1,781 results on '"Suchard, Marc A."'
Search Results
52. Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study
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Cai, Cindy X., Nishimura, Akihiko, Bowring, Mary G., Westlund, Erik, Tran, Diep, Ng, Jia H., Nagy, Paul, Cook, Michael, McLeggon, Jody-Ann, DuVall, Scott L., Matheny, Michael E., Golozar, Asieh, Ostropolets, Anna, Minty, Evan, Desai, Priya, Bu, Fan, Toy, Brian, Hribar, Michelle, Falconer, Thomas, Zhang, Linying, Lawrence-Archer, Laurence, Boland, Michael V., Goetz, Kerry, Hall, Nathan, Shoaibi, Azza, Reps, Jenna, Sena, Anthony G., Blacketer, Clair, Swerdel, Joel, Jhaveri, Kenar D., Lee, Edward, Gilbert, Zachary, Zeger, Scott L., Crews, Deidra C., Suchard, Marc A., Hripcsak, George, and Ryan, Patrick B.
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- 2024
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53. Scalable Bayesian divergence time estimation with ratio transformations
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Ji, Xiang, Fisher, Alexander A., Su, Shuo, Thorne, Jeffrey L., Potter, Barney, Lemey, Philippe, Baele, Guy, and Suchard, Marc A.
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Quantitative Biology - Populations and Evolution ,Statistics - Computation - Abstract
Divergence time estimation is crucial to provide temporal signals for dating biologically important events, from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly-correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original N - 1 internal node heights into a space of one height parameter and N - 2 ratio parameters. To make analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in four pathogenic virus phylogenies: West Nile virus, rabies virus, Lassa virus and Ebola virus. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples. Our method also makes it now computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study and reveals clearer multimodal distributions of the divergence times of some clades of interest., Comment: 34 pages, 6 figures
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- 2021
54. Accommodating sampling location uncertainty in continuous phylogeography
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Dellicour, Simon, Lemey, Philippe, Suchard, Marc A, Gilbert, Marius, and Baele, Guy
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Biological Sciences ,Genetics ,virus ,host species ,continuous phylogeography ,sampling precision ,Bayesian inference ,BEAST ,Evolutionary Biology ,Microbiology - Abstract
Phylogeographic inference of the dispersal history of viral lineages offers key opportunities to tackle epidemiological questions about the spread of fast-evolving pathogens across human, animal and plant populations. In continuous space, i.e. when locations are specified by longitude and latitude, these reconstructions are however often limited by the availability or accessibility of precise sampling locations required for such spatially explicit analyses. We here review the different approaches that can be considered when genomic sequences are associated with a geographic area of sampling instead of precise coordinates. In particular, we describe and compare the approaches to define homogeneous and heterogeneous prior ranges of sampling coordinates.
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- 2022
55. Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies
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Khera, Rohan, Schuemie, Martijn J, Lu, Yuan, Ostropolets, Anna, Chen, RuiJun, Hripcsak, George, Ryan, Patrick B, Krumholz, Harlan M, and Suchard, Marc A
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Pharmacology and Pharmaceutical Sciences ,Biomedical and Clinical Sciences ,Patient Safety ,Clinical Research ,Comparative Effectiveness Research ,Diabetes ,Cardiovascular ,Generic health relevance ,Metabolic and endocrine ,Good Health and Well Being ,Adult ,Diabetes Mellitus ,Type 2 ,Dipeptidyl-Peptidase IV Inhibitors ,Humans ,Hypoglycemic Agents ,Reproducibility of Results ,Sodium-Glucose Transporter 2 Inhibitors ,Sulfonylurea Compounds ,Health informatics ,DIABETES & ENDOCRINOLOGY ,Cardiology ,Clinical Sciences ,Public Health and Health Services ,Other Medical and Health Sciences ,Biomedical and clinical sciences ,Health sciences ,Psychology - Abstract
IntroductionTherapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk.Methods and analysisThe large-scale evidence generations across a network of databases for T2DM (LEGEND-T2DM) initiative is a series of systematic, large-scale, multinational, real-world comparative cardiovascular effectiveness and safety studies of all four major second-line anti-hyperglycaemic agents, including sodium-glucose co-transporter-2 inhibitor, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor and sulfonylureas. LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. LEGEND-T2DM will identify all adult, patients with T2DM who newly initiate a traditionally second-line T2DM agent. Using an active comparator, new-user cohort design, LEGEND-T2DM will execute all pairwise class-versus-class and drug-versus-drug comparisons in each data source, producing extensive study diagnostics that assess reliability and generalisability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes. The primary cardiovascular outcomes include a composite of major adverse cardiovascular events and a series of safety outcomes. The study will pursue data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias.Ethics and disseminationThe study ensures data safety through a federated analytic approach and follows research best practices, including prespecification and full disclosure of results. LEGEND-T2DM is dedicated to open science and transparency and will publicly share all analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data and results to verify and extend our findings.
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- 2022
56. Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements
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Hassler, Gabriel, Tolkoff, Max R, Allen, William L, Ho, Lam Si Tung, Lemey, Philippe, and Suchard, Marc A
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Economics ,Statistics ,Econometrics ,Mathematical Sciences ,Bioengineering ,1.4 Methodologies and measurements ,Aetiology ,2.5 Research design and methodologies (aetiology) ,Underpinning research ,Generic health relevance ,Bayesian inference ,Matrix-normal ,Missing data ,Phylogenetics ,matrix-normal ,missing data ,phylogenetics ,stat.ME ,stat.CO ,Demography ,Statistics & Probability - Abstract
Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or non-heritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale.
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- 2022
57. Mathematical models to study the biology of pathogens and the infectious diseases they cause
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Xavier, Joao B, Monk, Jonathan M, Poudel, Saugat, Norsigian, Charles J, Sastry, Anand V, Liao, Chen, Bento, Jose, Suchard, Marc A, Arrieta-Ortiz, Mario L, Peterson, Eliza JR, Baliga, Nitin S, Stoeger, Thomas, Ruffin, Felicia, Richardson, Reese AK, Gao, Catherine A, Horvath, Thomas D, Haag, Anthony M, Wu, Qinglong, Savidge, Tor, and Yeaman, Michael R
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Biochemistry and Cell Biology ,Biological Sciences ,Vaccine Related ,Emerging Infectious Diseases ,Prevention ,Bioengineering ,Infectious Diseases ,Biodefense ,2.1 Biological and endogenous factors ,Aetiology ,Infection ,Good Health and Well Being ,Computer modeling ,Infection control in health technology ,Microbiology - Abstract
Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.
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- 2022
58. Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis
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Hassler, Gabriel W., Gallone, Brigida, Aristide, Leandro, Allen, William L., Tolkoff, Max R., Holbrook, Andrew J., Baele, Guy, Lemey, Philippe, and Suchard, Marc A.
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Quantitative Biology - Populations and Evolution ,Statistics - Applications ,Statistics - Methodology - Abstract
Biological phenotypes are products of complex evolutionary processes in which selective forces influence multiple biological trait measurements in unknown ways. Phylogenetic factor analysis disentangles these relationships across the evolutionary history of a group of organisms. Scientists seeking to employ this modeling framework confront numerous modeling and implementation decisions, the details of which pose computational and replicability challenges. General and impactful community employment requires a data scientific analysis plan that balances flexibility, speed and ease of use, while minimizing model and algorithm tuning. Even in the presence of non-trivial phylogenetic model constraints, we show that one may analytically address latent factor uncertainty in a way that (a) aids model flexibility, (b) accelerates computation (by as much as 500-fold) and (c) decreases required tuning. We further present practical guidance on inference and modeling decisions as well as diagnosing and solving common problems in these analyses. We codify this analysis plan in an automated pipeline that distills the potentially overwhelming array of modeling decisions into a small handful of (typically binary) choices. We demonstrate the utility of these methods and analysis plan in four real-world problems of varying scales., Comment: 27 pages, 7 figures, 1 table
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- 2021
59. An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials
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Oikonomou, Evangelos K., Thangaraj, Phyllis M., Bhatt, Deepak L., Ross, Joseph S., Young, Lawrence H., Krumholz, Harlan M., Suchard, Marc A., and Khera, Rohan
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- 2023
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60. A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm
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Jun, Seong-Hwan, Nasif, Hassan, Jennings-Shaffer, Chris, Rich, David H, Kooperberg, Anna, Fourment, Mathieu, Zhang, Cheng, Suchard, Marc A, and Matsen, IV, Frederick A
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- 2023
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61. From viral evolution to spatial contagion: a biologically modulated Hawkes model.
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Holbrook, Andrew J, Ji, Xiang, and Suchard, Marc A
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Biodefense ,Prevention ,Bioengineering ,Infectious Diseases ,Emerging Infectious Diseases ,Vaccine Related ,Genetics ,2.5 Research design and methodologies (aetiology) ,Aetiology ,Infection ,Good Health and Well Being ,Humans ,Bayes Theorem ,Phylogeny ,Hemorrhagic Fever ,Ebola ,Disease Outbreaks ,Genome ,Viral ,Mathematical Sciences ,Biological Sciences ,Information and Computing Sciences ,Bioinformatics - Abstract
SummaryMutations sometimes increase contagiousness for evolving pathogens. During an epidemic, scientists use viral genome data to infer a shared evolutionary history and connect this history to geographic spread. We propose a model that directly relates a pathogen's evolution to its spatial contagion dynamics-effectively combining the two epidemiological paradigms of phylogenetic inference and self-exciting process modeling-and apply this phylogenetic Hawkes process to a Bayesian analysis of 23 421 viral cases from the 2014 to 2016 Ebola outbreak in West Africa. The proposed model is able to detect individual viruses with significantly elevated rates of spatiotemporal propagation for a subset of 1610 samples that provide genome data. Finally, to facilitate model application in big data settings, we develop massively parallel implementations for the gradient and Hessian of the log-likelihood and apply our high-performance computing framework within an adaptively pre-conditioned Hamiltonian Monte Carlo routine.Supplementary informationSupplementary data are available at Bioinformatics online.
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- 2022
62. BAYESIAN MITIGATION OF SPATIAL COARSENING FOR A HAWKES MODEL APPLIED TO GUNFIRE, WILDFIRE AND VIRAL CONTAGION.
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Holbrook, Andrew, Ji, Xiang, and Suchard, Marc
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Bayesian multidimensional scaling ,gun violence ,self-exciting processes ,spatial coarsening ,viral contagion ,wildfires - Abstract
Self-exciting spatiotemporal Hawkes processes have found increasing use in the study of large-scale public health threats, ranging from gun violence and earthquakes to wildfires and viral contagion. Whereas many such applications feature locational uncertainty, that is, the exact spatial positions of individual events are unknown, most Hawkes model analyses to date have ignored spatial coarsening present in the data. Three particular 21st century public health crises-urban gun violence, rural wildfires and global viral spread-present qualitatively and quantitatively varying uncertainty regimes that exhibit: (a) different collective magnitudes of spatial coarsening, (b) uniform and mixed magnitude coarsening, (c) differently shaped uncertainty regions and-less orthodox-(d) locational data distributed within the wrong effective space. We explicitly model such uncertainties in a Bayesian manner and jointly infer unknown locations together with all parameters of a reasonably flexible Hawkes model, obtaining results that are practically and statistically distinct from those obtained while ignoring spatial coarsening. This work also features two different secondary contributions: first, to facilitate Bayesian inference of locations and background rate parameters, we make a subtle yet crucial change to an established kernel-based rate model, and second, to facilitate the same Bayesian inference at scale, we develop a massively parallel implementation of the models log-likelihood gradient with respect to locations and thus avoid its quadratic computational cost in the context of Hamiltonian Monte Carlo. Our examples involve thousands of observations and allow us to demonstrate practicality at moderate scales.
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- 2022
63. Phylogeography reveals association between swine trade and the spread of porcine epidemic diarrhea virus in China and across the world
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He, Wan-Ting, Bollen, Nena, Xu, Yi, Zhao, Jin, Dellicour, Simon, Yan, Ziqing, Gong, Wenjie, Zhang, Cheng, Zhang, Letian, Lu, Meng, Lai, Alexander, Suchard, Marc A, Ji, Xiang, Tu, Changchun, Lemey, Philippe, Baele, Guy, and Su, Shuo
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Biochemistry and Cell Biology ,Evolutionary Biology ,Genetics ,Biological Sciences ,Emerging Infectious Diseases ,Vaccine Related ,Prevention ,Infectious Diseases ,Infection ,Good Health and Well Being ,Animals ,China ,Coronavirus ,Pandemics ,Phylogeny ,Phylogeography ,Porcine epidemic diarrhea virus ,Swine ,Swine Diseases ,United States ,porcine epidemic diarrhea virus ,Coronaviridae ,phylogeography ,Bayesian inference ,generalized linear model ,BEAST ,Biochemistry and cell biology ,Evolutionary biology - Abstract
The ongoing SARS (severe acute respiratory syndrome)-CoV (coronavirus)-2 pandemic has exposed major gaps in our knowledge on the origin, ecology, evolution, and spread of animal coronaviruses. Porcine epidemic diarrhea virus (PEDV) is a member of the genus Alphacoronavirus in the family Coronaviridae that may have originated from bats and leads to significant hazards and widespread epidemics in the swine population. The role of local and global trade of live swine and swine-related products in disseminating PEDV remains unclear, especially in developing countries with complex swine production systems. Here, we undertake an in-depth phylogeographic analysis of PEDV sequence data (including 247 newly sequenced samples) and employ an extension of this inference framework that enables formally testing the contribution of a range of predictor variables to the geographic spread of PEDV. Within China, the provinces of Guangdong and Henan were identified as primary hubs for the spread of PEDV, for which we estimate live swine trade to play a very important role. On a global scale, the United States and China maintain the highest number of PEDV lineages. We estimate that, after an initial introduction out of China, the United States acted as an important source of PEDV introductions into Japan, Korea, China, and Mexico. Live swine trade also explains the dispersal of PEDV on a global scale. Given the increasingly global trade of live swine, our findings have important implications for designing prevention and containment measures to combat a wide range of livestock coronaviruses.
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- 2022
64. Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.
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Williams, Ross, Markus, Aniek, Yang, Cynthia, Duarte-Salles, Talita, DuVall, Scott, Falconer, Thomas, Jonnagaddala, Jitendra, Kim, Chungsoo, Rho, Yeunsook, Williams, Andrew, Machado, Amanda, An, Min, Aragón, María, Areia, Carlos, Burn, Edward, Choi, Young, Drakos, Iannis, Abrahão, Maria, Fernández-Bertolín, Sergio, Hripcsak, George, Kaas-Hansen, Benjamin, Kandukuri, Prasanna, Kors, Jan, Kostka, Kristin, Liaw, Siaw-Teng, Lynch, Kristine, Machnicki, Gerardo, Matheny, Michael, Morales, Daniel, Nyberg, Fredrik, Park, Rae, Prats-Uribe, Albert, Pratt, Nicole, Rao, Gowtham, Reich, Christian, Rivera, Marcela, Seinen, Tom, Shoaibi, Azza, Spotnitz, Matthew, Steyerberg, Ewout, Suchard, Marc, You, Seng, Zhang, Lin, Zhou, Lili, Ryan, Patrick, Prieto-Alhambra, Daniel, Reps, Jenna, and Rijnbeek, Peter
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COVID-19 ,Patient-level prediction modelling ,Risk score ,COVID-19 ,COVID-19 Testing ,Humans ,Influenza ,Human ,Pneumonia ,SARS-CoV-2 ,United States - Abstract
BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patients risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.
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- 2022
65. Global disparities in SARS-CoV-2 genomic surveillance
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Brito, Anderson F, Semenova, Elizaveta, Dudas, Gytis, Hassler, Gabriel W, Kalinich, Chaney C, Kraemer, Moritz UG, Ho, Joses, Tegally, Houriiyah, Githinji, George, Agoti, Charles N, Matkin, Lucy E, Whittaker, Charles, Howden, Benjamin P, Sintchenko, Vitali, Zuckerman, Neta S, Mor, Orna, Blankenship, Heather M, de Oliveira, Tulio, Lin, Raymond TP, Siqueira, Marilda Mendonça, Resende, Paola Cristina, Vasconcelos, Ana Tereza R, Spilki, Fernando R, Aguiar, Renato Santana, Alexiev, Ivailo, Ivanov, Ivan N, Philipova, Ivva, Carrington, Christine VF, Sahadeo, Nikita SD, Branda, Ben, Gurry, Céline, Maurer-Stroh, Sebastian, Naidoo, Dhamari, von Eije, Karin J, Perkins, Mark D, van Kerkhove, Maria, Hill, Sarah C, Sabino, Ester C, Pybus, Oliver G, Dye, Christopher, Bhatt, Samir, Flaxman, Seth, Suchard, Marc A, Grubaugh, Nathan D, Baele, Guy, and Faria, Nuno R
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Biological Sciences ,Bioinformatics and Computational Biology ,Health Sciences ,Prevention ,Human Genome ,Biodefense ,Vaccine Related ,Immunization ,Emerging Infectious Diseases ,Genetics ,Infection ,Good Health and Well Being ,Humans ,SARS-CoV-2 ,Genome ,Viral ,COVID-19 ,Pandemics ,Genomics ,Bulgarian SARS-CoV-2 sequencing group ,Communicable Diseases Genomics Network ,COVID-19 Impact Project ,Danish Covid-19 Genome Consortium ,Fiocruz COVID-19 Genomic Surveillance Network ,GISAID core curation team ,Network for Genomic Surveillance in South Africa ,Swiss SARS-CoV-2 Sequencing Consortium - Abstract
Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time
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- 2022
66. Corrigendum: Vaccine safety surveillance using routinely collected healthcare data—An empirical evaluation of epidemiological designs
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Schuemie, Martijn J, Arshad, Faaizah, Pratt, Nicole, Nyberg, Fredrik, Alshammari, Thamir M, Hripcsak, George, Ryan, Patrick, Prieto-Alhambra, Daniel, Lai, Lana YH, Li, Xintong, Fortin, Stephen, Minty, Evan, and Suchard, Marc A
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Pharmacology and Pharmaceutical Sciences ,Biomedical and Clinical Sciences ,Good Health and Well Being ,vaccine safely ,routinely collected data ,adverse event ,surveillance ,methods ,Pharmacology and pharmaceutical sciences - Abstract
[This corrects the article DOI: 10.3389/fphar.2022.893484.].
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- 2022
67. Characteristics and outcomes of COVID-19 patients with COPD from the United States, South Korea, and Europe
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Moreno-Martos, David, Verhamme, Katia, Ostropolets, Anna, Kostka, Kristin, Duarte-Sales, Talita, Prieto-Alhambra, Daniel, Alshammari, Thamir M, Alghoul, Heba, Ahmed, Waheed-Ul-Rahman, Blacketer, Clair, DuVall, Scott, Lai, Lana, Matheny, Michael, Nyberg, Fredrik, Posada, Jose, Rijnbeek, Peter, Spotnitz, Matthew, Sena, Anthony, Shah, Nigam, Suchard, Marc, You, Seng Chan, Hripcsak, George, Ryan, Patrick, and Morales, Daniel
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Biomedical and Clinical Sciences ,Clinical Sciences ,Chronic Obstructive Pulmonary Disease ,Infectious Diseases ,Lung ,Clinical Research ,Cardiovascular ,Respiratory ,Good Health and Well Being ,COPD ,COVID ,SARS-CoV-2 ,coronavirus ,epidemiology. ,Biomedical and clinical sciences ,Health sciences - Abstract
Background: Characterization studies of COVID-19 patients with chronic obstructive pulmonary disease (COPD) are limited in size and scope. The aim of the study is to provide a large-scale characterization of COVID-19 patients with COPD. Methods: We included thirteen databases contributing data from January-June 2020 from North America (US), Europe and Asia. We defined two cohorts of patients with COVID-19 namely a 'diagnosed' and 'hospitalized' cohort. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes among COPD patients with COVID-19. Results: The study included 934,778 patients in the diagnosed COVID-19 cohort and 177,201 in the hospitalized COVID-19 cohort. Observed COPD prevalence in the diagnosed cohort ranged from 3.8% (95%CI 3.5-4.1%) in French data to 22.7% (95%CI 22.4-23.0) in US data, and from 1.9% (95%CI 1.6-2.2) in South Korean to 44.0% (95%CI 43.1-45.0) in US data, in the hospitalized cohorts. COPD patients in the hospitalized cohort had greater comorbidity than those in the diagnosed cohort, including hypertension, heart disease, diabetes and obesity. Mortality was higher in COPD patients in the hospitalized cohort and ranged from 7.6% (95%CI 6.9-8.4) to 32.2% (95%CI 28.0-36.7) across databases. ARDS, acute renal failure, cardiac arrhythmia and sepsis were the most common outcomes among hospitalized COPD patients. Conclusion: COPD patients with COVID-19 have high levels of COVID-19-associated comorbidities and poor COVID-19 outcomes. Further research is required to identify patients with COPD at high risk of worse outcomes.
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- 2022
68. Vaccine Safety Surveillance Using Routinely Collected Healthcare Data—An Empirical Evaluation of Epidemiological Designs
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Schuemie, Martijn J, Arshad, Faaizah, Pratt, Nicole, Nyberg, Fredrik, Alshammari, Thamir M, Hripcsak, George, Ryan, Patrick, Prieto-Alhambra, Daniel, Lai, Lana YH, Li, Xintong, Fortin, Stephen, Minty, Evan, and Suchard, Marc A
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Immunization ,Patient Safety ,Vaccine Related ,Prevention ,Clinical Research ,Generic health relevance ,Good Health and Well Being ,vaccine safety ,routinely collected data ,adverse event ,surveillance ,methods ,Pharmacology and Pharmaceutical Sciences - Abstract
Background: Routinely collected healthcare data such as administrative claims and electronic health records (EHR) can complement clinical trials and spontaneous reports to detect previously unknown risks of vaccines, but uncertainty remains about the behavior of alternative epidemiologic designs to detect and declare a true risk early. Methods: Using three claims and one EHR database, we evaluate several variants of the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations using real negative control outcomes (outcomes with no evidence to suggest that they could be caused by the vaccines) and simulated positive control outcomes. Results: Most methods show large type 1 error, often identifying false positive signals. The cohort method appears either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using effect-size estimates for negative control outcomes can bring type 1 error closer to nominal, often at the cost of increasing type 2 error. After calibration, the self-controlled case series (SCCS) design most rapidly detects small true effect sizes, while the historical comparator performs well for strong effects. Conclusion: When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address differences between vaccinated and unvaccinated, and for the cohort method the choice of index date is important for the comparability of the groups. Analysis of negative control outcomes allows both quantification of the systematic error and, if desired, subsequent empirical calibration to restore type 1 error to its nominal value. In order to detect weaker signals, one may have to accept a higher type 1 error.
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- 2022
69. Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases
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Ostropolets, Anna, Li, Xintong, Makadia, Rupa, Rao, Gowtham, Rijnbeek, Peter R, Duarte-Salles, Talita, Sena, Anthony G, Shaoibi, Azza, Suchard, Marc A, Ryan, Patrick B, Prieto-Alhambra, Daniel, and Hripcsak, George
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Pharmacology and Pharmaceutical Sciences ,Biomedical and Clinical Sciences ,Prevention ,SARS-CoV-2 ,COVID-19 ,vaccine ,adverse events ,incidence rates ,background rates ,sensitivity analysis ,Pharmacology and pharmaceutical sciences - Abstract
Objective: Background incidence rates are routinely used in safety studies to evaluate an association of an exposure and outcome. Systematic research on sensitivity of rates to the choice of the study parameters is lacking. Materials and Methods: We used 12 data sources to systematically examine the influence of age, race, sex, database, time-at-risk, season and year, prior observation and clean window on incidence rates using 15 adverse events of special interest for COVID-19 vaccines as an example. For binary comparisons we calculated incidence rate ratios and performed random-effect meta-analysis. Results: We observed a wide variation of background rates that goes well beyond age and database effects previously observed. While rates vary up to a factor of 1,000 across age groups, even after adjusting for age and sex, the study showed residual bias due to the other parameters. Rates were highly influenced by the choice of anchoring (e.g., health visit, vaccination, or arbitrary date) for the time-at-risk start. Anchoring on a healthcare encounter yielded higher incidence comparing to a random date, especially for short time-at-risk. Incidence rates were highly influenced by the choice of the database (varying by up to a factor of 100), clean window choice and time-at-risk duration, and less so by secular or seasonal trends. Conclusion: Comparing background to observed rates requires appropriate adjustment and careful time-at-risk start and duration choice. Results should be interpreted in the context of study parameter choices.
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- 2022
70. Current Approaches to Vaccine Safety Using Observational Data: A Rationale for the EUMAEUS (Evaluating Use of Methods for Adverse Events Under Surveillance-for Vaccines) Study Design
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Lai, Lana YH, Arshad, Faaizah, Areia, Carlos, Alshammari, Thamir M, Alghoul, Heba, Casajust, Paula, Li, Xintong, Dawoud, Dalia, Nyberg, Fredrik, Pratt, Nicole, Hripcsak, George, Suchard, Marc A, Prieto-Alhambra, Dani, Ryan, Patrick, and Schuemie, Martijn J
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Pharmacology and Pharmaceutical Sciences ,Biomedical and Clinical Sciences ,Patient Safety ,Infectious Diseases ,Vaccine Related ,Immunization ,Prevention ,8.4 Research design and methodologies (health services) ,Health and social care services research ,Generic health relevance ,Good Health and Well Being ,vaccine safety surveillance ,methods evaluation ,real-world data ,study design ,bias ,Pharmacology and pharmaceutical sciences - Abstract
Post-marketing vaccine safety surveillance aims to detect adverse events following immunization in a population. Whether certain methods of surveillance are more precise and unbiased in generating safety signals is unclear. Here, we synthesized information from existing literature to provide an overview of the strengths, weaknesses, and clinical applications of epidemiologic and analytical methods used in vaccine monitoring, focusing on cohort, case-control and self-controlled designs. These designs are proposed to be evaluated in the EUMAEUS (Evaluating Use of Methods for Adverse Event Under Surveillance-for vaccines) study because of their widespread use and potential utility. Over the past decades, there have been an increasing number of epidemiological study designs used for vaccine safety surveillance. While traditional cohort and case-control study designs remain widely used, newer, novel designs such as the self-controlled case series and self-controlled risk intervals have been developed. Each study design comes with its strengths and limitations, and the most appropriate study design will depend on availability of resources, access to records, number and distribution of cases, and availability of population coverage data. Several assumptions have to be made while using the various study designs, and while the goal is to mitigate any biases, violations of these assumptions are often still present to varying degrees. In our review, we discussed some of the potential biases (i.e., selection bias, misclassification bias and confounding bias), and ways to mitigate them. While the types of epidemiological study designs are well established, a comprehensive comparison of the analytical aspects (including method evaluation and performance metrics) of these study designs are relatively less well studied. We summarized the literature, reporting on two simulation studies, which compared the detection time, empirical power, error rate and risk estimate bias across the above-mentioned study designs. While these simulation studies provided insights on the analytic performance of each of the study designs, its applicability to real-world data remains unclear. To bridge that gap, we provided the rationale of the EUMAEUS study, with a brief description of the study design; and how the use of real-world multi-database networks can provide insights into better methods evaluation and vaccine safety surveillance.
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- 2022
71. Regional connectivity drove bidirectional transmission of SARS-CoV-2 in the Middle East during travel restrictions
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Parker, Edyth, Anderson, Catelyn, Zeller, Mark, Tibi, Ahmad, Havens, Jennifer L, Laroche, Geneviève, Benlarbi, Mehdi, Ariana, Ardeshir, Robles-Sikisaka, Refugio, Latif, Alaa Abdel, Watts, Alexander, Awidi, Abdalla, Jaradat, Saied A, Gangavarapu, Karthik, Ramesh, Karthik, Kurzban, Ezra, Matteson, Nathaniel L, Han, Alvin X, Hughes, Laura D, McGraw, Michelle, Spencer, Emily, Nicholson, Laura, Khan, Kamran, Suchard, Marc A, Wertheim, Joel O, Wohl, Shirlee, Côté, Marceline, Abdelnour, Amid, Andersen, Kristian G, and Abu-Dayyeh, Issa
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Life on Land ,COVID-19 ,Humans ,Middle East ,Pandemics ,SARS-CoV-2 ,Travel - Abstract
Regional connectivity and land travel have been identified as important drivers of SARS-CoV-2 transmission. However, the generalizability of this finding is understudied outside of well-sampled, highly connected regions. In this study, we investigated the relative contributions of regional and intercontinental connectivity to the source-sink dynamics of SARS-CoV-2 for Jordan and the Middle East. By integrating genomic, epidemiological and travel data we show that the source of introductions into Jordan was dynamic across 2020, shifting from intercontinental seeding in the early pandemic to more regional seeding for the travel restrictions period. We show that land travel, particularly freight transport, drove introduction risk during the travel restrictions period. High regional connectivity and land travel also drove Jordan's export risk. Our findings emphasize regional connectedness and land travel as drivers of transmission in the Middle East.
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- 2022
72. Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS
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Kostka, Kristin, Duarte-Salles, Talita, Prats-Uribe, Albert, Sena, Anthony G, Pistillo, Andrea, Khalid, Sara, Lai, Lana YH, Golozar, Asieh, Alshammari, Thamir M, Dawoud, Dalia M, Nyberg, Fredrik, Wilcox, Adam B, Andryc, Alan, Williams, Andrew, Ostropolets, Anna, Areia, Carlos, Jung, Chi Young, Harle, Christopher A, Reich, Christian G, Blacketer, Clair, Morales, Daniel R, Dorr, David A, Burn, Edward, Roel, Elena, Tan, Eng Hooi, Minty, Evan, DeFalco, Frank, de Maeztu, Gabriel, Lipori, Gigi, Alghoul, Hiba, Zhu, Hong, Thomas, Jason A, Bian, Jiang, Park, Jimyung, Roldán, Jordi Martínez, Posada, Jose D, Banda, Juan M, Horcajada, Juan P, Kohler, Julianna, Shah, Karishma, Natarajan, Karthik, Lynch, Kristine E, Liu, Li, Schilling, Lisa M, Recalde, Martina, Spotnitz, Matthew, Gong, Mengchun, Matheny, Michael E, Valveny, Neus, Weiskopf, Nicole G, Shah, Nigam, Alser, Osaid, Casajust, Paula, Park, Rae Woong, Schuff, Robert, Seager, Sarah, DuVall, Scott L, You, Seng Chan, Song, Seokyoung, Fernández-Bertolín, Sergio, Fortin, Stephen, Magoc, Tanja, Falconer, Thomas, Subbian, Vignesh, Huser, Vojtech, Ahmed, Waheed-Ul-Rahman, Carter, William, Guan, Yin, Galvan, Yankuic, He, Xing, Rijnbeek, Peter R, Hripcsak, George, Ryan, Patrick B, Suchard, Marc A, and Prieto-Alhambra, Daniel
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Clinical Research ,Infectious Diseases ,Prevention ,2.4 Surveillance and distribution ,Aetiology ,Good Health and Well Being ,OHDSI ,OMOP CDM ,descriptive epidemiology ,real world data ,real world evidence ,open science ,Clinical Sciences ,Public Health and Health Services - Abstract
PurposeRoutinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and methodsWe conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.ResultsWe aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.ConclusionWe constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.
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- 2022
73. Archival influenza virus genomes from Europe reveal genomic variability during the 1918 pandemic
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Patrono, Livia V, Vrancken, Bram, Budt, Matthias, Düx, Ariane, Lequime, Sebastian, Boral, Sengül, Gilbert, M Thomas P, Gogarten, Jan F, Hoffmann, Luisa, Horst, David, Merkel, Kevin, Morens, David, Prepoint, Baptiste, Schlotterbeck, Jasmin, Schuenemann, Verena J, Suchard, Marc A, Taubenberger, Jeffery K, Tenkhoff, Luisa, Urban, Christian, Widulin, Navena, Winter, Eduard, Worobey, Michael, Schnalke, Thomas, Wolff, Thorsten, Lemey, Philippe, and Calvignac-Spencer, Sébastien
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Influenza ,Infectious Diseases ,Pneumonia & Influenza ,Biodefense ,Vaccine Related ,Human Genome ,Prevention ,Emerging Infectious Diseases ,Genetics ,2.2 Factors relating to the physical environment ,Aetiology ,Infection ,Genome ,Viral ,Genomics ,Humans ,Influenza A Virus ,H1N1 Subtype ,Influenza A virus ,Influenza ,Human - Abstract
The 1918 influenza pandemic was the deadliest respiratory pandemic of the 20th century and determined the genomic make-up of subsequent human influenza A viruses (IAV). Here, we analyze both the first 1918 IAV genomes from Europe and the first from samples prior to the autumn peak. 1918 IAV genomic diversity is consistent with a combination of local transmission and long-distance dispersal events. Comparison of genomes before and during the pandemic peak shows variation at two sites in the nucleoprotein gene associated with resistance to host antiviral response, pointing at a possible adaptation of 1918 IAV to humans. Finally, local molecular clock modeling suggests a pure pandemic descent of seasonal H1N1 IAV as an alternative to the hypothesis of origination through an intrasubtype reassortment.
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- 2022
74. The phylodynamics of SARS-CoV-2 during 2020 in Finland.
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Truong Nguyen, Phuoc, Kant, Ravi, Van den Broeck, Frederik, Suvanto, Maija T, Alburkat, Hussein, Virtanen, Jenni, Ahvenainen, Ella, Castren, Robert, Hong, Samuel L, Baele, Guy, Ahava, Maarit J, Jarva, Hanna, Jokiranta, Suvi Tuulia, Kallio-Kokko, Hannimari, Kekäläinen, Eliisa, Kirjavainen, Vesa, Kortela, Elisa, Kurkela, Satu, Lappalainen, Maija, Liimatainen, Hanna, Suchard, Marc A, Hannula, Sari, Ellonen, Pekka, Sironen, Tarja, Lemey, Philippe, Vapalahti, Olli, and Smura, Teemu
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SARS-CoV-2 ,Viral epidemiology ,Pneumonia & Influenza ,Vaccine Related ,Biodefense ,Emerging Infectious Diseases ,Prevention ,Infectious Diseases ,Infection ,Good Health and Well Being - Abstract
BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of infections and fatalities globally since its emergence in late 2019. The virus was first detected in Finland in January 2020, after which it rapidly spread among the populace in spring. However, compared to other European nations, Finland has had a low incidence of SARS-CoV-2. To gain insight into the origins and turnover of SARS-CoV-2 lineages circulating in Finland in 2020, we investigated the phylogeographic and -dynamic history of the virus.MethodsThe origins of SARS-CoV-2 introductions were inferred via Travel-aware Bayesian time-measured phylogeographic analyses. Sequences for the analyses included virus genomes belonging to the B.1 lineage and with the D614G mutation from countries of likely origin, which were determined utilizing Google mobility data. We collected all available sequences from spring and fall peaks to study lineage dynamics.ResultsWe observed rapid turnover among Finnish lineages during this period. Clade 20C became the most prevalent among sequenced cases and was replaced by other strains in fall 2020. Bayesian phylogeographic reconstructions suggested 42 independent introductions into Finland during spring 2020, mainly from Italy, Austria, and Spain.ConclusionsA single introduction from Spain might have seeded one-third of cases in Finland during spring in 2020. The investigations of the original introductions of SARS-CoV-2 to Finland during the early stages of the pandemic and of the subsequent lineage dynamics could be utilized to assess the role of transboundary movements and the effects of early intervention and public health measures.
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- 2022
75. Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades
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Klitting, Raphaëlle, Kafetzopoulou, Liana E, Thiery, Wim, Dudas, Gytis, Gryseels, Sophie, Kotamarthi, Anjali, Vrancken, Bram, Gangavarapu, Karthik, Momoh, Mambu, Sandi, John Demby, Goba, Augustine, Alhasan, Foday, Grant, Donald S, Okogbenin, Sylvanus, Ogbaini-Emovo, Ephraim, Garry, Robert F, Smither, Allison R, Zeller, Mark, Pauthner, Matthias G, McGraw, Michelle, Hughes, Laura D, Duraffour, Sophie, Günther, Stephan, Suchard, Marc A, Lemey, Philippe, Andersen, Kristian G, and Dellicour, Simon
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Infectious Diseases ,Emerging Infectious Diseases ,Rare Diseases ,Aetiology ,2.2 Factors relating to the physical environment ,Infection ,Life on Land ,Animals ,Humans ,Lassa Fever ,Lassa virus ,Phylogeography ,Risk Factors ,Rodentia - Abstract
Lassa fever is a severe viral hemorrhagic fever caused by a zoonotic virus that repeatedly spills over to humans from its rodent reservoirs. It is currently not known how climate and land use changes could affect the endemic area of this virus, currently limited to parts of West Africa. By exploring the environmental data associated with virus occurrence using ecological niche modelling, we show how temperature, precipitation and the presence of pastures determine ecological suitability for virus circulation. Based on projections of climate, land use, and population changes, we find that regions in Central and East Africa will likely become suitable for Lassa virus over the next decades and estimate that the total population living in ecological conditions that are suitable for Lassa virus circulation may drastically increase by 2070. By analysing geotagged viral genomes using spatially-explicit phylogeography and simulating virus dispersal, we find that in the event of Lassa virus being introduced into a new suitable region, its spread might remain spatially limited over the first decades.
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- 2022
76. Shrinkage-based random local clocks with scalable inference
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Fisher, Alexander A., Ji, Xiang, Nishimura, Akihiko, Lemey, Philippe, and Suchard, Marc A.
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Statistics - Methodology ,Quantitative Biology - Populations and Evolution - Abstract
Local clock models propose that the rate of molecular evolution is constant within phylogenetic sub-trees. Current local clock inference procedures scale poorly to large taxa problems, impose model misspecification, or require a priori knowledge of the existence and location of clocks. To overcome these challenges, we present an autocorrelated, Bayesian model of heritable clock rate evolution that leverages heavy-tailed priors with mean zero to shrink increments of change between branch-specific clocks. We further develop an efficient Hamiltonian Monte Carlo sampler that exploits closed form gradient computations to scale our model to large trees. Inference under our shrinkage-clock exhibits an over 3-fold speed increase compared to the popular random local clock when estimating branch-specific clock rates on a simulated dataset. We further show our shrinkage-clock recovers known local clocks within a rodent and mammalian phylogeny. Finally, in a problem that once appeared computationally impractical, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about clock placement., Comment: 24 pages, 6 figures
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- 2021
77. Zigzag path connects two Monte Carlo samplers: Hamiltonian counterpart to a piecewise deterministic Markov process
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Nishimura, Akihiko, Zhang, Zhenyu, and Suchard, Marc A.
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Statistics - Computation ,Mathematics - Probability - Abstract
Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-the-art sampler, exploiting fictitious momentum to guide Markov chains through complex target distributions. We establish an important connection between the zigzag sampler and a variant of Hamiltonian Monte Carlo based on Laplace-distributed momentum. The position and velocity component of the corresponding Hamiltonian dynamics travels along a zigzag path paralleling the Markovian zigzag process; however, the dynamics is non-Markovian in this position-velocity space as the momentum component encodes non-immediate pasts. This information is partially lost during a momentum refreshment step, in which we preserve its direction but re-sample magnitude. In the limit of increasingly frequent momentum refreshments, we prove that Hamiltonian zigzag converges strongly to its Markovian counterpart. This theoretical insight suggests that, when retaining full momentum information, Hamiltonian zigzag can better explore target distributions with highly correlated parameters by suppressing the diffusive behavior of Markovian zigzag. We corroborate this intuition by comparing performance of the two zigzag cousins on high-dimensional truncated multivariate Gaussians, including a 11,235-dimensional target arising from a Bayesian phylogenetic multivariate probit modeling of HIV virus data., Comment: Code available at https://github.com/aki-nishimura/code-for-hamiltonian-zigzag-2024 and data at http://doi.org/10.5281/zenodo.4679720
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- 2021
78. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations
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Gangavarapu, Karthik, Latif, Alaa Abdel, Mullen, Julia L., Alkuzweny, Manar, Hufbauer, Emory, Tsueng, Ginger, Haag, Emily, Zeller, Mark, Aceves, Christine M., Zaiets, Karina, Cano, Marco, Zhou, Xinghua, Qian, Zhongchao, Sattler, Rachel, Matteson, Nathaniel L., Levy, Joshua I., Lee, Raphael T. C., Freitas, Lucas, Maurer-Stroh, Sebastian, Suchard, Marc A., Wu, Chunlei, Su, Andrew I., Andersen, Kristian G., and Hughes, Laura D.
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- 2023
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79. Risk assessment of SARS-CoV-2 replicating and evolving in animals
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Zhao, Jin, Kang, Mei, Wu, Hongyan, Sun, Bowen, Baele, Guy, He, Wan-Ting, Lu, Meng, Suchard, Marc A., Ji, Xiang, He, Na, Su, Shuo, and Veit, Michael
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- 2024
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80. From viral evolution to spatial contagion: a biologically modulated Hawkes model
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Holbrook, Andrew J., Ji, Xiang, and Suchard, Marc A.
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Quantitative Biology - Populations and Evolution ,Statistics - Applications - Abstract
Mutations sometimes increase contagiousness for evolving pathogens. During an epidemic, scientists use viral genome data to infer a shared evolutionary history and connect this history to geographic spread. We propose a model that directly relates a pathogen's evolution to its spatial contagion dynamics -- effectively combining the two epidemiological paradigms of phylogenetic inference and self-exciting process modeling -- and apply this \emph{phylogenetic Hawkes process} to a Bayesian analysis of 23,422 viral cases from the 2014-2016 Ebola outbreak in West Africa. The proposed model is able to detect individual viruses with significantly elevated rates of spatiotemporal propagation for a subset of 1,610 samples that provide genome data. Finally, to facilitate model application in big data settings, we develop massively parallel implementations for the gradient and Hessian of the log-likelihood and apply our high performance computing framework within an adaptively preconditioned Hamiltonian Monte Carlo routine.
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- 2021
81. Combining Cox Regressions Across a Heterogeneous Distributed Research Network Facing Small and Zero Counts
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Schuemie, Martijn J., Chen, Yong, Madigan, David, and Suchard, Marc A.
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Statistics - Methodology - Abstract
Studies of the effects of medical interventions increasingly take place in distributed research settings using data from multiple clinical data sources including electronic health records and administrative claims. In such settings, privacy concerns typically prohibit sharing of individual patient data, and instead, analyses can only utilize summary statistics from the individual databases. In the specific but very common context of the Cox proportional hazards model, we show that standard meta analysis methods then lead to substantial bias when outcome counts are small. This bias derives primarily from the normal approximations that the methods utilize. Here we propose and evaluate methods that eschew normal approximations in favor of three more flexible approximations: a skew-normal, a one-dimensional grid, and a custom parametric function that mimics the behavior of the Cox likelihood function. In extensive simulation studies we demonstrate how these approximations impact bias in the context of both fixed-effects and (Bayesian) random-effects models. We then apply these approaches to three real-world studies of the comparative safety of antidepressants, each using data from four observational healthcare databases., Comment: 13 pages, 4 figures, 2 tables
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- 2021
82. Characteristics and outcomes of patients with COVID-19 with and without prevalent hypertension: a multinational cohort study
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Reyes, Carlen, Pistillo, Andrea, Fernández-Bertolín, Sergio, Recalde, Martina, Roel, Elena, Puente, Diana, Sena, Anthony G, Blacketer, Clair, Lai, Lana, Alshammari, Thamir M, Ahmed, Waheed-UI-Rahman, Alser, Osaid, Alghoul, Heba, Areia, Carlos, Dawoud, Dalia, Prats-Uribe, Albert, Valveny, Neus, de Maeztu, Gabriel, Redó, Luisa Sorlí, Roldan, Jordi Martinez, Montesinos, Inmaculada Lopez, Schilling, Lisa M, Golozar, Asieh, Reich, Christian, Posada, Jose D, Shah, Nigam, You, Seng Chan, Lynch, Kristine E, DuVall, Scott L, Matheny, Michael E, Nyberg, Fredrik, Ostropolets, Anna, Hripcsak, George, Rijnbeek, Peter R, Suchard, Marc A, Ryan, Patrick, Kostka, Kristin, and Duarte-Salles, Talita
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Clinical Research ,Cardiovascular ,Aetiology ,2.4 Surveillance and distribution ,Good Health and Well Being ,COVID-19 ,COVID-19 Testing ,Cohort Studies ,Comorbidity ,Female ,Hospitalization ,Humans ,Hypertension ,Middle Aged ,Retrospective Studies ,SARS-CoV-2 ,epidemiology ,hypertension ,Clinical Sciences ,Public Health and Health Services ,Other Medical and Health Sciences - Abstract
ObjectiveTo characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients.Design and settingThis is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020.ParticipantsTwo non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days.OutcomesDemographics, comorbidities and 30-day outcomes (hospitalisation and death for the 'diagnosed' cohort and adverse events and death for the 'hospitalised' cohort) were reported.ResultsWe identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension.ConclusionsCOVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.
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- 2021
83. A phylogenetic approach for weighting genetic sequences
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De Maio, Nicola, Alekseyenko, Alexander V, Coleman-Smith, William J, Pardi, Fabio, Suchard, Marc A, Tamuri, Asif U, Truszkowski, Jakub, and Goldman, Nick
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Biological Sciences ,Bioinformatics and Computational Biology ,Evolutionary Biology ,Genetics ,Generic health relevance ,Algorithms ,Computational Biology ,Phylogeny ,Sequence Alignment ,Phylogenetics ,Sequence weights ,Alignment ,Protein profile ,Conservation scores ,Mathematical Sciences ,Information and Computing Sciences ,Bioinformatics ,Biological sciences ,Information and computing sciences ,Mathematical sciences - Abstract
BackgroundMany important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are 'novel' compared to the others in the same dataset, and low weights to sequences that are over-represented.ResultsWe formalise this principle by rigorously defining the evolutionary 'novelty' of a sequence within an alignment. This results in new sequence weights that we call 'phylogenetic novelty scores'. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column-important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they are versatile and can improve the accuracy of character frequency estimation compared to existing sequence weighting schemes.ConclusionsOur phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy.
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- 2021
84. A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data
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Khalid, Sara, Yang, Cynthia, Blacketer, Clair, Duarte-Salles, Talita, Fernández-Bertolín, Sergio, Kim, Chungsoo, Park, Rae Woong, Park, Jimyung, Schuemie, Martijn J, Sena, Anthony G, Suchard, Marc A, You, Seng Chan, Rijnbeek, Peter R, and Reps, Jenna M
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Information and Computing Sciences ,Software Engineering ,Networking and Information Technology R&D (NITRD) ,Bioengineering ,Generic health relevance ,COVID-19 ,Humans ,Logistic Models ,Machine Learning ,Pandemics ,SARS-CoV-2 ,Data harmonization ,Data quality control ,Distributed data network ,Machine learning ,Risk prediction ,Artificial Intelligence and Image Processing ,Biomedical Engineering ,Electrical and Electronic Engineering ,Medical Informatics ,Biomedical engineering ,Applied computing ,Computer vision and multimedia computation - Abstract
Background and objectiveAs a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code).MethodsWe show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA.ResultsOur open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated.ConclusionOur results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.
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- 2021
85. COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries
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Tan, Eng Hooi, Sena, Anthony G, Prats-Uribe, Albert, You, Seng Chan, Ahmed, Waheed-Ul-Rahman, Kostka, Kristin, Reich, Christian, Duvall, Scott L, Lynch, Kristine E, Matheny, Michael E, Duarte-Salles, Talita, Bertolin, Sergio Fernandez, Hripcsak, George, Natarajan, Karthik, Falconer, Thomas, Spotnitz, Matthew, Ostropolets, Anna, Blacketer, Clair, Alshammari, Thamir M, Alghoul, Heba, Alser, Osaid, Lane, Jennifer CE, Dawoud, Dalia M, Shah, Karishma, Yang, Yue, Zhang, Lin, Areia, Carlos, Golozar, Asieh, Recalde, Martina, Casajust, Paula, Jonnagaddala, Jitendra, Subbian, Vignesh, Vizcaya, David, Lai, Lana YH, Nyberg, Fredrik, Morales, Daniel R, Posada, Jose D, Shah, Nigam H, Gong, Mengchun, Vivekanantham, Arani, Abend, Aaron, Minty, Evan P, Suchard, Marc, Rijnbeek, Peter, Ryan, Patrick B, and Prieto-Alhambra, Daniel
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Infectious Diseases ,Lung ,Emerging Infectious Diseases ,Pneumonia & Influenza ,Clinical Research ,Influenza ,Autoimmune Disease ,Cardiovascular ,Good Health and Well Being ,Adult ,Aged ,Aged ,80 and over ,Autoimmune Diseases ,COVID-19 ,Cohort Studies ,Female ,Hospitalization ,Humans ,Influenza ,Human ,Male ,Middle Aged ,Prevalence ,Prognosis ,Republic of Korea ,SARS-CoV-2 ,Spain ,United States ,Young Adult ,autoimmune condition ,mortality ,hospitalization ,open science ,Observational Health Data Sciences and Informatics ,Observational Medical Outcomes Partnership ,Clinical Sciences ,Immunology ,Public Health and Health Services ,Arthritis & Rheumatology - Abstract
ObjectivePatients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.MethodsA multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center [USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017-18 were included. Outcomes were death and complications within 30 days of hospitalization.ResultsWe studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2-4.3% vs 6.32-24.6%).ConclusionCompared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.
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- 2021
86. Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and SpainCharacteristics of 300,000 COVID-19 Individuals with Cancer
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Roel, Elena, Pistillo, Andrea, Recalde, Martina, Sena, Anthony G, Fernández-Bertolín, Sergio, Aragón, Maria, Puente, Diana, Ahmed, Waheed-Ul-Rahman, Alghoul, Heba, Alser, Osaid, Alshammari, Thamir M, Areia, Carlos, Blacketer, Clair, Carter, William, Casajust, Paula, Culhane, Aedin C, Dawoud, Dalia, DeFalco, Frank, DuVall, Scott L, Falconer, Thomas, Golozar, Asieh, Gong, Mengchun, Hester, Laura, Hripcsak, George, Tan, Eng Hooi, Jeon, Hokyun, Jonnagaddala, Jitendra, Lai, Lana YH, Lynch, Kristine E, Matheny, Michael E, Morales, Daniel R, Natarajan, Karthik, Nyberg, Fredrik, Ostropolets, Anna, Posada, José D, Prats-Uribe, Albert, Reich, Christian G, Rivera, Donna R, Schilling, Lisa M, Soerjomataram, Isabelle, Shah, Karishma, Shah, Nigam H, Shen, Yang, Spotniz, Matthew, Subbian, Vignesh, Suchard, Marc A, Trama, Annalisa, Zhang, Lin, Zhang, Ying, Ryan, Patrick B, Prieto-Alhambra, Daniel, Kostka, Kristin, and Duarte-Salles, Talita
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Biomedical and Clinical Sciences ,Health Services and Systems ,Health Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Breast Cancer ,Patient Safety ,Infectious Diseases ,Rare Diseases ,Hematology ,Cancer ,Urologic Diseases ,Prevention ,Clinical Research ,Aetiology ,2.4 Surveillance and distribution ,Good Health and Well Being ,Adolescent ,Adult ,Aged ,Aged ,80 and over ,COVID-19 ,Child ,Cohort Studies ,Comorbidity ,Databases ,Factual ,Female ,Hospitalization ,Humans ,Immunosuppression Therapy ,Influenza ,Human ,Male ,Middle Aged ,Neoplasms ,Outcome Assessment ,Health Care ,Pandemics ,Prevalence ,Risk Factors ,SARS-CoV-2 ,Spain ,United States ,Young Adult ,Medical and Health Sciences ,Epidemiology ,Biomedical and clinical sciences ,Health sciences - Abstract
BackgroundWe described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.MethodsWe conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.ResultsWe included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.ConclusionsPatients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.ImpactThis study provides epidemiologic characteristics that can inform clinical care and etiologic studies.
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- 2021
87. Bayesian mitigation of spatial coarsening for a Hawkes model applied to gunfire, wildfire and viral contagion
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Holbrook, Andrew J., Ji, Xiang, and Suchard, Marc A.
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Statistics - Methodology - Abstract
Self-exciting spatiotemporal Hawkes processes have found increasing use in the study of large-scale public health threats ranging from gun violence and earthquakes to wildfires and viral contagion. Whereas many such applications feature locational uncertainty, i.e., the exact spatial positions of individual events are unknown, most Hawkes model analyses to date have ignored spatial coarsening present in the data. Three particular 21st century public health crises -- urban gun violence, rural wildfires and global viral spread -- present qualitatively and quantitatively varying uncertainty regimes that exhibit (a) different collective magnitudes of spatial coarsening, (b) uniform and mixed magnitude coarsening, (c) differently shaped uncertainty regions and -- less orthodox -- (d) locational data distributed within the `wrong' effective space. We explicitly model such uncertainties in a Bayesian manner and jointly infer unknown locations together with all parameters of a reasonably flexible Hawkes model, obtaining results that are practically and statistically distinct from those obtained while ignoring spatial coarsening. This work also features two different secondary contributions: first, to facilitate Bayesian inference of locations and background rate parameters, we make a subtle yet crucial change to an established kernel-based rate model; and second, to facilitate the same Bayesian inference at scale, we develop a massively parallel implementation of the model's log-likelihood gradient with respect to locations and thus avoid its quadratic computational cost in the context of Hamiltonian Monte Carlo. Our examples involve thousands of observations and allow us to demonstrate practicality at moderate scales., Comment: To appear in AOAS
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- 2020
88. Comparative First-Line Effectiveness and Safety of ACE (Angiotensin-Converting Enzyme) Inhibitors and Angiotensin Receptor Blockers: A Multinational Cohort Study.
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Chen, RuiJun, Suchard, Marc A, Krumholz, Harlan M, Schuemie, Martijn J, Shea, Steven, Duke, Jon, Pratt, Nicole, Reich, Christian G, Madigan, David, You, Seng Chan, Ryan, Patrick B, and Hripcsak, George
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Humans ,Hypertension ,Antihypertensive Agents ,Angiotensin-Converting Enzyme Inhibitors ,Treatment Outcome ,Retrospective Studies ,Adolescent ,Adult ,Aged ,Aged ,80 and over ,Middle Aged ,Child ,Child ,Preschool ,Infant ,Female ,Male ,Young Adult ,Angiotensin Receptor Antagonists ,angiotensin receptor ,angiotensin receptor blocker ,cardiovascular outcomes ,hypertension ,safety ,Cardiorespiratory Medicine and Haematology ,Clinical Sciences ,Public Health and Health Services ,Cardiovascular System & Hematology - Abstract
[Figure: see text].
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- 2021
89. Emergence of an early SARS-CoV-2 epidemic in the United States.
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Zeller, Mark, Gangavarapu, Karthik, Anderson, Catelyn, Smither, Allison R, Vanchiere, John A, Rose, Rebecca, Snyder, Daniel J, Dudas, Gytis, Watts, Alexander, Matteson, Nathaniel L, Robles-Sikisaka, Refugio, Marshall, Maximilian, Feehan, Amy K, Sabino-Santos, Gilberto, Bell-Kareem, Antoinette R, Hughes, Laura D, Alkuzweny, Manar, Snarski, Patricia, Garcia-Diaz, Julia, Scott, Rona S, Melnik, Lilia I, Klitting, Raphaëlle, McGraw, Michelle, Belda-Ferre, Pedro, DeHoff, Peter, Sathe, Shashank, Marotz, Clarisse, Grubaugh, Nathan D, Nolan, David J, Drouin, Arnaud C, Genemaras, Kaylynn J, Chao, Karissa, Topol, Sarah, Spencer, Emily, Nicholson, Laura, Aigner, Stefan, Yeo, Gene W, Farnaes, Lauge, Hobbs, Charlotte A, Laurent, Louise C, Knight, Rob, Hodcroft, Emma B, Khan, Kamran, Fusco, Dahlene N, Cooper, Vaughn S, Lemey, Phillipe, Gardner, Lauren, Lamers, Susanna L, Kamil, Jeremy P, Garry, Robert F, Suchard, Marc A, and Andersen, Kristian G
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Humans ,Risk Factors ,Disease Outbreaks ,Phylogeny ,Travel ,United States ,Louisiana ,Texas ,Databases as Topic ,Epidemics ,COVID-19 ,SARS-CoV-2 ,genomic epidemiology ,mobility ,phylogenetics ,viral emergence ,viral sequencing ,Emerging Infectious Diseases ,Biodefense ,Prevention ,Vaccine Related ,Lung ,Developmental Biology ,Biological Sciences ,Medical and Health Sciences - Abstract
The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics.
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- 2021
90. Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI
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Hripcsak, George, Schuemie, Martijn, Madigan, David, Ryan, Patrick, and Suchard, Marc
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Epidemiology ,Health Sciences ,Networking and Information Technology R&D (NITRD) ,8.4 Research design and methodologies (health services) ,Health and social care services research ,Generic health relevance ,Good Health and Well Being ,COVID-19 ,Humans ,Information Dissemination ,Observational Studies as Topic ,Publication Bias ,Reproducibility of Results ,Biochemistry and Cell Biology ,Library and Information Studies ,Public Health and Health Services ,Health services and systems ,Public health - Abstract
ObjectiveThe current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science.MethodsOHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world's population. Using a common data model with a practical schema and extensive vocabulary mappings, data from around the world follow the identical format. OHDSI's research methods emphasize reproducibility, with a large-scale approach to addressing confounding using propensity score adjustment with extensive diagnostics; negative and positive control hypotheses to test for residual systematic error; a variety of data sources to assess consistency and generalizability; a completely open approach including protocol, software, models, parameters, and raw results so that studies can be externally verified; and the study of many hypotheses in parallel so that the operating characteristics of the methods can be assessed.ResultsOHDSI has already produced findings in areas like hypertension treatment that are being incorporated into practice, and it has produced rigorous studies of COVID-19 that have aided government agencies in their treatment decisions, that have characterized the disease extensively, that have estimated the comparative effects of treatments, and that the predict likelihood of advancing to serious complications.ConclusionsOHDSI practices open science and incorporates a series of methods to address reproducibility. It has produced important results in several areas, including hypertension therapy and COVID-19 research.
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- 2021
91. Untangling introductions and persistence in COVID-19 resurgence in Europe
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Lemey, Philippe, Ruktanonchai, Nick, Hong, Samuel L, Colizza, Vittoria, Poletto, Chiara, Van den Broeck, Frederik, Gill, Mandev S, Ji, Xiang, Levasseur, Anthony, Oude Munnink, Bas B, Koopmans, Marion, Sadilek, Adam, Lai, Shengjie, Tatem, Andrew J, Baele, Guy, Suchard, Marc A, and Dellicour, Simon
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Infectious Diseases ,Prevention ,Infection ,Good Health and Well Being ,COVID-19 ,Europe ,Genome ,Viral ,Humans ,Incidence ,Locomotion ,Phylogeny ,Phylogeography ,SARS-CoV-2 ,Time Factors ,Travel ,General Science & Technology - Abstract
After the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting in late summer 2020 that was deadlier and more difficult to contain1. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave2. Here we build a phylogeographical model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the resurgence of COVID-19 in Europe. We inform this model using genomic, mobility and epidemiological data from 10 European countries and estimate that in many countries more than half of the lineages circulating in late summer resulted from new introductions since 15 June 2020. The success in onward transmission of newly introduced lineages was negatively associated with the local incidence of COVID-19 during this period. The pervasive spread of variants in summer 2020 highlights the threat of viral dissemination when restrictions are lifted, and this needs to be carefully considered in strategies to control the current spread of variants that are more transmissible and/or evade immunity. Our findings indicate that more effective and coordinated measures are required to contain the spread through cross-border travel even as vaccination is reducing disease burden.
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- 2021
92. Relax, keep walking—a practical guide to continuous phylogeographic inference with BEAST
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Dellicour, Simon, Gill, Mandev S, Faria, Nuno R, Rambaut, Andrew, Pybus, Oliver G, Suchard, Marc A, and Lemey, Philippe
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Bayes Theorem ,Biological Evolution ,Phylogeography ,Software ,relaxed random walk ,continuous phylogeography ,viruses ,BEAST ,Biochemistry and Cell Biology ,Evolutionary Biology ,Genetics - Abstract
Spatially explicit phylogeographic analyses can be performed with an inference framework that employs relaxed random walks to reconstruct phylogenetic dispersal histories in continuous space. This core model was first implemented 10 years ago and has opened up new opportunities in the field of phylodynamics, allowing researchers to map and analyze the spatial dissemination of rapidly evolving pathogens. We here provide a detailed and step-by-step guide on how to set up, run, and interpret continuous phylogeographic analyses using the programs BEAUti, BEAST, Tracer, and TreeAnnotator.
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- 2021
93. Ebola vaccine–induced protection in nonhuman primates correlates with antibody specificity and Fc-mediated effects
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Meyer, Michelle, Gunn, Bronwyn M, Malherbe, Delphine C, Gangavarapu, Karthik, Yoshida, Asuka, Pietzsch, Colette, Kuzmina, Natalia A, Saphire, Erica Ollmann, Collins, Peter L, Crowe, James E, Zhu, James J, Suchard, Marc A, Brining, Douglas L, Mire, Chad E, Cross, Robert W, Geisbert, Joan B, Samal, Siba K, Andersen, Kristian G, Alter, Galit, Geisbert, Thomas W, and Bukreyev, Alexander
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Emerging Infectious Diseases ,Vaccine Related ,Biodefense ,Vaccine Related (AIDS) ,Biotechnology ,Prevention ,Immunization ,3.4 Vaccines ,Prevention of disease and conditions ,and promotion of well-being ,Good Health and Well Being ,Animals ,Antibodies ,Neutralizing ,Antibodies ,Viral ,Antibody Specificity ,Ebola Vaccines ,Ebolavirus ,Hemorrhagic Fever ,Ebola ,Humans ,Primates ,Biological Sciences ,Medical and Health Sciences - Abstract
Although substantial progress has been made with Ebola virus (EBOV) vaccine measures, the immune correlates of vaccine-mediated protection remain uncertain. Here, five mucosal vaccine vectors based on human and avian paramyxoviruses provided nonhuman primates with varying degrees of protection, despite expressing the same EBOV glycoprotein (GP) immunogen. Each vaccine produced antibody responses that differed in Fc-mediated functions and isotype composition, as well as in magnitude and coverage toward GP and its conformational and linear epitopes. Differences in the degree of protection and comprehensive characterization of the response afforded the opportunity to identify which features and functions were elevated in survivors and could therefore serve as vaccine correlates of protection. Pairwise network correlation analysis of 139 immune- and vaccine-related parameters was performed to demonstrate relationships with survival. Total GP-specific antibodies, as measured by biolayer interferometry, but not neutralizing IgG or IgA titers, correlated with survival. Fc-mediated functions and the amount of receptor binding domain antibodies were associated with improved survival outcomes, alluding to the protective mechanisms of these vaccines. Therefore, functional qualities of the antibody response, particularly Fc-mediated effects and GP specificity, rather than simply magnitude of the response, appear central to vaccine-induced protection against EBOV. The heterogeneity of the response profile between the vaccines indicates that each vaccine likely exhibits its own protective signature and the requirements for an efficacious EBOV vaccine are complex.
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- 2021
94. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST).
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Oikonomou, Evangelos K, Van Dijk, David, Parise, Helen, Suchard, Marc A, de Lemos, James, Antoniades, Charalambos, Velazquez, Eric J, Miller, Edward J, and Khera, Rohan
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Biomedical Imaging ,Clinical Research ,Heart Disease ,Heart Disease - Coronary Heart Disease ,Chronic Pain ,Pain Research ,Patient Safety ,Cardiovascular ,4.2 Evaluation of markers and technologies ,Detection ,screening and diagnosis ,Generic health relevance ,Good Health and Well Being ,Chest Pain ,Computed Tomography Angiography ,Coronary Angiography ,Coronary Artery Disease ,Humans ,Prospective Studies ,Chest pain ,Phenomapping ,Machine learning ,Computed tomography ,Stress testing ,Cardiorespiratory Medicine and Haematology ,Clinical Sciences ,Cardiovascular System & Hematology - Abstract
AimsCoronary artery disease is frequently diagnosed following evaluation of stable chest pain with anatomical or functional testing. A more granular understanding of patient phenotypes that benefit from either strategy may enable personalized testing.Methods and resultsUsing participant-level data from 9572 patients undergoing anatomical (n = 4734) vs. functional (n = 4838) testing in the PROMISE (PROspective Multicenter Imaging Study for Evaluation of Chest Pain) trial, we created a topological representation of the study population based on 57 pre-randomization variables. Within each patient's 5% topological neighbourhood, Cox regression models provided individual patient-centred hazard ratios for major adverse cardiovascular events and revealed marked heterogeneity across the phenomap [median 1.11 (10th to 90th percentile: 0.52-2.61]), suggestive of distinct phenotypic neighbourhoods favouring anatomical or functional testing. Based on this risk phenomap, we employed an extreme gradient boosting algorithm in 80% of the PROMISE population to predict the personalized benefit of anatomical vs. functional testing using 12 model-derived, routinely collected variables and created a decision support tool named ASSIST (Anatomical vs. Stress teSting decIsion Support Tool). In both the remaining 20% of PROMISE and an external validation set consisting of patients from SCOT-HEART (Scottish COmputed Tomography of the HEART Trial) undergoing anatomical-first vs. functional-first assessment, the testing strategy recommended by ASSIST was associated with a significantly lower incidence of each study's primary endpoint (P = 0.0024 and P = 0.0321 for interaction, respectively), as well as a harmonized endpoint of all-cause mortality or non-fatal myocardial infarction (P = 0.0309 and P
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- 2021
95. Scalable Algorithms for Large Competing Risks Data
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Kawaguchi, Eric S, Shen, Jenny I, Suchard, Marc A, and Li, Gang
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Mathematical Sciences ,Statistics ,Bioengineering ,Broken adaptive ridge ,Fine-Gray model ,l(0)-regularization ,Massive sample size ,Model selection/variable selection ,Oracle property ,Subdistribution hazard ,Broken Adaptive Ridge ,Massive Sample Size ,Model Selection/Variable selection ,ℓ0-regularization ,stat.ME ,stat.CO ,Econometrics ,Statistics & Probability - Abstract
This paper develops two orthogonal contributions to scalable sparse regression for competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge method (BAR), a surrogate ℓ 0-based iteratively reweighted ℓ 2-penalization algorithm that achieves sparsity in its limit, in the context of the Fine-Gray (1999) proportional subdistributional hazards (PSH) model. In particular, we derive a new algorithm for BAR regression, named cycBAR, that performs cyclic update of each coordinate using an explicit thresholding formula. The new cycBAR algorithm effectively avoids fitting multiple reweighted ℓ 2-penalizations and thus yields impressive speedups over the original BAR algorithm. Second, we address a pivotal computational issue related to fitting the PSH model. Specifically, the computation costs of the log-pseudo likelihood and its derivatives for PSH model grow at the rate of O(n 2) with the sample size n in current implementations. We propose a novel forward-backward scan algorithm that reduces the computation costs to O(n). The proposed method applies to both unpenalized and penalized estimation for the PSH model and has exhibited drastic speedups over current implementations. Finally, combining the two algorithms can yields > 1, 000 fold speedups over the original BAR algorithm. Illustrations of the impressive scalability of our proposed algorithm for large competing risks data are given using both simulations and a United States Renal Data System data. Supplementary materials for this article are available online.
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- 2021
96. Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: a multinational network cohort study
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Lane, Jennifer CE, Weaver, James, Kostka, Kristin, Duarte-Salles, Talita, Abrahao, Maria Tereza F, Alghoul, Heba, Alser, Osaid, Alshammari, Thamir M, Areia, Carlos, Biedermann, Patricia, Banda, Juan M, Burn, Edward, Casajust, Paula, Fister, Kristina, Hardin, Jill, Hester, Laura, Hripcsak, George, Kaas-Hansen, Benjamin Skov, Khosla, Sajan, Kolovos, Spyros, Lynch, Kristine E, Makadia, Rupa, Mehta, Paras P, Morales, Daniel R, Morgan-Stewart, Henry, Mosseveld, Mees, Newby, Danielle, Nyberg, Fredrik, Ostropolets, Anna, Park, Rae Woong, Prats-Uribe, Albert, Rao, Gowtham A, Reich, Christian, Rijnbeek, Peter, Sena, Anthony G, Shoaibi, Azza, Spotnitz, Matthew, Subbian, Vignesh, Suchard, Marc A, Vizcaya, David, Wen, Haini, de Wilde, Marcel, Xie, Junqing, You, Seng Chan, Zhang, Lin, Lovestone, Simon, Ryan, Patrick, Prieto-Alhambra, Daniel, and consortium, for the OHDSI-COVID-19
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Brain Disorders ,Autoimmune Disease ,Serious Mental Illness ,Mental Health ,Suicide ,Depression ,Behavioral and Social Science ,Arthritis ,Clinical Research ,Prevention ,Suicide Prevention ,Mental health ,Good Health and Well Being ,Adolescent ,Adult ,Aged ,Antirheumatic Agents ,Arthritis ,Rheumatoid ,Cohort Studies ,Female ,Germany ,Humans ,Hydroxychloroquine ,Male ,Middle Aged ,Psychoses ,Substance-Induced ,Risk Assessment ,Suicidal Ideation ,United Kingdom ,United States ,Young Adult ,COVID-19 Drug Treatment ,HCQ ,safety ,epidemiology ,RA ,psychosis ,depression ,OHDSI-COVID-19 consortium ,epidemiology ,RA ,Clinical Sciences ,Immunology ,Public Health and Health Services ,Arthritis & Rheumatology - Abstract
ObjectivesConcern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.MethodsWe performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2
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- 2021
97. Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data
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Holbrook, Andrew J., Loeffler, Charles E., Flaxman, Seth R., and Suchard, Marc A.
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Statistics - Applications - Abstract
The Hawkes process and its extensions effectively model self-excitatory phenomena including earthquakes, viral pandemics, financial transactions, neural spike trains and the spread of memes through social networks. The usefulness of these stochastic process models within a host of economic sectors and scientific disciplines is undercut by the processes' computational burden: complexity of likelihood evaluations grows quadratically in the number of observations for both the temporal and spatiotemporal Hawkes processes. We show that, with care, one may parallelize these calculations using both central and graphics processing unit implementations to achieve over 100-fold speedups over single-core processing. Using a simple adaptive Metropolis-Hastings scheme, we apply our high-performance computing framework to a Bayesian analysis of big gunshot data generated in Washington D.C. between the years of 2006 and 2019, thereby extending a past analysis of the same data from under 10,000 to over 85,000 observations. To encourage wide-spread use, we provide hpHawkes, an open-source R package, and discuss high-level implementation and program design for leveraging aspects of computational hardware that become necessary in a big data setting., Comment: Submitted to Statistics and Computing
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- 2020
98. Efficient Bayesian Inference of General Gaussian Models on Large Phylogenetic Trees
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Bastide, Paul, Ho, Lam Si Tung, Baele, Guy, Lemey, Philippe, and Suchard, Marc A
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Statistics - Applications ,Quantitative Biology - Populations and Evolution - Abstract
Phylogenetic comparative methods correct for shared evolutionary history among a set of non-independent organisms by modeling sample traits as arising from a diffusion process along on the branches of a possibly unknown history. To incorporate such uncertainty, we present a scalable Bayesian inference framework under a general Gaussian trait evolution model that exploits Hamiltonian Monte Carlo (HMC). HMC enables efficient sampling of the constrained model parameters and takes advantage of the tree structure for fast likelihood and gradient computations, yielding algorithmic complexity linear in the number of observations. This approach encompasses a wide family of stochastic processes, including the general Ornstein-Uhlenbeck (OU) process, with possible missing data and measurement errors. We implement inference tools for a biologically relevant subset of all these models into the BEAST phylogenetic software package and develop model comparison through marginal likelihood estimation. We apply our approach to study the morphological evolution in the superfamilly of Musteloidea (including weasels and allies) as well as the heritability of HIV virulence. This second problem furnishes a new measure of evolutionary heritability that demonstrates its utility through a targeted simulation study.
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- 2020
99. Online Bayesian phylodynamic inference in BEAST with application to epidemic reconstruction
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Gill, Mandev S., Lemey, Philippe, Suchard, Marc A., Rambaut, Andrew, and Baele, Guy
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Quantitative Biology - Populations and Evolution ,Statistics - Methodology - Abstract
Reconstructing pathogen dynamics from genetic data as they become available during an outbreak or epidemic represents an important statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an 'online' fashion. Widely-used Bayesian phylogenetic inference packages are not set up for this purpose, generally requiring one to recompute trees and evolutionary model parameters de novo when new data arrive. To accommodate increasing data flow in a Bayesian phylogenetic framework, we introduce a methodology to efficiently update the posterior distribution with newly available genetic data. Our procedure is implemented in the BEAST 1.10 software package, and relies on a distance-based measure to insert new taxa into the current estimate of the phylogeny and imputes plausible values for new model parameters to accommodate growing dimensionality. This augmentation creates informed starting values and re-uses optimally tuned transition kernels for posterior exploration of growing data sets, reducing the time necessary to converge to target posterior distributions. We apply our framework to data from the recent West African Ebola virus epidemic and demonstrate a considerable reduction in time required to obtain posterior estimates at different time points of the outbreak. Beyond epidemic monitoring, this framework easily finds other applications within the phylogenetics community, where changes in the data -- in terms of alignment changes, sequence addition or removal -- present common scenarios that can benefit from online inference., Comment: 20 pages, 3 figures
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- 2020
100. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil
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Faria, Nuno R, Mellan, Thomas A, Whittaker, Charles, Claro, Ingra M, Candido, Darlan da S, Mishra, Swapnil, Crispim, Myuki AE, Sales, Flavia CS, Hawryluk, Iwona, McCrone, John T, Hulswit, Ruben JG, Franco, Lucas AM, Ramundo, Mariana S, de Jesus, Jaqueline G, Andrade, Pamela S, Coletti, Thais M, Ferreira, Giulia M, Silva, Camila AM, Manuli, Erika R, Pereira, Rafael HM, Peixoto, Pedro S, Kraemer, Moritz UG, Gaburo, Nelson, Camilo, Cecilia da C, Hoeltgebaum, Henrique, Souza, William M, Rocha, Esmenia C, de Souza, Leandro M, de Pinho, Mariana C, Araujo, Leonardo JT, Malta, Frederico SV, de Lima, Aline B, Silva, Joice do P, Zauli, Danielle AG, Ferreira, Alessandro C de S, Schnekenberg, Ricardo P, Laydon, Daniel J, Walker, Patrick GT, Schlüter, Hannah M, Dos Santos, Ana LP, Vidal, Maria S, Del Caro, Valentina S, Filho, Rosinaldo MF, Dos Santos, Helem M, Aguiar, Renato S, Proença-Modena, José L, Nelson, Bruce, Hay, James A, Monod, Mélodie, Miscouridou, Xenia, Coupland, Helen, Sonabend, Raphael, Vollmer, Michaela, Gandy, Axel, Prete, Carlos A, Nascimento, Vitor H, Suchard, Marc A, Bowden, Thomas A, Pond, Sergei LK, Wu, Chieh-Hsi, Ratmann, Oliver, Ferguson, Neil M, Dye, Christopher, Loman, Nick J, Lemey, Philippe, Rambaut, Andrew, Fraiji, Nelson A, Carvalho, Maria do PSS, Pybus, Oliver G, Flaxman, Seth, Bhatt, Samir, and Sabino, Ester C
- Subjects
Human Genome ,Immunization ,Pneumonia & Influenza ,Lung ,Prevention ,Genetics ,Vaccine Related ,Biotechnology ,Pneumonia ,Emerging Infectious Diseases ,Biodefense ,Infectious Diseases ,Infection ,Good Health and Well Being ,Angiotensin-Converting Enzyme 2 ,Brazil ,COVID-19 ,Communicable Diseases ,Emerging ,Epidemiological Monitoring ,Genome ,Viral ,Genomics ,Humans ,Models ,Theoretical ,Molecular Epidemiology ,Mutation ,Protein Binding ,SARS-CoV-2 ,Spike Glycoprotein ,Coronavirus ,Viral Load ,General Science & Technology - Abstract
Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.
- Published
- 2021
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