Publications by authors named "Mattia Prosperi"

145 Publications

Fast and exact quantification of motif occurrences in biological sequences.

BMC Bioinformatics 2021 Sep 18;22(1):445. Epub 2021 Sep 18.

Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.

Background: Identification of motifs and quantification of their occurrences are important for the study of genetic diseases, gene evolution, transcription sites, and other biological mechanisms. Exact formulae for estimating count distributions of motifs under Markovian assumptions have high computational complexity and are impractical to be used on large motif sets. Approximated formulae, e.g. based on compound Poisson, are faster, but reliable p value calculation remains challenging. Here, we introduce 'motif_prob', a fast implementation of an exact formula for motif count distribution through progressive approximation with arbitrary precision. Our implementation speeds up the exact calculation, usually impractical, making it feasible and posit to substitute currently employed heuristics.

Results: We implement motif_prob in both Perl and C+ + languages, using an efficient error-bound iterative process for the exact formula, providing comparison with state-of-the-art tools (e.g. MoSDi) in terms of precision, run time benchmarks, along with a real-world use case on bacterial motif characterization. Our software is able to process a million of motifs (13-31 bases) over genome lengths of 5 million bases within the minute on a regular laptop, and the run times for both the Perl and C+ + code are several orders of magnitude smaller (50-1000× faster) than MoSDi, even when using their fast compound Poisson approximation (60-120× faster). In the real-world use cases, we first show the consistency of motif_prob with MoSDi, and then how the p-value quantification is crucial for enrichment quantification when bacteria have different GC content, using motifs found in antimicrobial resistance genes. The software and the code sources are available under the MIT license at https://github.com/DataIntellSystLab/motif_prob .

Conclusions: The motif_prob software is a multi-platform and efficient open source solution for calculating exact frequency distributions of motifs. It can be integrated with motif discovery/characterization tools for quantifying enrichment and deviation from expected frequency ranges with exact p values, without loss in data processing efficiency.
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http://dx.doi.org/10.1186/s12859-021-04355-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8449872PMC
September 2021

KARGA: Multi-platform Toolkit for -mer-based Antibiotic Resistance Gene Analysis of High-throughput Sequencing Data.

IEEE EMBS Int Conf Biomed Health Inform 2021 Jul 10;2021. Epub 2021 Aug 10.

Data Intelligence Systems Lab, Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.

High-throughput sequencing is widely used for strain detection and characterization of antibiotic resistance in microbial metagenomic samples. Current analytical tools use curated antibiotic resistance gene (ARG) databases to classify individual sequencing reads or assembled contigs. However, identifying ARGs from raw read data can be time consuming (especially if assembly or alignment is required) and challenging, due to genome rearrangements and mutations. Here, we present the -mer-based antibiotic gene resistance analyzer (KARGA), a multi-platform Java toolkit for identifying ARGs from metagenomic short read data. KARGA does not perform alignment; it uses an efficient double-lookup strategy, statistical filtering on false positives, and provides individual read classification as well as covering of the database resistome. On simulated data, KARGA's antibiotic resistance class recall is 99.89% for error/mutation rates within 10%, and of 83.37% for error/mutation rates between 10% and 25%, while it is 99.92% on ARGs with rearrangements. On empirical data, KARGA provides higher hit score (≥1.5-fold) than AMRPlusPlus, DeepARG, and MetaMARC. KARGA has also faster runtimes than all other tools (2x faster than AMRPlusPlus, 7x than DeepARG, and over 100x than MetaMARC). KARGA is available under the MIT license at https://github.com/DataIntellSystLab/KARGA.
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http://dx.doi.org/10.1109/bhi50953.2021.9508479DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8383893PMC
July 2021

Propensity score synthetic augmentation matching using generative adversarial networks (PSSAM-GAN).

Comput Methods Programs Biomed Update 2021 16;1. Epub 2021 Jul 16.

Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Florida 32610, USA.

Understanding causality is of crucial importance in biomedical sciences, where developing prediction models is insufficient because the models need to be actionable. However, data sources, such as electronic health records, are observational and often plagued with various types of biases, e.g. confounding. Although randomized controlled trials are the gold standard to estimate the causal effects of treatment interventions on health outcomes, they are not always possible. Propensity score matching (PSM) is a popular statistical technique for observational data that aims at balancing the characteristics of the population assigned either to a treatment or to a control group, making treatment assignment and outcome independent upon these characteristics. However, matching subjects can reduce the sample size. Inverse probability weighting (IPW) maintains the sample size, but extreme values can lead to instability. While PSM and IPW have been historically used in conjunction with linear regression, machine learning methods -including deep learning with propensity dropout- have been proposed to account for nonlinear treatment assignments. In this work, we propose a novel deep learning approach -the Propensity Score Synthetic Augmentation Matching using Generative Adversarial Networks (PSSAM-GAN)- that aims at keeping the sample size, without IPW, by generating synthetic matches. PSSAM-GAN can be used in conjunction with any other prediction method to estimate treatment effects. Experiments performed on both semi-synthetic (perinatal interventions) and real-world observational data (antibiotic treatments, and job interventions) show that the PSSAM-GAN approach effectively creates balanced datasets, relaxing the weighting/dropout needs for downstream methods, and providing competitive performance in effects estimation as compared to simple GAN and in conjunction with other deep counterfactual learning architectures, e.g. TARNet.
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http://dx.doi.org/10.1016/j.cmpbup.2021.100020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357304PMC
July 2021

Challenges in replicating secondary analysis of electronic health records data with multiple computable phenotypes: A case study on methicillin-resistant Staphylococcus aureus bacteremia infections.

Int J Med Inform 2021 09 16;153:104531. Epub 2021 Jul 16.

Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA. Electronic address:

Background: Replication of prediction modeling using electronic health records (EHR) is challenging because of the necessity to compute phenotypes including study cohort, outcomes, and covariates. However, some phenotypes may not be easily replicated across EHR data sources due to a variety of reasons such as the lack of gold standard definitions and documentation variations across systems, which may lead to measurement error and potential bias. Methicillin-resistant Staphylococcus aureus (MRSA) infections are responsible for high mortality worldwide. With limited treatment options for the infection, the ability to predict MRSA outcome is of interest. However, replicating these MRSA outcome prediction models using EHR data is problematic due to the lack of well-defined computable phenotypes for many of the predictors as well as study inclusion and outcome criteria.

Objective: In this study, we aimed to evaluate a prediction model for 30-day mortality after MRSA bacteremia infection diagnosis with reduced vancomycin susceptibility (MRSA-RVS) considering multiple computable phenotypes using EHR data.

Methods: We used EHR data from a large academic health center in the United States to replicate the original study conducted in Taiwan. We derived multiple computable phenotypes of risk factors and predictors used in the original study, reported stratified descriptive statistics, and assessed the performance of the prediction model.

Results: In our replication study, it was possible to (re)compute most of the original variables. Nevertheless, for certain variables, their computable phenotypes can only be approximated by proxy with structured EHR data items, especially the composite clinical indices such as the Pitt bacteremia score. Even computable phenotype for the outcome variable was subject to variation on the basis of the admission/discharge windows. The replicated prediction model exhibited only a mild discriminatory ability.

Conclusion: Despite the rich information in EHR data, replication of prediction models involving complex predictors is still challenging, often due to the limited availability of validated computable phenotypes. On the other hand, it is often possible to derive proxy computable phenotypes that can be further validated and calibrated.
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http://dx.doi.org/10.1016/j.ijmedinf.2021.104531DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8451470PMC
September 2021

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.

J Am Med Inform Assoc 2021 08;28(9):2050-2067

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

Objective: To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

Materials And Methods: We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening.

Results: In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications.

Discussion: Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias.

Conclusion: There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.
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http://dx.doi.org/10.1093/jamia/ocab098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8344463PMC
August 2021

Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer's disease.

NPJ Digit Med 2021 May 14;4(1):84. Epub 2021 May 14.

Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA.

In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimer's disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trial's study protocol to identify the study population, treatment regimen assignments and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care (SOC) arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher serious adverse event (SAE) rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23 mg arm than in the 10 mg SOC arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials' design. Nevertheless, such an approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.
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http://dx.doi.org/10.1038/s41746-021-00452-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121837PMC
May 2021

Semantic standards of external exposome data.

Environ Res 2021 06 24;197:111185. Epub 2021 Apr 24.

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA; Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA. Electronic address:

An individual's health and conditions are associated with a complex interplay between the individual's genetics and his or her exposures to both internal and external environments. Much attention has been placed on characterizing of the genome in the past; nevertheless, genetics only account for about 10% of an individual's health conditions, while the remaining appears to be determined by environmental factors and gene-environment interactions. To comprehensively understand the causes of diseases and prevent them, environmental exposures, especially the external exposome, need to be systematically explored. However, the heterogeneity of the external exposome data sources (e.g., same exposure variables using different nomenclature in different data sources, or vice versa, two variables have the same or similar name but measure different exposures in reality) increases the difficulty of analyzing and understanding the associations between environmental exposures and health outcomes. To solve the issue, the development of semantic standards using an ontology-driven approach is inevitable because ontologies can (1) provide a unambiguous and consistent understanding of the variables in heterogeneous data sources, and (2) explicitly express and model the context of the variables and relationships between those variables. We conducted a review of existing ontology for the external exposome and found only four relevant ontologies. Further, the four existing ontologies are limited: they (1) often ignored the spatiotemporal characteristics of external exposome data, and (2) were developed in isolation from other conceptual frameworks (e.g., the socioecological model and the social determinants of health). Moving forward, the combination of multi-domain and multi-scale data (i.e., genome, phenome and exposome at different granularity) and different conceptual frameworks is the basis of health outcomes research in the future.
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http://dx.doi.org/10.1016/j.envres.2021.111185DOI Listing
June 2021

Deep propensity network using a sparse autoencoder for estimation of treatment effects.

J Am Med Inform Assoc 2021 06;28(6):1197-1206

Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA.

Objective: Drawing causal estimates from observational data is problematic, because datasets often contain underlying bias (eg, discrimination in treatment assignment). To examine causal effects, it is important to evaluate what-if scenarios-the so-called "counterfactuals." We propose a novel deep learning architecture for propensity score matching and counterfactual prediction-the deep propensity network using a sparse autoencoder (DPN-SA)-to tackle the problems of high dimensionality, nonlinear/nonparallel treatment assignment, and residual confounding when estimating treatment effects.

Materials And Methods: We used 2 randomized prospective datasets, a semisynthetic one with nonlinear/nonparallel treatment selection bias and simulated counterfactual outcomes from the Infant Health and Development Program and a real-world dataset from the LaLonde's employment training program. We compared different configurations of the DPN-SA against logistic regression and LASSO as well as deep counterfactual networks with propensity dropout (DCN-PD). Models' performances were assessed in terms of average treatment effects, mean squared error in precision on effect's heterogeneity, and average treatment effect on the treated, over multiple training/test runs.

Results: The DPN-SA outperformed logistic regression and LASSO by 36%-63%, and DCN-PD by 6%-10% across all datasets. All deep learning architectures yielded average treatment effects close to the true ones with low variance. Results were also robust to noise-injection and addition of correlated variables. Code is publicly available at https://github.com/Shantanu48114860/DPN-SAz.

Discussion And Conclusion: Deep sparse autoencoders are particularly suited for treatment effect estimation studies using electronic health records because they can handle high-dimensional covariate sets, large sample sizes, and complex heterogeneity in treatment assignments.
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http://dx.doi.org/10.1093/jamia/ocaa346DOI Listing
June 2021

Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation.

Front Genet 2021 22;12:564186. Epub 2021 Jan 22.

Department of Epidemiology, University of Florida, Gainesville, FL, United States.

Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approach, several AMR databases and gene identification algorithms have been recently developed. A key problem in AMR detection, however, is the need for computational approaches detecting potential novel AMR genes or variants, which are not included in the reference databases. Toward this direction, here we study the relation between AMR and relative solvent accessibility (RSA) of protein variants from an perspective. We show how known AMR protein variants tend to correspond to exposed residues, while on the contrary their susceptible counterparts tend to be buried. Based on these findings, we develop RSA-AMR, a novel relative solvent accessibility-based AMR scoring system. This scoring system can be applied to any protein variant to estimate its propensity of altering the relative solvent accessibility, and potentially conferring (or hindering) AMR. We show how RSA-AMR score can be integrated with existing AMR detection algorithms to expand their range of applicability into detecting potential novel AMR variants, and provide a ten-fold increase in Specificity. The two main limitations of RSA-AMR score is that it is designed on single point changes, and a limited number of variants was available for model learning.
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http://dx.doi.org/10.3389/fgene.2021.564186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862766PMC
January 2021

Bagged random causal networks for interventional queries on observational biomedical datasets.

J Biomed Inform 2021 03 4;115:103689. Epub 2021 Feb 4.

Cancer Informatics Shared Resource, University of Florida Health Cancer Center, FL, USA; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, FL, USA. Electronic address:

Learning causal effects from observational data, e.g. estimating the effect of a treatment on survival by data-mining electronic health records (EHRs), can be biased due to unmeasured confounders, mediators, and colliders. When the causal dependencies among features/covariates are expressed in the form of a directed acyclic graph, using do-calculus it is possible to identify one or more adjustment sets for eliminating the bias on a given causal query under certain assumptions. However, prior knowledge of the causal structure might be only partial; algorithms for causal structure discovery often provide ambiguous solutions, and their computational complexity becomes practically intractable when the feature sets grow large. We hypothesize that the estimation of the true causal effect of a causal query on to an outcome can be approximated as an ensemble of lower complexity estimators, namely bagged random causal networks. A bagged random causal network is an ensemble of subnetworks constructed by sampling the feature subspaces (with the query, the outcome, and a random number of other features), drawing conditional dependencies among the features, and inferring the corresponding adjustment sets. The causal effect can be then estimated by any regression function of the outcome by the query paired with the adjustment sets. Through simulations and a real-world clinical dataset (class III malocclusion data), we show that the bagged estimator is -in most cases- consistent with the true causal effect if the structure is known, has a good variance/bias trade-off when the structure is unknown (estimated using heuristics), has lower computational complexity than learning a full network, and outperforms boosted regression. In conclusion, the bagged random causal network is well-suited to estimate query-target causal effects from observational studies on EHR and other high-dimensional biomedical databases.
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http://dx.doi.org/10.1016/j.jbi.2021.103689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085906PMC
March 2021

Brain tissue transcriptomic analysis of SIV-infected macaques identifies several altered metabolic pathways linked to neuropathogenesis and poly (ADP-ribose) polymerases (PARPs) as potential therapeutic targets.

J Neurovirol 2021 02 6;27(1):101-115. Epub 2021 Jan 6.

Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, USA.

Despite improvements in antiretroviral therapy, human immunodeficiency virus type 1 (HIV-1)-associated neurocognitive disorders (HAND) remain prevalent in subjects undergoing therapy. HAND significantly affects individuals' quality of life, as well as adherence to therapy, and, despite the increasing understanding of neuropathogenesis, no definitive diagnostic or prognostic marker has been identified. We investigated transcriptomic profiles in frontal cortex tissues of Simian immunodeficiency virus (SIV)-infected Rhesus macaques sacrificed at different stages of infection. Gene expression was compared among SIV-infected animals (n = 11), with or without CD8+ lymphocyte depletion, based on detectable (n = 6) or non-detectable (n = 5) presence of the virus in frontal cortex tissues. Significant enrichment in activation of monocyte and macrophage cellular pathways was found in animals with detectable brain infection, independently from CD8+ lymphocyte depletion. In addition, transcripts of four poly (ADP-ribose) polymerases (PARPs) were up-regulated in the frontal cortex, which was confirmed by real-time polymerase chain reaction. Our results shed light on involvement of PARPs in SIV infection of the brain and their role in SIV-associated neurodegenerative processes. Inhibition of PARPs may provide an effective novel therapeutic target for HIV-related neuropathology.
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http://dx.doi.org/10.1007/s13365-020-00927-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7786889PMC
February 2021

Prevalence and hospital charges from firearm injuries treated in US emergency departments from 2006 to 2016.

Surgery 2021 05 29;169(5):1188-1198. Epub 2020 Dec 29.

Dean's Office, Boston University School of Public Health, MA.

Background: Age- and intent-related differences in the burden and costs of firearm injury treated in emergency departments are not well-documented.

Methods: We performed a serial cross-sectional study of the Healthcare Cost and Utilization Program Nationwide Emergency Department Survey from 2006 to 2016. We used International Classification of Diseases diagnoses codes revisions 9 and 10 to identify firearm injuries. We calculated survey-weighted counts, proportions, means, and rates and confidence intervals of national, age-specific (0-4, 5-9, 10-14, 15-17, 18-44, 45-64, 65-84, >84) and intent-specific (assault, unintentional, suicide, undetermined) emergency department discharges for firearm injuries. We used survey-weighted regression to assess temporal trends.

Results: There was a total of 868,483 (25.5 per 100,000) emergency department visits for firearm injuries from 2006 to 2016, and 7.8% died in the emergency department. Overall, firearm injury rates remained steady (P = .78). The largest burden was among those 25 to 44 years of age, but their rates remained stable (10.8 per 100,000). Overall assault injuries declined from 39.7% to 36.4%, and overall unintentional injuries increased from 46.4% to 54.7%. Legal-intervention injuries declined from 0.6 to 0.3 per 100,000. The charges (total $4,059,070,364, $369,006,396/year) increased across time in age and intent groups. The mean predicted charges increased from $1,922 to $3,348 in those alive versus $3,741 to $6,515 among those who died.

Conclusion: Interventions and programs to manage the consequences of firearm injury in persons who live with ongoing morbidity and economic burden are warranted.
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http://dx.doi.org/10.1016/j.surg.2020.11.009DOI Listing
May 2021

An ontology-based documentation of data discovery and integration process in cancer outcomes research.

BMC Med Inform Decis Mak 2020 12 14;20(Suppl 4):292. Epub 2020 Dec 14.

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 2197 Mowry Road, Suite 122, PO Box 100177, Gainesville, FL, 32610-0177, USA.

Background: To reduce cancer mortality and improve cancer outcomes, it is critical to understand the various cancer risk factors (RFs) across different domains (e.g., genetic, environmental, and behavioral risk factors) and levels (e.g., individual, interpersonal, and community levels). However, prior research on RFs of cancer outcomes, has primarily focused on individual level RFs due to the lack of integrated datasets that contain multi-level, multi-domain RFs. Further, the lack of a consensus and proper guidance on systematically identify RFs also increase the difficulty of RF selection from heterogenous data sources in a multi-level integrative data analysis (mIDA) study. More importantly, as mIDA studies require integrating heterogenous data sources, the data integration processes in the limited number of existing mIDA studies are inconsistently performed and poorly documented, and thus threatening transparency and reproducibility.

Methods: Informed by the National Institute on Minority Health and Health Disparities (NIMHD) research framework, we (1) reviewed existing reporting guidelines from the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network and (2) developed a theory-driven reporting guideline to guide the RF variable selection, data source selection, and data integration process. Then, we developed an ontology to standardize the documentation of the RF selection and data integration process in mIDA studies.

Results: We summarized the review results and created a reporting guideline-ATTEST-for reporting the variable selection and data source selection and integration process. We provided an ATTEST check list to help researchers to annotate and clearly document each step of their mIDA studies to ensure the transparency and reproducibility. We used the ATTEST to report two mIDA case studies and further transformed annotation results into sematic triples, so that the relationships among variables, data sources and integration processes are explicitly standardized and modeled using the classes and properties from OD-ATTEST.

Conclusion: Our ontology-based reporting guideline solves some key challenges in current mIDA studies for cancer outcomes research, through providing (1) a theory-driven guidance for multi-level and multi-domain RF variable and data source selection; and (2) a standardized documentation of the data selection and integration processes powered by an ontology, thus a way to enable sharing of mIDA study reports among researchers.
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http://dx.doi.org/10.1186/s12911-020-01270-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734720PMC
December 2020

Authors' Reply to: Errors in Tracing Coronavirus SARS-CoV-2 Transmission Using a Maximum Likelihood Tree. Comment on "A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis".

JMIR Public Health Surveill 2020 11 11;6(4):e24661. Epub 2020 Nov 11.

Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States.

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http://dx.doi.org/10.2196/24661DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688377PMC
November 2020

Differing impacts of global and regional responses on SARS-CoV-2 transmission cluster dynamics.

bioRxiv 2020 Nov 6. Epub 2020 Nov 6.

Although the global response to COVID-19 has not been entirely unified, the opportunity arises to assess the impact of regional public health interventions and to classify strategies according to their outcome. Analysis of genetic sequence data gathered over the course of the pandemic allows us to link the dynamics associated with networks of connected individuals with specific interventions. In this study, clusters of transmission were inferred from a phylogenetic tree representing the relationships of patient sequences sampled from December 30, 2019 to April 17, 2020. Metadata comprising sampling time and location were used to define the global behavior of transmission over this earlier sampling period, but also the involvement of individual regions in transmission cluster dynamics. Results demonstrate a positive impact of international travel restrictions and nationwide lockdowns on global cluster dynamics. However, residual, localized clusters displayed a wide range of estimated initial secondary infection rates, for which uniform public health interventions are unlikely to have sustainable effects. Our findings highlight the presence of so-called "super-spreaders", with the propensity to infect a larger-than-average number of people, in countries, such as the USA, for which additional mitigation efforts targeting events surrounding this type of spread are urgently needed to curb further dissemination of SARS-CoV-2.
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http://dx.doi.org/10.1101/2020.11.06.370999DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654859PMC
November 2020

Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data.

J Am Med Inform Assoc 2020 12;27(12):1999-2010

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

Objective: To synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ assessment in the national Patient-centered Clinical Research Network (PCORnet).

Materials And Methods: We started with 3 widely cited DQ literature-2 reviews from Chan et al (2010) and Weiskopf et al (2013a) and 1 DQ framework from Kahn et al (2016)-and expanded our review systematically to cover relevant articles published up to February 2020. We extracted DQ dimensions and assessment methods from these studies, mapped their relationships, and organized a synthesized summarization of existing DQ dimensions and assessment methods. We reviewed the data checks employed by the PCORnet and mapped them to the synthesized DQ dimensions and methods.

Results: We analyzed a total of 3 reviews, 20 DQ frameworks, and 226 DQ studies and extracted 14 DQ dimensions and 10 assessment methods. We found that completeness, concordance, and correctness/accuracy were commonly assessed. Element presence, validity check, and conformance were commonly used DQ assessment methods and were the main focuses of the PCORnet data checks.

Discussion: Definitions of DQ dimensions and methods were not consistent in the literature, and the DQ assessment practice was not evenly distributed (eg, usability and ease-of-use were rarely discussed). Challenges in DQ assessments, given the complex and heterogeneous nature of real-world data, exist.

Conclusion: The practice of DQ assessment is still limited in scope. Future work is warranted to generate understandable, executable, and reusable DQ measures.
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http://dx.doi.org/10.1093/jamia/ocaa245DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7727392PMC
December 2020

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach.

Int J Med Inform 2020 11 15;143:104272. Epub 2020 Sep 15.

Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, United States.

Background: Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don't necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data.

Objective: The purpose of this study was to use tree-based machine learning methods to determine the most important predictors of inpatient falls, while also validating each via cross-validation.

Materials And Methods: A case-control study was designed using EHR and electronic administrative data collected between January 1, 2013 to October 31, 2013 in 14 medical surgical units. The data contained 38 predictor variables which comprised of patient characteristics, admission information, assessment information, clinical data, and organizational characteristics. Classification tree, bagging, random forest, and adaptive boosting methods were used to identify the most important factors of inpatient fall-risk through variable importance measures. Sensitivity, specificity, and area under the ROC curve were computed via ten-fold cross validation and compared via pairwise t-tests. These methods were also compared to a univariate logistic regression of the Morse Fall Scale total score.

Results: In terms of AUROC, bagging (0.89), random forest (0.90), and boosting (0.89) all outperformed the Morse Fall Scale (0.86) and the classification tree (0.85), but no differences were measured between bagging, random forest, and adaptive boosting, at a p-value of 0.05. History of Falls, Age, Morse Fall Scale total score, quality of gait, unit type, mental status, and number of high fall risk increasing drugs (FRIDs) were considered the most important features for predicting inpatient fall risk.

Conclusions: Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.
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http://dx.doi.org/10.1016/j.ijmedinf.2020.104272DOI Listing
November 2020

Employing Molecular Phylodynamic Methods to Identify and Forecast HIV Transmission Clusters in Public Health Settings: A Qualitative Study.

Viruses 2020 08 22;12(9). Epub 2020 Aug 22.

Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL 32610, USA.

Molecular HIV surveillance is a promising public health strategy for curbing the HIV epidemic. Clustering technologies used by health departments to date are limited in their ability to infer/forecast cluster growth trajectories. Resolution of the spatiotemporal dynamics of clusters, through phylodynamic and phylogeographic modelling, is one potential strategy to develop a forecasting tool; however, the projected utility of this approach needs assessment. Prior to incorporating novel phylodynamic-based molecular surveillance tools, we sought to identify possible issues related to their feasibility, acceptability, interpretation, and utility. Qualitative data were collected via focus groups among field experts ( = 17, 52.9% female) using semi-structured, open-ended questions. Data were coded using an iterative process, first through the development of provisional themes and subthemes, followed by independent line-by-line coding by two coders. Most participants routinely used molecular methods for HIV surveillance. All agreed that linking molecular sequences to epidemiological data is important for improving HIV surveillance. We found that, in addition to methodological challenges, a variety of implementation barriers are expected in relation to the uptake of phylodynamic methods for HIV surveillance. The participants identified several opportunities to enhance current methods, as well as increase the usability and utility of promising works-in-progress.
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http://dx.doi.org/10.3390/v12090921DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7551766PMC
August 2020

Correction: A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis.

JMIR Public Health Surveill 2020 08 10;6(3):e22853. Epub 2020 Aug 10.

Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States.

[This corrects the article DOI: 10.2196/19170.].
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http://dx.doi.org/10.2196/22853DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7445602PMC
August 2020

Precision Health Analytics With Predictive Analytics and Implementation Research: JACC State-of-the-Art Review.

J Am Coll Cardiol 2020 07;76(3):306-320

Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland. Electronic address:

Emerging data science techniques of predictive analytics expand the quality and quantity of complex data relevant to human health and provide opportunities for understanding and control of conditions such as heart, lung, blood, and sleep disorders. To realize these opportunities, the information sources, the data science tools that use the information, and the application of resulting analytics to health and health care issues will require implementation research methods to define benefits, harms, reach, and sustainability; and to understand related resource utilization implications to inform policymakers. This JACC State-of-the-Art Review is based on a workshop convened by the National Heart, Lung, and Blood Institute to explore predictive analytics in the context of implementation science. It highlights precision medicine and precision public health as complementary and compelling applications of predictive analytics, and addresses future research and training endeavors that might further foster the application of predictive analytics in clinical medicine and public health.
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http://dx.doi.org/10.1016/j.jacc.2020.05.043DOI Listing
July 2020

Sociodemographic, Ecological, and Spatiotemporal Factors Associated with Human Immunodeficiency Virus Drug Resistance in Florida: A Retrospective Analysis.

J Infect Dis 2021 Mar;223(5):866-875

Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA.

Background: Persons living with human immunodeficiency virus (HIV) with resistance to antiretroviral therapy are vulnerable to adverse HIV-related health outcomes and can contribute to transmission of HIV drug resistance (HIVDR) when nonvirally suppressed. The degree to which HIVDR contributes to disease burden in Florida-the US state with the highest HIV incidence- is unknown.

Methods: We explored sociodemographic, ecological, and spatiotemporal associations of HIVDR. HIV-1 sequences (n = 34 447) collected during 2012-2017 were obtained from the Florida Department of Health. HIVDR was categorized by resistance class, including resistance to nucleoside reverse-transcriptase , nonnucleoside reverse-transcriptase , protease , and integrase inhibitors. Multidrug resistance and transmitted drug resistance were also evaluated. Multivariable fixed-effects logistic regression models were fitted to associate individual- and county-level sociodemographic and ecological health indicators with HIVDR.

Results: The HIVDR prevalence was 19.2% (nucleoside reverse-transcriptase inhibitor resistance), 29.7% (nonnucleoside reverse-transcriptase inhibitor resistance), 6.6% (protease inhibitor resistance), 23.5% (transmitted drug resistance), 13.2% (multidrug resistance), and 8.2% (integrase strand transfer inhibitor resistance), with significant variation by Florida county. Individuals who were older, black, or acquired HIV through mother-to-child transmission had significantly higher odds of HIVDR. HIVDR was linked to counties with lower socioeconomic status, higher rates of unemployment, and poor mental health.

Conclusions: Our findings indicate that HIVDR prevalence is higher in Florida than aggregate North American estimates with significant geographic and socioecological heterogeneity.
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http://dx.doi.org/10.1093/infdis/jiaa413DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7938178PMC
March 2021

Mining Twitter to Assess the Determinants of Health Behavior towards Palliative Care in the United States.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:730-739. Epub 2020 May 30.

University of Florida, Gainesville, Florida, USA.

Palliative care is a specialized service with proven efficacy in improving patients' quality-of-life. Nevertheless, lack of awareness and misunderstanding limits its adoption. Research is urgently needed to understand the determinants (e.g., knowledge) related to its adoption. Traditionally, these determinants are measured with questionnaires. In this study, we explored Twitter to reveal these determinants guided by the Integrated Behavioral Model. A secondary goal is to assess the feasibility of extracting user demographics from Twitter data-a significant shortcoming in existing studies that limits our ability to explore more fine-grained research questions (e.g., gender difference). Thus, we collected, preprocessed, and geocoded palliative care-related tweets from 2013 to 2019 and then built classifiers to: 1) categorize tweets into promotional vs. consumer discussions, and 2) extract user gender. Using topic modeling, we explored whether the topics learned from tweets are comparable to responses of palliative care-related questions in the Health Information National Trends Survey.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233059PMC
May 2020

An external exposome-wide association study of hypertensive disorders of pregnancy.

Environ Int 2020 08 12;141:105797. Epub 2020 May 12.

Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.

It is widely recognized that exogenous factors play an important role in the development of hypertensive disorders of pregnancy (HDP). However, only a few external environmental factors have been studied, often separately, with no attempt to examine the totality of the external environment, or the external exposome. We conducted an external exposome-wide association study (ExWAS) using the Florida Vital Statistics Birth Records including 819,399 women with live births in 2010-2013. A total of 5784 factors characterizing women's surrounding natural, built, and social environment during pregnancy from 10 data sources were collected, harmonized, integrated, and spatiotemporally linked to the women based on pregnancy periods using 250 m buffers around their geocoded residential addresses. A random 50:50 split divided the data into discovery and replication sets, and a 3-phase procedure was used. In phase 1, associations between HDP and individual factors were examined, and Bonferroni adjustment was performed. In phase 2, an elastic net model was used to perform variable selection among significant variables from phase 1. In phase 3, a multivariable logistic regression model including all variables selected by the elastic net model was fitted. Variables that were significant in both the discovery and replication sets were retained. Among the 528 and 490 variables identified in Phase 1, 232 and 224 were selected by the elastic net model in Phase 2, and 67 and 48 variables remained statistically significant in Phase 3 in the discovery and replication sets, respectively. A total of 12 variables were significant in both the discovery and replication sets, including air toxicants (e.g., 2,2,4-trimethylpentane), meteorological factors (e.g., omega or vertical velocity at 125mb pressure level), neighborhood crime and safety (e.g., burglary rate), and neighborhood sociodemographic status (e.g., urbanization). This is the first large external exposome study of HDP. It confirmed some of the previously reported associations and generated unexpected predictors within the environment that may warrant more focused evaluation.
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http://dx.doi.org/10.1016/j.envint.2020.105797DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336837PMC
August 2020

A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis.

JMIR Public Health Surveill 2020 06 1;6(2):e19170. Epub 2020 Jun 1.

Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States.

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been growing exponentially, affecting over 4 million people and causing enormous distress to economies and societies worldwide. A plethora of analyses based on viral sequences has already been published both in scientific journals and through non-peer-reviewed channels to investigate the genetic heterogeneity and spatiotemporal dissemination of SARS-CoV-2. However, a systematic investigation of phylogenetic information and sampling bias in the available data is lacking. Although the number of available genome sequences of SARS-CoV-2 is growing daily and the sequences show increasing phylogenetic information, country-specific data still present severe limitations and should be interpreted with caution.

Objective: The objective of this study was to determine the quality of the currently available SARS-CoV-2 full genome data in terms of sampling bias as well as phylogenetic and temporal signals to inform and guide the scientific community.

Methods: We used maximum likelihood-based methods to assess the presence of sufficient information for robust phylogenetic and phylogeographic studies in several SARS-CoV-2 sequence alignments assembled from GISAID (Global Initiative on Sharing All Influenza Data) data released between March and April 2020.

Results: Although the number of high-quality full genomes is growing daily, and sequence data released in April 2020 contain sufficient phylogenetic information to allow reliable inference of phylogenetic relationships, country-specific SARS-CoV-2 data sets still present severe limitations.

Conclusions: At the present time, studies assessing within-country spread or transmission clusters should be considered preliminary or hypothesis-generating at best. Hence, current reports should be interpreted with caution, and concerted efforts should continue to increase the number and quality of sequences required for robust tracing of the epidemic.
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http://dx.doi.org/10.2196/19170DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265655PMC
June 2020

Under-the-Radar Dengue Virus Infections in Natural Populations of Aedes aegypti Mosquitoes.

mSphere 2020 04 29;5(2). Epub 2020 Apr 29.

Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA

The incidence of locally acquired dengue infections increased during the last decade in the United States, compelling a sustained research effort concerning the dengue mosquito vector, , and its microbiome, which has been shown to influence virus transmission success. We examined the "metavirome" of four populations of mosquitoes collected in 2016 to 2017 in Manatee County, FL. Unexpectedly, we discovered that dengue virus serotype 4 (DENV4) was circulating in these mosquito populations, representing the first documented case of such a phenomenon in the absence of a local DENV4 human case in this county over a 2-year period. We confirmed that all of the mosquito populations carried the same DENV4 strain, assembled its full genome, validated infection orthogonally by reverse transcriptase PCR, traced the virus origin, estimated the time period of its introduction to the Caribbean region, and explored the viral genetic signatures and mosquito-specific virome associations that potentially mediated DENV4 persistence in mosquitoes. We discuss the significance of prolonged maintenance of the DENV4 infections in that occurred in the absence of a DENV4 human index case in Manatee County with respect to the inability of current surveillance paradigms to detect mosquito vector infections prior to a potential local outbreak. Since 1999, dengue outbreaks in the continental United States involving local transmission have occurred only episodically and only in Florida and Texas. In Florida, these episodes appear to be coincident with increased introductions of dengue virus into the region through human travel and migration from countries where the disease is endemic. To date, the U.S. public health response to dengue outbreaks has been largely reactive, and implementation of comprehensive arbovirus surveillance in advance of predictable transmission seasons, which would enable proactive preventative efforts, remains unsupported. The significance of our finding is that it is the first documented report of DENV4 transmission to and maintenance within a local mosquito vector population in the continental United States in the absence of a human case during two consecutive years. Our data suggest that molecular surveillance of mosquito populations in high-risk, high-tourism areas of the United States may enable proactive, targeted vector control before potential arbovirus outbreaks.
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http://dx.doi.org/10.1128/mSphere.00316-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193045PMC
April 2020

Portable nanopore analytics: are we there yet?

Bioinformatics 2020 08;36(16):4399-4405

Department of Epidemiology, University of Florida, Gainesville, FL 32610, USA.

Motivation: Oxford Nanopore technologies (ONT) add miniaturization and real time to high-throughput sequencing. All available software for ONT data analytics run on cloud/clusters or personal computers. Instead, a linchpin to true portability is software that works on mobile devices of internet connections. Smartphones' and tablets' chipset/memory/operating systems differ from desktop computers, but software can be recompiled. We sought to understand how portable current ONT analysis methods are.

Results: Several tools, from base-calling to genome assembly, were ported and benchmarked on an Android smartphone. Out of 23 programs, 11 succeeded. Recompilation failures included lack of standard headers and unsupported instruction sets. Only DSK, BCALM2 and Kraken were able to process files up to 16 GB, with linearly scaling CPU-times. However, peak CPU temperatures were high. In conclusion, the portability scenario is not favorable. Given the fast market growth, attention of developers to ARM chipsets and Android/iOS is warranted, as well as initiatives to implement mobile-specific libraries.

Availability And Implementation: The source code is freely available at: https://github.com/marco-oliva/portable-nanopore-analytics.
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http://dx.doi.org/10.1093/bioinformatics/btaa237DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828464PMC
August 2020

Design and methodology challenges of environment-wide association studies: A systematic review.

Environ Res 2020 04 19;183:109275. Epub 2020 Feb 19.

Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA. Electronic address:

Environment-wide association studies (EWAS) are an untargeted, agnostic, and hypothesis-generating approach to exploring environmental factors associated with health outcomes, akin to genome-wide association studies (GWAS). While design, methodology, and replicability standards for GWAS are established, EWAS pose many challenges. We systematically reviewed published literature on EWAS to categorize scope, impact, types of analytical approaches, and open challenges in designs and methodologies. The Web of Science and PubMed databases were searched through multiple queries to identify EWAS articles between January 2010 and December 2018, and a systematic review was conducted following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting standard. Twenty-three articles met our inclusion criteria and were included. For each study, we categorized the data sources, the definitions of study outcomes, the sets of environmental variables, and the data engineering/analytical approaches, e.g. neighborhood definition, variable standardization, handling of multiple hypothesis testing, model selection, and validation. We identified limited exploitation of data sources, high heterogeneity in analytical approaches, and lack of replication. Despite of the promising utility of EWAS, further development of EWAS will require improved data sources, standardization of study designs, and rigorous testing of methodologies.
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http://dx.doi.org/10.1016/j.envres.2020.109275DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346707PMC
April 2020

The global spread of 2019-nCoV: a molecular evolutionary analysis.

Pathog Glob Health 2020 03 12;114(2):64-67. Epub 2020 Feb 12.

Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.

The global spread of the 2019-nCoV is continuing and is fast moving, as indicated by the WHO raising the risk assessment to high. In this article, we provide a preliminary phylodynamic and phylogeographic analysis of this new virus. A Maximum Clade Credibility tree has been built using the 29 available whole genome sequences of 2019-nCoV and two whole genome sequences that are highly similar sequences from Bat SARS-like Coronavirus available in GeneBank. We are able to clarify the mechanism of transmission among the countries which have provided the 2019-nCoV sequence isolates from their patients. The Bayesian phylogeographic reconstruction shows that the 2019-2020 nCoV most probably originated from the Bat SARS-like Coronavirus circulating in the bat family. In agreement with epidemiological observations, the most likely geographic origin of the new outbreak was the city of Wuhan, China, where 2019-nCoV time of the most recent common ancestor emerged, according to molecular clock analysis, around November 25, 2019. These results, together with previously recorded epidemics, suggest a recurring pattern of periodical epizootic outbreaks due to . Moreover, our study describes the same population genetic dynamic underlying the SARS 2003 epidemic, and suggests the urgent need for the development of effective molecular surveillance strategies of among animals and of the bat family.
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http://dx.doi.org/10.1080/20477724.2020.1725339DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099638PMC
March 2020

Are Preregistration and Registered Reports Vulnerable to Hacking?

Epidemiology 2020 05;31(3):e32

Department of Management, Warrington College of Business, University of Florida, Gainesville, FL.

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http://dx.doi.org/10.1097/EDE.0000000000001162DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757516PMC
May 2020
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