Gain and loss of function CFTR alleles modulate COVID-19 clinical outcome

Background/Objectives: We previously demonstrated that carrying a single pathogenic CFTR allele increases the risk for COVID-19 severity and mortality rate. We now aim to clarify the role of several uncharacterized rare alleles, including complex (cis) alleles, and in trans combinations. Method(s): LASSO logistic regression was used for the association of sets of variants, stratified by MAF, with severity. Immortalized cystic fibrosis bronchial epithelial cell lines and Fischer Rat Thyroid cells were transfected by plasmid carrying specific CFTR mutations. YFP-based assays were used to measure CFTR activity. Result(s): Here we functionally demonstrate that the rare (MAF=0.007) complex G576V/R668C allelemitigates the disease by a gain of function mechanism. Several novel CFTR ultra-rare (MAF <0.001) alleles were proved to have a reduced function;they are associated with disease severity either alone (single or complex alleles) or with another hypomorphic allele in the second chromosome, with a global reduction of CFTR activity between 40 to 72%. Conclusion(s): CFTR is a bidirectional modulator of COVID-19 outcome. At-risk subjects do not have open cystic fibrosis before viral infection and therefore are not easily recognisable in the general population unless a genetic analysis is performed. As the CFTR activity is partially retained, CFTR potentiator drugs could be an option as add-on therapy for at-risk patients.

Diagnostic Performance of Human Readers and Artificial Intelligence Model in Classification Task of Computed Tomography Images of Different Types of Viral Pneumonia

Purposes: Artificial Intelligence (AI) models are constantly developing to help clinicians in challenging tasks such as classification of images in radiological practice. The aim of this work was to compare the diagnostic performance of an AI classifier model developed in our hospital with the results obtained from the radiologists reading the CT images in discriminating different types of viral pneumonia. Material(s) and Method(s): Chest CT images of 1028 patients with positive swab for SARS-CoV-2 (n=646) and other respiratory viruses (n=382) were segmented automatically for lung extraction and Radiomic Features (RF) of first (n=18) and second (n=120) order were extracted using PyRadiomics tools. RF, together with patient age and sex, were used to develop a Multi-Layer Perceptron classifier to discriminate images of patients with COVID-19 and non-COVID-19 viral pneumonia. The model was trained with 808 CT images performing a LASSO regression (Least Absolute Shrinkage and Selection Operator), a hyper-parameter tuning and a final 4-fold cross validation. The remaining 220 CT images (n=151 COVID-19, n=69 non-COVID-19) were used as independent validation (IV) dataset. Four readers (three radiologists with >10 years of experience and one radiology resident with 3 years of experience) were recruited to blindly evaluate the IV dataset using the 5-points scale CO-RADS score. CT images with CO-RADS >=3 were considered "COVID-19". The same images were classified as "COVID-19" or "non-COVID-19" by applying the AI model with a threshold on the predicted values of 0.5. Diagnostic accuracy, specificity, sensibility and F1 score were calculated for human readers and AI model. Result(s): The AI model was trained using 24 relevant features while the Area under ROC curve values after 4-fold cross validation and its application to the IV dataset were, respectively, 0.89 and 0.85. Interreader agreement in assigning CO-RADS class, analyzed with Fleiss' kappa with ordinal weighting, was good (k=0.68;IC95% 0.63-0.72) and diagnostic performance were then averaged among readers. Diagnostic accuracy, specificity, sensibility and F1 score resulted 78.6%, 78.3%, 78.8% and 78.5% for AI model and 77.7%, 65.6%, 83.3% and 72.0% for human readers. The difference between specificity and sensitivity observed in human readers could be related to the higher rate of false positive due to the higher incidence of COVID-19 patients in comparison with other types of viral pneumonitis during the last 2 years. Conclusion(s): A model based on RF and artificial intelligence provides comparable results with human readers in terms of diagnostic performance in a classification task.Copyright © 2023 Southern Society for Clinical Investigation.

Optimal Knowledge Component Extracting Model for Knowledge-Concept Graph Completion in Education

As people have become accustomed to non-face-to-face education because of the COVID-19 pandemic, adaptive and personalized learning is being emphasized in the field of education. Learning paths suitable for each student may differ from those normally provided by teachers. To support coaching based on the concept of adaptive learning, the first step is to discover the relationships among the concepts in the curriculum provided in the form of a knowledge graph. In this study, feature reduction for the target knowledge-concept was first performed using Elastic Net and Random Forest algorithms, which are known to have the best performance in machine learning. Deep knowledge tracing (DKT) in the form of a dual-net, which is more efficient because of the already slimmer data, was then applied to increase the accuracy of feature selection. The new approach, termed the optimal knowledge component extracting (OKCE) model, was proven to be superior to a feature reduction approach using only Elastic Net and Random Forest using both open and commercial datasets. Finally, the OKCE model showed a meaningful knowledge-concept graph that could help teachers in adaptive and personalized learning. © 2013 IEEE.

Detection of COVID-19 in hospitalised patients by exhaled breath analysis

Introduction: SARS-COV-2 is mainly transmitted through respiratory droplets. The standard diagnostic procedure is based on a reverse transcription polymerase chain reaction (RT-PCR). Aim(s): 1) To develop a safe and easy to perform breath test for the detection of COVID-19 in hospitalised patients based on the analysis of volatile organic compounds (VOCs) in exhaled breath. 2) To differentiate in hospitalised patients with respiratory symptoms those with and without COVID-19. Method(s): We performed a monocenter, cross-sectional, case-control study in 38 subjects (63% males, age 62+/-12.7 yrs) admitted at the pulmonology ward. Breath samples were taken using a home-made sampling system. Analysis of breath samples was performed by proton transfer high resolution mass spectrometry (PTR-HRMS). A lassoregression with leave-one-out cross-validation was performed to differentiate the groups and designate the most differentiating VOCs. Result(s): COVID-19 positive (n=22) and control respiratory patients (n=16) were similar with respect to baseline characteristics, except for lower blood neutrophil and lymphocyte counts and higher ferritin level in COVID+ve patients (p<0.05). Lasso-regression revealed 6 VOCs as potential biomarkers that differentiated between both groups with 84% accuracy, 100% specificity and 100% positive predictive value based on PTR-HRMS data. Conclusion(s): Breath analysis could identify a breathprint differentiating between hospitalised COVID-19 and nonCOVID-19 patients with respiratory symptoms with a good accuracy. Therefore, VOCs profiling could be integrated in sensors allowing a fast breathalyzer for COVID-19 for large-scale screening.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Development and Validation of Multivariable Prediction Models of Serological Response to SARS-CoV-2 Vaccination in Kidney Transplant Recipients

Background: Repeated vaccination against SARS-CoV-2 increases serological response in kidney transplant recipients (KTR) with high interindividual variability. Still, no decision support tool exists to predict SARS-CoV-2 vaccination response in KTR. Method(s): We developed, internally and externally validated five different multivariable prediction models of serological response after the third and fourth vaccine dose against SARS-CoV-2 in KTR. Using 27 candidate predictor variables, we applied statistical and machine learning approaches including logistic regression (LR), LASSO LR, random forest, and gradient boosted regression trees. For development and internal validation, data from 585 vaccinations were used. External validation was performed in four independent, international validation datasets comprising 191, 184, 254, and 321 vaccinations, respectively. Result(s): Internal validation using a rigorous resampling approach showed AUC-ROC of 0.825 for LASSO LR, which was then used for model fitting and external validation. LASSO LR performed on the whole development dataset yielded a 23- and 11-variable model, respectively. External validation showed ROC-AUC of 0.855, 0.749, 0.828, and 0.763 for the sparser 11-variable model, yielding an overall AUC-ROC of 0.809, and a negative predictive value of 0.752. The 23-variable model showed AUC-ROC of 0.853, 0.714, 0.844, and 0.778 in four independent validation sets, yielding an overall AUC-ROC of 0.818, and a negative predictive value of 0.795. Conclusion(s): Both, an 11- and 23-variable LASSO LR model predict vaccination response in KTR with good AUC-ROC. Implemented as an online tool at https://www.txvaccine. com, it can guide decisions when choosing between different immunization strategies to improve protection against COVID-19 in KTR. (Figure Presented).

EXTREMELY RARE CASE OF CARTILAGINOUS TUMOR IN THE POSTERIOR WALL OF THE TRACHEA

SESSION TITLE: Lung Cancer Case Report Posters 3 SESSION TYPE: Case Report Posters PRESENTED ON: 10/17/2022 12:15 pm - 01:15 pm INTRODUCTION: Tracheal tumor accounts for 0.4% of all tumors and only 10% of them are benign (1). We present, to our knowledge, the first case of a primary benign tracheal tumor with features of chondroid metaplasia arising from the posterior wall of the trachea. CASE PRESENTATION: 58-year-old male non—smoker with non-significant past medical history, presented to the Emergency department for COVID-19 pneumonia. CTA chest was done showing bilateral pulmonary embolism and a 12 mm polypoid tracheal mass arising from the posterior wall of the trachea extending into the lumen (Figure#1). The patient was asymptomatic prior to his COVID 19 infection;he denied any chest pain, hemoptysis, trauma, or prior intubation. After recovering from COVID-19, the patient was scheduled for an outpatient rigid bronchoscopy which revealed a tracheal polyp arising from the mid-distal posterior membranous trachea. (Figure#2). An electrocautery snare was used to simultaneously cut and cauterize the stalk using a lasso technique. The polyp was removed in its entirety without complication. Histopathology examination demonstrated a respiratory epithelium lined cyst with cartilaginous tissue, favoring chondroid metaplasia. DISCUSSION: Primary benign tracheal tumors with cartilaginous features are uncommon, especially in the posterior membrane of the trachea, which lacks cartilaginous support. Diagnosis of any benign tracheal tumor is usually delayed since most patients are asymptomatic. The majority of such tumors are found incidentally, as in this case. One of the most common benign tracheal tumors is hamartoma, which can have respiratory epithelium and cartilaginous tissue, however they do not have features of chondroid metaplasia, and are generally found in the lateral or anterior wall of the trachea. Furthermore, endobronchial lesions only account for 3% of all pulmonary hamartomas. (2) Reports of airway chondroid metaplasia are usually described in the larynx and are commonly associated with prior trauma or inflammation in the area which is not known to have occurred in this case (3). The histopathologic findings and unusual location of this tumor makes this case unique. CONCLUSIONS: The tracheal origin of this benign tumor, arising from the posterior membrane with cartilaginous features is extremely rare, and has not previously been described in the literature. Reference #1: Park CM, Goo JM, Lee HJ, Kim MA, Lee CH, Kang MJ. Tumors in the tracheobronchial tree: CT and FDG PET features. Radiographics. 2009 Jan-Feb;29(1):55-71. doi: 10.1148/rg.291085126. PMID: 19168836. Reference #2: Hurst IJ Jr, Nelson KG. Tracheal hamartoma. Chest. 1977 Nov;72(5):661-2. doi: 10.1378/chest.72.5.661. PMID: 913152. Reference #3: Orlandi A, Fratoni S, Hermann I, Spagnoli LG. Symptomatic laryngeal nodular chondrometaplasia: a clinicopathological study. J Clin Pathol. 2003 Dec;56(12):976-7. doi: 10.1136/jcp.56.12.976. PMID: 14645364;PMCID: PMC1770148. DISCLOSURES: No relevant relationships by Jorge Cedano Consultant relationship with Olympus America Please note: 8/1/21-present Added 04/18/2022 by Lucas Pitts, value=Consulting fee

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Association of Ventilator Settings and Lung Mechanics During VV-ECMO Treatment with Short Term Pulmonary Function and Lung Imaging Outcomes in COVID-19 Survivors, a Retrospective Cohort Study

Background: Lung protective ventilation plays a crucial role in the management of patients with COVID-19 ARDS treated with VV-ECMO. We hypothesized that increasingly protective ventilator settings may be associated with improved lung recovery by reducing ventilator induced lung injury. Method(s): We performed a retrospective cohort study of all patients treated with VV-ECMO for COVID-19 at NYU Langone Medical Center from March 2020 to June 2020. Ventilator data including tidal volume (cc/ kg predicted body weight), peak airway pressure, PEEP, Driving pressure (DP), Respiratory Rate, FiO2, lung compliance, and mechanical power were obtained. Pulmonary function test (PFT) results, 6-minute walk test results, and quantitative chest CT scores were obtained from the first outpatient follow up assessment. Bivariate and multivariate analysis correlating ventilator data with lung function and CT outcomes was performed. Result(s): 30 COVID-19 patients were treated with VV-ECMO during the study period, of which 26 survived without lung transplantation and 12 completed follow up assessment at a median of 106 days post ECMO decannulation. Multivariate LASSO regression model results;FEV1: DP (beta=-5.535), Respiratory Rate (beta=-0.370), compliance (beta=0.467), FVC: DP (beta=-4.08), compliance (beta=0.875), preECMO tidal volume (beta=-0.0008), TLC: DP (beta=-4.518), ECMO sweep (beta=-0.598), DLCO: peak airway presure (beta=-1.836), 6MWT distance: compliance (beta=1.436), Chest CT total opacity score: DP(beta=-0.60), preECMO tidal volume(beta=-0.0033). Conclusion(s): Driving pressure and peak airway pressure during VV-ECMO had the strongest associations with improved short-term follow up lung volumes, DLCO, and chest CT outcomes in VV-ECMO treated COVID-19 survivors.

USING MOBILE PHONES FOR COLLECTING HEALTH INFORMATION IN THE SUB SAHARAN AFRICA DURING THE COVID-19 PANDEMIC: TAKING LESSONS FROM THE WORLD DEVELOPMENT INDICATORS DATABASE

Objective The primary aim of this study was to portray the level of spread and the dynamic of diffusion of mobile phone technology in sub-Saharan Africa during the last two decades. The secondary aim was to investigate factors related to the use of mobile phone technology in sub-Saharan Africa and to derive profiles of the most suitable areas to conduct mobile phone technology-based research. Methods The present work was based on the data collected by the World Bank database;a collection of public access data derived from yearly surveys conducted at country level. Two methods were applied to perform the selection of variables related to the diffusion of mobile phones in sub-Saharan Africa. Firstly, a Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied. Afterwards, a system of simultaneous equation was applied to estimate the model coefficients and determine the joint statistical significance. Results The number of mobile phones subscriptions in relation to the population of sub-Saharan Africa has increased consistently during the period 2000 to 2010. The rate of mobile phones subscriptions in relation to the population ranged between less than 1% to more than 90%. Urban areas and having a lower number of people leaving in slums seems to be the most suitable places to conduct mobile phone-based interviews. This information is useful in identifying countries and macro areas to conduct mobile phone interviews;and this could be extended to smallest area within a country. Discussion More effort is required to better understand how to identify areas suitable for conducting research using mobile phones and other electronic-based tools. Such an effort should be based on individual level surveys to understand not only the material possibility but also the will to participate to research based on data capturing made by mobile phones and similar tools.

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.

Introduction: The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features. Material and methods: The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics. Results: Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7%, specificity of 91.3%, the sensitivity of 91.6%, F-measure of 91.8%, and AUC of 0.91%. Conclusion: The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective.

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Identification and validation of prognostic factors in patients with COVID-19: A retrospective study based on artificial intelligence algorithms.

Background: Novel coronavirus disease 2019 (COVID-19) is an ongoing global pandemic with high mortality. Although several studies have reported different risk factors for mortality in patients based on traditional analytics, few studies have used artificial intelligence (AI) algorithms. This study investigated prognostic factors for COVID-19 patients using AI methods. Methods: COVID-19 patients who were admitted in Wuhan Infectious Diseases Hospital from December 29, 2019 to March 2, 2020 were included. The whole cohort was randomly divided into training and testing sets at a 6:4 ratio. Demographic and clinical data were analyzed to identify predictors of mortality using least absolute shrinkage and selection operator (LASSO) regression and LASSO-based artificial neural network (ANN) models. The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis. Results: A total of 1145 patients (610 male, 53.3%) were included in the study. Of the 1145 patients, 704 were assigned to the training set and 441 were assigned to the testing set. The median age of the patients was 57 years (range: 47-66 years). Severity of illness, age, platelet count, leukocyte count, prealbumin, C-reactive protein (CRP), total bilirubin, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and Sequential Organ Failure Assessment (SOFA) score were identified as independent prognostic factors for mortality. Incorporating these nine factors into the LASSO regression model yielded a correct classification rate of 0.98, with area under the ROC curve (AUC) values of 0.980 and 0.990 in the training and testing cohorts, respectively. Incorporating the same factors into the LASSO-based ANN model yielded a correct classification rate of 0.990, with an AUC of 0.980 in both the training and testing cohorts. Conclusions: Both the LASSO regression and LASSO-based ANN model accurately predicted the clinical outcome of patients with COVID-19. Severity of illness, age, platelet count, leukocyte count, prealbumin, CRP, total bilirubin, APACHE II score, and SOFA score were identified as prognostic factors for mortality in patients with COVID-19.

Predicting the European stock market during COVID-19: A machine learning approach.

This research attempts to explore the total of 21 potential internal and external shocks to the European market during the Covid-19 Crisis. Using the time series of 1 Jan 2020 to 26 June 2020, I employ a machine learning technique, i.e. Least Absolute Shrinkage and Selection Operator (LASSO) to examine the research question for its benefits over the traditional regression methods. This further allows me to cater to the issue of limited data during the crisis and at the same time, allows both variable selection and regularization in the analysis. Additionally, LASSO is not susceptible to and sensitive to outliers and multi-collinearity. The European market is mostly affected by indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index. There is a significant difference in the predictors before and after the pandemic announcement by WHO. Before the Pandemic period announcement by WHO, Europe was hit by the gold market, EUR/USD exchange rate, Dow Jones index, Switzerland, Spain, France, Italy, Germany, and Turkey and after the announcement by WHO, only France and Germany were selected by the lasso approach. It is found that Germany and France are the most predictors in the European market.•A LASSO approach is used to predict the European stock market index during COVID-19•European market is mostly affected by the indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index.•There is a significant difference in the predictors before and after the pandemic announcement by WHO.