Pages: 1-7, ISSN: 2041-1723

Recently, random lasing in complex networks has shown efficient lasing over more than 50 localised modes, promoted by multiple scattering over the underlying graph. If controlled, these network lasers can lead to fast-switching multifunctional light sources with synthesised spectrum. Here, we observe both in experiment and theory high sensitivity of the network laser spectrum to the spatial shape of the pump profile, with some modes for example increasing in intensity by 280% when switching off 7% of the pump beam. We solve the nonlinear equations within the steady state ab-initio laser theory (SALT) approximation over a graph and we show selective lasing of around 90% of the strongest intensity modes, effectively programming the spectrum of the lasing networks. In our experiments with polymer networks, this high sensitivity enables control of the lasing spectrum through non-uniform pump patterns. We propose the underlying complexity of the network modes as the key element behind efficient spectral control opening the way for the development of optical devices with wide impact for on-chip photonics for communication, sensing, and computation.

Journal article

Strömich L, Wu N, Barahona M, Yaliraki SNet al., 2022, Allosteric Hotspots in the Main Protease of SARS-CoV-2., J Mol Biol, Vol: 434

Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.

Journal article

Wu N, Yaliraki SN, Barahona M, 2022, Prediction of Protein Allosteric Signalling Pathways and Functional Residues Through Paths of Optimised Propensity, Journal of Molecular Biology, Vol: 434, Pages: 167749-167749, ISSN: 0022-2836

Journal article

Freischem LJ, Barahona M, Oyarzún DA, 2022, Prediction of gene essentiality using machine learning and genome-scale metabolic models, 9th IFAC Conference on Foundations of Systems Biology in Engineering FOSBE 2022, Publisher: Elsevier BV, Pages: 13-18, ISSN: 2405-8963

The identification of essential genes, i.e. those that impair cell survival when deleted, requires large growth assays of knock-out strains. The complexity and cost of such experiments has triggered a growing interest in computational methods for prediction of gene essentiality. In the case of metabolic genes, Flux Balance Analysis (FBA) is widely employed to predict essentiality under the assumption that cells maximize their growth rate. However, this approach assumes that knock-out strains optimize the same objectives as the wild-type, which excludes cases in which deletions cause large physiological changes to meet other objectives for survival. Here, we resolve this limitation with a novel machine learning approach that predicts essentiality directly from wild-type flux distributions. We first project the wild-type FBA solution onto a mass flow graph, a digraph with reactions as nodes and edge weights proportional to the mass transfer between reactions, and then train binary classifiers on the connectivity of graph nodes. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli, achieving near state-of-the art prediction accuracy for essential genes. Our approach suggests that wild-type FBA solutions contain enough information to predict essentiality, without the need to assume optimality of deletion strains.

Conference paper

Alaa A, Mayer E, Barahona M, 2022, ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations, Machine Learning for Healthcare Conference, Pages: 537-564, ISSN: 2640-3498

Early diagnosis of disease can lead to improved health outcomes, including higher survival rates and lower treatment costs. With the massive amount of information available in electronic health records (EHRs), there is great potential to use machine learning (ML) methods to model disease progression aimed at early prediction of disease onset and other outcomes. In this work, we employ recent innovations in neural ODEs combined with rich semantic embeddings of clinical codes to harness the full temporal information of EHRs. We propose ICE-NODE (Integration of Clinical Embeddings with Neural Ordinary Differential Equations), an architecture that temporally integrates embeddings of clinical codes and neural ODEs to learn and predict patient trajectories in EHRs. We apply our method to the publicly available MIMIC-III and MIMIC-IV datasets, and we find improved prediction results compared to state-of-the-art methods, specifically for clinical codes that are not frequently observed in EHRs. We also show that ICE-NODE is more competent at predicting certain medical conditions, like acute renal failure, pulmonary heart disease and birth-related problems, where the full temporal information could provide important information. Furthermore, ICE-NODE is also able to produce patient risk trajectories over time that can be exploited for further detailed predictions of disease evolution.

Conference paper

Myall A, Price J, Peach R, Abbas M, Mookerjee S, Zhu N, Ahmad I, Ming D, Ramzan F, Teixeira D, Graf C, Weisse A, Harbarth S, Holmes A, Barahona Met 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

Journal article

Rodrigues D, Kreif N, Lawrence-Jones A, Barahona M, Mayer Eet al., 2022, Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research, International Journal of Epidemiology, Vol: 51, ISSN: 0300-5771

Directed acyclic graphs (DAGs) are a useful tool to represent, in a graphical format, researchers’ assumptions about the causal structure among variables while providing a rationale for the choice of confounding variables to adjust for. With origins in the field of probabilistic graphical modelling, DAGs are yet to be widely adopted in applied health research, where causal assumptions are frequently made for the purpose of evaluating health services initiatives. In this context, there is still limited practical guidance on how to construct and use DAGs. Some progress has recently been made in terms of building DAGs based on studies from the literature, but an area that has received less attention is how to create DAGs from information provided by domain experts, an approach of particular importance when there is limited published information about the intervention under study. This approach offers the opportunity for findings to be more robust and relevant to patients, carers and the public, and more likely to inform policy and clinical practice. This article draws lessons from a stakeholder workshop involving patients, health care professionals, researchers, commissioners and representatives from industry, whose objective was to draw DAGs for a complex intervention—online consultation, i.e. written exchange between the patient and health care professional using an online system—in the context of the English National Health Service. We provide some initial, practical guidance to those interested in engaging with domain experts to develop DAGs.

Journal article

Peach R, Arnaudon A, Barahona M, 2022, Relative, local and global dimension in complex networks., Nat Commun, Vol: 13

Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhomogeneities, or to intrinsically discrete systems such as networks. To take into account locality, finiteness and discreteness, dynamical processes can be used to probe the space geometry and define its dimension. Here we show that each point in space can be assigned a relative dimension with respect to the source of a diffusive process, a concept that provides a scale-dependent definition for local and global dimension also applicable to networks. To showcase its application to physical systems, we demonstrate that the local dimension of structural protein graphs correlates with structural flexibility, and the relative dimension with respect to the active site uncovers regions involved in allosteric communication. In simple models of epidemics on networks, the relative dimension is predictive of the spreading capability of nodes, and identifies scales at which the graph structure is predictive of infectivity. We further apply our dimension measures to neuronal networks, economic trade, social networks, ocean flows, and to the comparison of random graphs.

Journal article

Rodrigues D, Kreif N, Saravanakumar K, Delaney B, Barahona M, Mayer Eet al., 2022, Formalising triage in general practice towards a more equitable, safe, and efficient allocation of resources, BMJ: British Medical Journal, Vol: 377, ISSN: 0959-535X

Journal article

Sivan M, Greenhalgh T, Darbyshire JL, Mir G, O'Connor RJ, Dawes H, Greenwood D, O'Connor D, Horton M, Petrou S, de Lusignan S, Curcin V, Mayer E, Casson A, Milne R, Rayner C, Smith N, Parkin A, Preston N, Delaney Bet al., 2022, LOng COvid Multidisciplinary consortium Optimising Treatments and services acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK, BMJ OPEN, Vol: 12, ISSN: 2044-6055

Journal article

Qian Y, Expert P, Rieu T, Panzarasa P, Barahona Met al., 2022, Quantifying the alignment of graph and features in deep learning, IEEE Transactions on Neural Networks and Learning Systems, Vol: 33, Pages: 1663-1672, ISSN: 1045-9227

We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, and ground truth. The proposed measure is based on the principal angles between subspaces and has both spectral and geometrical interpretations. We showcase the relationship between the SAM and the classification performance through the study of limiting cases of GCNs and systematic randomizations of both features and graph structure applied to a constructive example and several examples of citation networks of different origins. The analysis also reveals the relative importance of the graph and features for classification purposes.

Journal article

Chrysostomou S, Roy R, Prischi F, Thamlikitkul L, Chapman KL, Mufti U, Peach R, Ding L, Hancock D, Moore C, Molina-Arcas M, Mauri F, Pinato DJ, Abrahams JM, Ottaviani S, Castellano L, Giamas G, Pascoe J, Moonamale D, Pirrie S, Gaunt C, Billingham L, Steven NM, Cullen M, Hrouda D, Winkler M, Post J, Cohen P, Salpeter SJ, Bar V, Zundelevich A, Golan S, Leibovici D, Lara R, Klug DR, Yaliraki SN, Barahona M, Wang Y, Downward J, Skehel JM, Ali MMU, Seckl MJ, Pardo Eet al., 2022, Re: Repurposed Floxacins Targeting RSK4 Prevent Chemoresistance and Metastasis in Lung and Bladder Cancer, JOURNAL OF UROLOGY, Vol: 207, Pages: 919-920, ISSN: 0022-5347

Journal article

Laumann F, von Kuegelgen J, Kanashiro Uehara TH, Barahona Met al., 2022, Quantitative assessment of complex interlinkages, key objectives and nexuses amongst the Sustainable Development Goals and climate change, The Lancet Planetary Health, Vol: 6, ISSN: 2542-5196

Background. Global sustainability is an enmeshed system of complex socio-economic, climato-logical and ecological interactions. The numerous objectives of the United Nations’ Sustainable Development Goals (SDGs) and the Paris Agreement have various levels of interdependence, making it difficult to ascertain the influence of changes in particular indicators across the whole system.Methods. We present a method to find interlinkages amongst the 17 SDGs and climate change, including non-linear and non-monotonic dependences, by using 400 indicators that track their temporal changes. The method detects statistically significant dependencies amongst the time evolution of the objectives by using partial distance correlations, a non-linear measure of conditional dependence that also discounts spurious correlations originating from lurking variables. We then employ a network representation to identify the most important objectives (using network centrality) and to obtain nexuses of objectives (defined as highly interconnected clusters in the network).Findings. Using temporal data from 181 countries spanning 20 years, we analyse dependencies amongst SDGs and climate for 35 country groupings based on region, development and income 2 level. Our results show that the significant interlinkages, central objectives, and nexuses identified vary greatly across country groupings, yet partnerships for the goals (SDG 17) and climate change rank as highly important across many country groupings.Temperature rise is strongly linked to urbanisation, air pollution, and slum expansion (SDG 11), especially in country groupings likely to be worst affectedby climate breakdown such as Africa. In several groupings encompassing the developing countries, a consistent nexus of strongly interconnected objectives is formed by poverty reduction (SDG 1), education (SDG 4), and economic growth (SDG 8), sometimes incorporating gender equality (SDG 5), and peace and justice (SDG 16).Interpretation. The

Journal article

Myall A, Price J, Peach R, Abbas M, Mookerjee S, Ahmad I, Ming D, Zhu NJ, Ramzan F, Weisse A, Holmes AH, Barahona Met 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

Conference paper

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