1
|
Er AG, Ding DY, Er B, Uzun M, Cakmak M, Sadee C, Durhan G, Ozmen MN, Tanriover MD, Topeli A, Aydin Son Y, Tibshirani R, Unal S, Gevaert O. Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study. NPJ Digit Med 2024; 7:117. [PMID: 38714751 PMCID: PMC11076490 DOI: 10.1038/s41746-024-01128-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/25/2024] [Indexed: 05/10/2024] Open
Abstract
Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients: Intensive care unit admission. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (cor(Xu1, Zv1) = 0.596, p value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.
Collapse
Affiliation(s)
- Ahmet Gorkem Er
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, 94305, USA.
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey.
- Department of Infectious Diseases and Clinical Microbiology, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey.
| | - Daisy Yi Ding
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Berrin Er
- Department of Internal Medicine, Division of Intensive Care Medicine, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey
| | - Mertcan Uzun
- Department of Infectious Diseases and Clinical Microbiology, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey
| | - Mehmet Cakmak
- Department of Internal Medicine, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey
| | - Christoph Sadee
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, 94305, USA
| | - Gamze Durhan
- Department of Radiology, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey
| | - Mustafa Nasuh Ozmen
- Department of Radiology, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey
| | - Mine Durusu Tanriover
- Department of Internal Medicine, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey
| | - Arzu Topeli
- Department of Internal Medicine, Division of Intensive Care Medicine, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey
| | - Yesim Aydin Son
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
- Department of Statistics, Stanford University, Stanford, CA, 94305, USA
| | - Serhat Unal
- Department of Infectious Diseases and Clinical Microbiology, Hacettepe University Faculty of Medicine, 06230, Ankara, Turkey
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, 94305, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
| |
Collapse
|
2
|
Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
Collapse
Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
| |
Collapse
|
3
|
Asteris PG, Gandomi AH, Armaghani DJ, Tsoukalas MZ, Gavriilaki E, Gerber G, Konstantakatos G, Skentou AD, Triantafyllidis L, Kotsiou N, Braunstein E, Chen H, Brodsky R, Touloumenidou T, Sakellari I, Alkayem NF, Bardhan A, Cao M, Cavaleri L, Formisano A, Guney D, Hasanipanah M, Khandelwal M, Mohammed AS, Samui P, Zhou J, Terpos E, Dimopoulos MA. Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm. J Cell Mol Med 2024; 28:e18105. [PMID: 38339761 PMCID: PMC10863978 DOI: 10.1111/jcmm.18105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/14/2023] [Accepted: 11/22/2023] [Indexed: 02/12/2024] Open
Abstract
Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.
Collapse
Affiliation(s)
- Panagiotis G. Asteris
- Computational Mechanics Laboratory, School of Pedagogical and Technological EducationAthensGreece
| | - Amir H. Gandomi
- Faculty of Engineering & ITUniversity of Technology SydneySydneyNew South WalesAustralia
- University Research and Innovation Center (EKIK), Óbuda UniversityBudapestHungary
| | - Danial J. Armaghani
- School of Civil and Environmental EngineeringUniversity of Technology SydneySydneyNew South WalesAustralia
| | - Markos Z. Tsoukalas
- Computational Mechanics Laboratory, School of Pedagogical and Technological EducationAthensGreece
| | - Eleni Gavriilaki
- 2nd Propedeutic Department of Internal MedicineAristotle University of ThessalonikiThessalonikiGreece
| | - Gloria Gerber
- Hematology DivisionJohns Hopkins UniversityBaltimoreUSA
| | - Gerasimos Konstantakatos
- Computational Mechanics Laboratory, School of Pedagogical and Technological EducationAthensGreece
| | - Athanasia D. Skentou
- Computational Mechanics Laboratory, School of Pedagogical and Technological EducationAthensGreece
| | - Leonidas Triantafyllidis
- Computational Mechanics Laboratory, School of Pedagogical and Technological EducationAthensGreece
| | - Nikolaos Kotsiou
- 2nd Propedeutic Department of Internal MedicineAristotle University of ThessalonikiThessalonikiGreece
| | | | - Hang Chen
- Hematology DivisionJohns Hopkins UniversityBaltimoreUSA
| | | | | | - Ioanna Sakellari
- Hematology Department – BMT UnitG Papanicolaou HospitalThessalonikiGreece
| | | | - Abidhan Bardhan
- Civil Engineering DepartmentNational Institute of Technology PatnaPatnaIndia
| | - Maosen Cao
- Department of Engineering MechanicsHohai UniversityNanjingChina
| | - Liborio Cavaleri
- Department of Civil, Environmental, Aerospace and Materials EngineeringUniversity of PalermoPalermoItaly
| | - Antonio Formisano
- Department of Structures for Engineering and ArchitectureUniversity of Naples “Federico II”NaplesItaly
| | - Deniz Guney
- Engineering FacultySan Diego State UniversitySan DiegoCaliforniaUSA
| | - Mahdi Hasanipanah
- Department of Geotechnics and Transportation, Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia
| | - Manoj Khandelwal
- Institute of Innovation, Science and SustainabilityFederation University AustraliaBallaratVictoriaAustralia
| | | | - Pijush Samui
- Civil Engineering DepartmentNational Institute of Technology PatnaPatnaIndia
| | - Jian Zhou
- School of Resources and Safety EngineeringCentral South UniversityChangshaChina
| | - Evangelos Terpos
- Department of Clinical Therapeutics, Medical School, Faculty of MedicineNational Kapodistrian University of AthensAthensGreece
| | - Meletios A. Dimopoulos
- Department of Clinical Therapeutics, Medical School, Faculty of MedicineNational Kapodistrian University of AthensAthensGreece
| |
Collapse
|
4
|
Er AG, Ding DY, Er B, Uzun M, Cakmak M, Sadee C, Durhan G, Ozmen MN, Tanriover MD, Topeli A, Son YA, Tibshirani R, Unal S, Gevaert O. Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19. RESEARCH SQUARE 2023:rs.3.rs-3569833. [PMID: 38045288 PMCID: PMC10690316 DOI: 10.21203/rs.3.rs-3569833/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (corr(Xu1, Zv1) = 0.596, p-value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.
Collapse
Affiliation(s)
- Ahmet Gorkem Er
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, 94305, USA
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Türkiye
- Department of Infectious Diseases and Clinical Microbiology, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Daisy Yi Ding
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Berrin Er
- Department of Internal Medicine, Division of Intensive Care Medicine, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Mertcan Uzun
- Department of Infectious Diseases and Clinical Microbiology, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Mehmet Cakmak
- Department of Internal Medicine, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Christoph Sadee
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, 94305, USA
| | - Gamze Durhan
- Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Mustafa Nasuh Ozmen
- Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Mine Durusu Tanriover
- Department of Internal Medicine, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Arzu Topeli
- Department of Internal Medicine, Division of Intensive Care Medicine, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Yesim Aydin Son
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Türkiye
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
- Department of Statistics, Stanford University, Stanford, CA, 94305, USA
| | - Serhat Unal
- Department of Infectious Diseases and Clinical Microbiology, Hacettepe University Faculty of Medicine, Ankara, 06230, Türkiye
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, 94305, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| |
Collapse
|
5
|
The Epidemiology of Infectious Diseases Meets AI: A Match Made in Heaven. Pathogens 2023; 12:pathogens12020317. [PMID: 36839589 PMCID: PMC9963936 DOI: 10.3390/pathogens12020317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
Infectious diseases remain a major threat to public health [...].
Collapse
|
6
|
Nicora G, Salemi M, Marini S, Bellazzi R. Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm. BMJ Health Care Inform 2022; 29:e100643. [PMID: 36593658 PMCID: PMC9742845 DOI: 10.1136/bmjhci-2022-100643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/18/2022] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVES The objective of this study is the implementation of an automatic procedure to weekly detect new SARS-CoV-2 variants and non-neutral variants (variants of concern (VOC) and variants of interest (VOI)). METHODS We downloaded spike protein primary sequences from the public resource GISAID and we represented each sequence as k-mer counts. For each week since 1 July 2020, we evaluate if each sequence represents an anomaly based on a One Class support vector machine (SVM) classification algorithm trained on neutral protein sequences collected from February to June 2020. RESULTS We assess the ability of the One Class classifier to detect known VOC and VOI, such as Alpha, Delta or Omicron, ahead of their official classification by health authorities. In median, the classifier predicts a non-neutral variant as outlier 10 weeks before the official date of designation as VOC/VOI. DISCUSSION The identification of non-neutral variants during a pandemic usually relies on indicators available during time, such as changing population size of a variant. Automatic variant surveillance systems based on protein sequences can enhance the fast identification of variants of potential concern. CONCLUSION Machine learning, and in particular One Class SVM classification, can support the detection of potentially VOC/VOI variants during an evolving pandemics.
Collapse
Affiliation(s)
- Giovanna Nicora
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Engenome s.r.l, Pavia, Italy
| | - Marco Salemi
- Department of Pathology, University of Florida, Gainesville, FL, USA
| | - Simone Marini
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| |
Collapse
|
7
|
Clerbaux LA, Albertini MC, Amigó N, Beronius A, Bezemer GFG, Coecke S, Daskalopoulos EP, del Giudice G, Greco D, Grenga L, Mantovani A, Muñoz A, Omeragic E, Parissis N, Petrillo M, Saarimäki LA, Soares H, Sullivan K, Landesmann B. Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework. J Clin Med 2022; 11:4464. [PMID: 35956081 PMCID: PMC9369763 DOI: 10.3390/jcm11154464] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 12/10/2022] Open
Abstract
Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors (age, sex, genetic factors, lipid disorders, heart failure, gut dysbiosis, diet, vitamin D deficiency, air pollution and exposure to chemicals) influence COVID-19 by applying the Adverse Outcome Pathway (AOP), which is well-established in regulatory toxicology. This framework aims to model the sequence of events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how those eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.
Collapse
Affiliation(s)
- Laure-Alix Clerbaux
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy; (S.C.); (E.P.D.); (N.P.); (M.P.); (B.L.)
| | | | - Núria Amigó
- Biosfer Teslab SL., 43204 Reus, Spain;
- Department of Basic Medical Sciences, Universitat Rovira i Virgili (URV), 23204 Reus, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
| | - Anna Beronius
- Institute of Environmental Medicine, Karolinska Institutet, 17177 Stockholm, Sweden;
| | - Gillina F. G. Bezemer
- Impact Station, 1223 JR Hilversum, The Netherlands;
- Department of Pharmaceutical Sciences, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 CG Utrecht, The Netherlands
| | - Sandra Coecke
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy; (S.C.); (E.P.D.); (N.P.); (M.P.); (B.L.)
| | - Evangelos P. Daskalopoulos
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy; (S.C.); (E.P.D.); (N.P.); (M.P.); (B.L.)
| | - Giusy del Giudice
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland; (G.d.G.); (D.G.); (L.A.S.)
| | - Dario Greco
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland; (G.d.G.); (D.G.); (L.A.S.)
| | - Lucia Grenga
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, F-30200 Bagnols-sur-Ceze, France;
| | - Alberto Mantovani
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161 Rome, Italy;
| | - Amalia Muñoz
- European Commission, Joint Research Centre (JRC), 2440 Geel, Belgium;
| | - Elma Omeragic
- Faculty of Pharmacy, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina;
| | - Nikolaos Parissis
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy; (S.C.); (E.P.D.); (N.P.); (M.P.); (B.L.)
| | - Mauro Petrillo
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy; (S.C.); (E.P.D.); (N.P.); (M.P.); (B.L.)
| | - Laura A. Saarimäki
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland; (G.d.G.); (D.G.); (L.A.S.)
| | - Helena Soares
- Laboratory of Immunobiology and Pathogenesis, Chronic Diseases Research Centre, Faculdade de Ciências Médicas Medical School, University of Lisbon, 1649-004 Lisbon, Portugal;
| | - Kristie Sullivan
- Physicians Committee for Responsible Medicine, Washington, DC 20016, USA;
| | - Brigitte Landesmann
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy; (S.C.); (E.P.D.); (N.P.); (M.P.); (B.L.)
| |
Collapse
|
8
|
Kumar A, Asghar A, Dwivedi P, Kumar G, Narayan RK, Jha RK, Parashar R, Sahni C, Pandey SN. A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2022; 3:e36860. [PMID: 36193192 PMCID: PMC9516867 DOI: 10.2196/36860] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 08/26/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022]
Abstract
Background Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model. Objective We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave. Methods We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period. Results Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant). Conclusions Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.
Collapse
Affiliation(s)
- Ashutosh Kumar
- Department of Anatomy All India Institute of Medical Sciences - Patna Patna India
| | - Adil Asghar
- Department of Anatomy All India Institute of Medical Sciences - Patna Patna India
| | - Prakhar Dwivedi
- Department of Anatomy All India Institute of Medical Sciences - Patna Patna India
| | - Gopichand Kumar
- Department of Anatomy All India Institute of Medical Sciences - Patna Patna India
| | - Ravi K Narayan
- Department of Anatomy Dr B C Roy Multispeciality Medical Research Center Indian Institute of Technology-Kharagpur Kharagpur India
| | - Rakesh K Jha
- Department of Anatomy All India Institute of Medical Sciences - Patna Patna India
| | - Rakesh Parashar
- India Health Lead Oxford Policy Management Limited Oxford United Kingdom
| | - Chetan Sahni
- Department of Anatomy Institute of Medical Sciences Banaras Hindu University Varanasi India
| | - Sada N Pandey
- Department of Zoology Banaras Hindu University Varanasi India
| |
Collapse
|