A systematic review on AI/ML approaches against COVID-19 outbreak
Tóm tắt
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.
Từ khóa
Tài liệu tham khảo
Wuhan Municipal Health Commission (2019) Report of clustering pneumonia of unknown aetiology in Wuhan City. http://wjw.wuhan.gov.cn/front/web/showDetail/201. Accessed 20 Jan 2020
Tiwari SM, Gaurav D, Abraham A (2020) COVID-19 outbreak in India: an early stage analysis. Int J Sci Rep 6(8):332–339
Jahanbin K, Rahmanian V et al (2020) Using twitter and web news mining to predict COVID-19 outbreak. Asian Pac J Trop Med 13(8):378
Ferguson NM, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, Bhatia S, Boonyasiri A, Cucunubá Z, Cuomo-Dannenburg G et al (2020) Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College COVID-19 Response Team
COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (2020) https://github.com/CSSEGISandData/COVID-19/blob/master/README.md. Accessed 29 July 2020
Baldwin R (2020) https://bit.ly/3vyXRhH. Accessed 30 May 2020
Gaurav D, Tiwari SM, Goyal A, Gandhi N, Abraham A (2020) Machine intelligence-based algorithms for spam filtering on document labeling. Soft Comput 24(13):9625–9638
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A et al (2017) Mastering the game of go without human knowledge. Nature 550(7676):354–359
Agrebi S, Larbi A (2020) Use of artificial intelligence in infectious diseases. In: Barh D (ed) Artificial intelligence in precision health. Academic Press, pp 415–438
Chakraborti S, Maiti A, Pramanik S, Sannigrahi S, Pilla F, Banerjee A, Das DN (2021) Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: a case for continent specific COVID-19 analysis. Sci Total Environ 765:142723
Muammer T (2021) Covidetectionet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble. Appl Intell 51:1213–1226
Islam MZ, Islam MM, Asraf A (2020) A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inform Med Unlocked 20:100412
Shahid F, Zameer A, Muneeb M (2020) Predictions for COVID-19 with deep learning models of LSTM, GRU and BI-LSTM. Chaos Solitons Fractals 140:110212
Rachna J, Meenu G, Soham T, Jude HD (2021) Deep learning based detection and analysis of COVID-19 on chest X-ray images. Appl Intell 51:1690–1700
Nguyen TT (2020) Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions, preprint on webpage at arXiv:2008.07343
Maghdid HS, Ghafoor KZ, Sadiq AS, Curran K, Rabie K (2020) A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: design study, preprint on webpage at arXiv:2003.07434
Kumar A, Gupta PK, Srivastava A (2020) A review of modern technologies for tackling COVID-19 pandemic. Diabetes Metab Syndr Clin Res Rev 14(4):569–573
Nguyen D, Gao K, Chen J, Wang R, Wei G (2020) Potentially highly potent drugs for 2019-ncov, preprint on webpage at https://doi.org/10.1101/2020.02.05.936013v1
Bullock J, Luccioni A, Pham KH, Lam CSN, Luengo-Oroz M (2020) Mapping the landscape of artificial intelligence applications against COVID-19. J Artif Intell Res 69:807–845
Al-Waisy AS, Al-Fahdawi S, Mohammed MA, Abdulkareem KH, Mostafa SA, Maashi MS, Arif M, Garcia-Zapirain B (2020) COVID-chexnet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Comput 24:1–16
Khaled Bayoudh AM, Hamdaoui F (2020) Hybrid-COVID: a novel hybrid 2d/3d CNN based on cross-domain adaptation approach for COVID-19 screening from chest X-ray images. Phys Eng Sci Med 43(4):1415–1431
Mohammed MA, Abdulkareem KH, Garcia-Zapirain B, Mostafa SA, Maashi MS, Al-Waisy AS, Subhi MA, Mutlag AA, Le DN (2020) A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of COVID-19 based on X-ray images. Comput Mater Continua 66(3):3289–3310
Mishra S, Sagban R, Yakoob A, Gandhi N (2018) Swarm intelligence in anomaly detection systems: an overview. Int J Comput Appl 43:109–118
Rahul M, Kohli N, Agarwal R, Mishra S (2019) Facial expression recognition using geometric features and modified hidden Markov model. Int J Grid Util Comput 10(5):488–496
Brereton P, Kitchenham BA, Budgen D, Turner M, Khalil M (2007) Lessons from applying the systematic literature review process within the software engineering domain. J Syst Softw 80(4):571–583
Kadi I, Idri A, Fernandez-Aleman J (2017) Knowledge discovery in cardiology: a systematic literature review. Int J Med Inform 97:12–32
Petersen K, Feldt R, Mujtaba S, Mattsson M (2008) Systematic mapping studies in software engineering. In: 12th international conference on evaluation and assessment in software engineering (EASE) 12, pp 1–10
Roberto R, Lima JP, Teichrieb V (2016) Tracking for mobile devices: a systematic mapping study. Comput Graph 56:20–30
Petersen K, Gencel C (2013) Worldviews, research methods, and their relationship to validity in empirical software engineering research. In: 2013 Joint Conference of the 23rd international workshop on software measurement and the 8th international conference on software process and product measurement. IEEE, pp 81–89
Kitchenham B, Brereton OP, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering: a systematic literature review. Inf Softw Technol 51(1):7–15
Chowdhury ME, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, Islam KR, Khan MS, Iqbal A, Al Emadi N et al (2020) Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665–132676
Ardabili SF, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy AR, Reuter U, Rabczuk T, Atkinson PM (2020) COVID-19 outbreak prediction with machine learning. Algorithms 13(10):249
Chimmula VKR, Zhang L (2020) Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals 135:109864
Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M (2020) COVID-19 image data collection: prospective predictions are the future. J Mach Learn Biomed Imaging 2:1–38
Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A (2020) Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput Biol Med 121:103795
Kavadi DP, Patan R, Ramachandran M, Gandomi AH (2020) Partial derivative nonlinear global pandemic machine learning prediction of COVID 19. Chaos Solitons Fractals 139:110056
Mohammed MA, Abdulkareem KH, Al-Waisy AS, Mostafa SA, Al-Fahdawi S, Dinar AM, Alhakami W, Abdullah B, Al-Mhiqani MN, Alhakami H et al (2020) Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and Topsis methods. IEEE Access 8:99115–99131
Abbas A, Abdelsamea MM, Gaber MM (2020) Classification of COVID-19 in chest X-ray images using detrac deep convolutional neural network. Appl Intell 51:854–864
Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A (2020) COVID-caps: a capsule network-based framework for identification of COVID-19 cases from X-ray images. Pattern Recognit Lett 138:638–643
Manapure P, Likhar K, Kosare H (2020) Detecting COVID-19 in X-ray images with keras, tensor flow, and deep learning. Artif Comput Intell 2(3):1–6
Asif S, Wenhui Y (2020) Automatic detection of COVID-19 using X-ray images with deep convolutional neural networks and machine learning, preprint on webpage at https://doi.org/10.1101/2020.05.01.20088211v2
Asnaoui KE, Chawki Y, Idri A (2020) Automated methods for detection and classification pneumonia based on X-ray images using deep learning, preprint on webpage at arXiv:2003.14363
Ghoshal B, Tucker A (2020) Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection, preprint on webpage at arXiv:2003.10769
El Asnaoui K, Chawki Y (2020) Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn 39:1–12
Khan AI, Shah JL, Bhat MM (2020) Coronet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput Methods Programs Biomed 196:105581
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge
Jin C, Chen W, Cao Y, Xu Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H, Feng J (2020) Development and evaluation of an AI system for COVID-19 diagnosis, preprint on webpage at https://doi.org/10.1101/2020.03.20.20039834v3
Karim MR, Döhmen T, Cochez M, Beyan O, Rebholz-Schuhmann D, Decker S (2020) Deepcovidexplainer: explainable COVID-19 diagnosis from chest X-ray images. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 1034–1037
Shibly KH, Dey SK, Islam MT-U, Rahman MM (2020) COVID faster R-CNN: a novel framework to diagnose novel coronavirus disease (COVID-19) in X-ray images. Inform Med Unlocked 20:100405
Toraman S, Alakus TB, Turkoglu I (2020) Convolutional capsnet: a novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos Solitons Fractals 140:110122
Das NN, Kumar N, Kaur M, Kumar V, Singh D (2020) Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays, preprint on webpage at https://doi.org/10.1016/j.irbm.2020.07.001
Altan A, Karasu S (2020) Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2d curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos Solitons Fractals 140:110071
Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Ni Q, Chen Y, Su J et al (2020) A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6(10):1122–1129
Zhu H, Guo Q, Li M, Wang C, Fang Z, Wang P, Tan J, Wu S, Xiao Y (2020) Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm, preprint on webpage at https://doi.org/10.1101/2020.01.21.914044v4
Zhu JS, Ge P, Jiang C, Zhang Y, Li X, Zhao Z, Zhang L, Duong TQ (2020) Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients. J Am Coll Emerg Physicians Open 1(6):1364–1373
Hall LO, Paul R, Goldgof DB, Goldgof GM (2020) Finding COVID-19 from chest X-rays using deep learning on a small dataset, preprint on webpage at arXiv:2004.02060
Jamil M, Hussain I et al (2020) Automatic detection of COVID-19 infection from chest x-ray using deep learning, preprint on webpage at https://doi.org/10.1101/2020.05.10.20097063v1
Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, Hou X, Ma W, Xu Z, Zheng Z et al (2020) Ai-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks, preprint on webpage at https://doi.org/10.1101/2020.03.19.20039354v1
Voulodimos A, Protopapadakis E, Katsamenis I, Doulamis A, Doulamis N (2020) Deep learning models for COVID-19 infected area segmentation in CT images, preprint on webpage at https://doi.org/10.1101/2020.05.08.20094664v2
Maghdid HS, Asaad AT, Ghafoor KZ, Sadiq AS, Khan MK (2020) Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms, preprint on webpage at arXiv:2004.00038
Alakus TB, Turkoglu I (2020) Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals 140:110120
Obaid OI, Mohammed MA, Mostafa SA (2020) Long short-term memory approach for coronavirus disease prediction. J Inf Technol Manag 12:11–21
Farooq M, Hafeez A (2020) COVID-resnet: a deep learning framework for screening of covid19 from radiographs, preprint on webpage at arXiv:2003.14395
Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q et al (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT, preprint on webpage at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233473/
Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S (2020) Deep transfer learning based classification model for COVID-19 disease, preprint on webpage at https://doi.org/10.1016/j.irbm.2020.05.003
Zhang J, Xie Y, Li Y, Shen C, Xia Y (2020) COVID-19 screening on chest X-ray images using deep learning based anomaly detection, preprint on webpage at arXiv:2003.12338
Castiglioni I, Ippolito D, Interlenghi M, Monti CB, Salvatore C, Schiaffino S, Polidori A, Gandola D, Messa C, Sardanelli F (2020) Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy, preprint on webpage at https://doi.org/10.1101/2020.04.08.20040907v1
Dogan O, Martinez-Millana A, Rojas E, Sepúlveda M, Munoz-Gama J, Traver V, Fernandez-Llatas C (2019) Individual behavior modeling with sensors using process mining. Electronics 8(7):766
Dogan O, Oztaysi B (2019) Genders prediction from indoor customer paths by Levenshtein-based fuzzy KNN. Expert Syst Appl 136:42–49
Li X, Pang T, Xiong B, Liu W, Liang P, Wang T (2017) Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: 2017 10th international congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI). IEEE, pp 1–11
Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274–282
Ahamad MM, Aktar S, Rashed-Al-Mahfuz M, Uddin S, Liò P, Xu H, Summers MA, Quinn JM, Moni MA (2020) A machine learning model to identify early stage symptoms of SARS-COV-2 infected patients. Expert Syst Appl 160:113661
de Moraes Batista AF, Miraglia JL, Donato THR, Chiavegatto Filho ADP (2020) COVID-19 diagnosis prediction in emergency care patients: a machine learning approach, preprint on webpage at https://doi.org/10.1101/2020.04.04.20052092v2
de Freitas Barbosa VA, Gomes JC, de Santana MA, de Almeida Albuquerque JE, de Souza RG, de Souza, RE, dos Santos WP (2020) Heg. IA: an intelligent system to support diagnosis of COVID-19 based on blood tests, preprint on webpage at https://doi.org/10.1007/s42600-020-00112-5
Iwendi C, Bashir AK, Peshkar A, Sujatha R, Chatterjee JM, Pasupuleti S, Mishra R, Pillai S, Jo O (2020) COVID-19 patient health prediction using boosted random forest algorithm. Front Public Health 8:357
Mei X, Lee H-C, Diao K-Y, Huang M, Lin B, Liu C, Xie Z, Ma Y, Robson PM, Chung M et al (2020) Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med 26(8):1224–1228
Pourghasemi HR, Pouyan S, Heidari B, Farajzadeh Z, Shamsi SRF, Babaei S, Khosravi R, Etemadi M, Ghanbarian G, Farhadi A et al (2020) Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14. Int J Infect Dis 98(2020):90–108
Apostolopoulos ID, Mpesiana TA (2020) COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640
Hassanien A, Mahdy LN, Ezzat KA, Elmousalami HH, Ella HA (2020) Automatic X-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine, preprint on webpage at https://doi.org/10.1101/2020.03.30.20047787v1
Özkaya U, Öztürk Ş, Barstugan M (2020) Coronavirus (COVID-19) classification using deep features fusion and ranking technique. In: Hassanien A-E, Dey N, Elghamrawy S (eds) Big data analytics and artificial intelligence against COVID-19: innovation vision and approach. Springer, pp 281–295
Ozturk S, Ozkaya U, Barstugan M (2020) Classification of coronavirus images using shrunken features, preprint on webpage at https://doi.org/10.1101/2020.04.03.20048868v2
Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Shi J, Dai J, Cai J, Zhang T et al (2020) Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput Mater Continua 63(1):537–551
Schwab P, Schütte A, Dietz B, Bauer S (2020) PREDCOVID-19: a systematic study of clinical predictive models for coronavirus disease 2019, preprint on webpage at arXiv:2005.08302
Song F, Shi N, Liu F, Li S, Li P, Zhang W, Jiang X, Zhang Y, Sun L, Sun L et al (2020) Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19. J Clin Virol 128:104431
Toğaçar M, Ergen B, Cömert Z (2020) COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput Biol Med 121:103805
Elgendi M, Fletcher R, Howard N, Menon C, Ward R (2020) The evaluation of deep neural networks and X-ray as a practical alternative for diagnosis and management of COVID-19, preprint on webpage at https://doi.org/10.1101/2020.05.12.20099481v1
Hemdan EE-D, Shouman MA, Karar ME (2020) Covidx-net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images, preprint on webpage at arXiv:2003.11055
Loey M, Smarandache F, Khalifa NEM (2020) Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on gan and deep transfer learning. Symmetry 12(4):651
Sethy PK, Behera SK, Ratha PK, Biswas P (2020) Detection of coronavirus disease (COVID-19) based on deep features and support vector machine, preprint on webpage at https://www.preprints.org/manuscript/202003.0300/v2
Dang Q, Miao R, Yong L (2020) COVID-19 in shang hai: it is worth learning from the successful experience in preventing and controlling the overseas epidemic situation, preprint on webpage at https://doi.org/10.1101/2020.05.13.20100164v1
Rui M, Qi D, Yong L (2020) A sparse gaussian network model for prediction the growth trend of COVID-19 overseas import case: When can Hong Kong lift the international traffic blockad? Preprint on webpage at https://doi.org/10.1101/2020.05.13.20099978v1
Yang W, Zeng G, Tan B, Ju Z, Chakravorty S, He X, Chen S, Yang X, Wu Q, Yu Z et al (2020) On the generation of medical dialogues for COVID-19, preprint on webpage at arXiv:2005.05442
Tuli S, Tuli S, Tuli R, Gill SS (2020) Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet Things 11:100222
Fong SJ, Li G, Dey N, Crespo RG, Herrera-Viedma E (2020) Finding an accurate early forecasting model from small dataset: a case of 2019-ncov novel coronavirus outbreak, preprint on webpage at arXiv:2003.10776
Ribeiro MHDM, da Silva RG, Mariani VC, dos Santos Coelho L (2020) Short-term forecasting COVID-19 cumulative confirmed cases: perspectives for Brazil. Chaos Solitons Fractals 135:109853
Shi F, Xia L, Shan F, Wu D, Wei Y, Yuan H, Jiang H, Gao Y, Sui H, Shen D (2020) Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification, preprint on webpage at arXiv:2003.09860
Tang Z, Zhao W, Xie X, Zhong Z, Shi F, Liu J, Shen D (2020) Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images, preprint on webpage at arXiv:2003.11988
Avila E, Kahmann A, Alho C, Dorn M (2020) Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios. PeerJ 8:e9482
Randhawa GS, Soltysiak MP, El Roz H, de Souza CP, Hill KA, Kari L (2020) Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One 15(4):e0232391
Khanday AMUD, Rabani ST, Khan QR, Rouf N, Din MMU (2020) Machine learning based approaches for detecting COVID-19 using clinical text data. Int J Inf Technol 12(3):731–739
Kotwal A, Yadav AK, Yadav J, Kotwal J, Khune S (2020) Predictive models of COVID-19 in India: a rapid review. Med J Armed Forces India 76(4):377–386
Postnikov EB (2020) Estimation of COVID-19 dynamics “on a back-of-envelope”: does the simplest sir model provide quantitative parameters and predictions? Chaos Solitons Fractals 135:109841
Shao P, Shan Y (2020) Beware of asymptomatic transmission: study on 2019-ncov prevention and control measures based on extended Seir model, preprint on webpage at https://doi.org/10.1101/2020.01.28.923169v1
Vaid S, McAdie A, Kremer R, Khanduja V, Bhandari M (2020) Risk of a second wave of COVID-19 infections: using artificial intelligence to investigate stringency of physical distancing policies in North America. Int Orthop 44(8):1581–1589
Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ (2020) Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal 65:101794
Vaishya R, Javaid M, Khan IH, Haleem A (2020) Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr Clin Res Rev 14:337–339
Wu J, Zhang P, Zhang L, Meng W, Li J, Tong C, Li Y, Cai J, Yang Z, Zhu J et al (2020) Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results, preprint on webpage at https://doi.org/10.1101/2020.04.02.20051136v1
Yadav M, Perumal M, Srinivas M (2020) Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos Solitons Fractals 139:110050
Yesilkanat CM (2020) Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos Solitons Fractals 140:110210
Perumal V, Narayanan V, Rajasekar SJS (2020) Detection of COVID-19 using CXR and CT images using transfer learning and haralick features. Appl Intell 51:341–358
Subudhi S, Verma A, Patel AB (2020) Prognostic machine learning models for COVID-19 to facilitate decision making. Int J Clin Pract 4(12):e13685
Chowdhury R, Heng K, Shawon MSR, Goh G, Okonofua D, Ochoa-Rosales C, Gonzalez-Jaramillo V, Bhuiya A, Reidpath D, Prathapan S et al (2020) Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries. Eur J Epidemiol 35(5):389–399
Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q et al (2020) Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 296(2):E65–E71
Misra S, Jeon S, Lee S, Managuli R, Jang I-S, Kim C (2020) Multi-channel transfer learning of chest X-ray images for screening of COVID-19. Electronics 9(9):1388
Vinod DN, Prabaharan S (2020) Data science and the role of artificial intelligence in achieving the fast diagnosis of COVID-19. Chaos Solitons Fractals 140:110182
Wynants L, Van Calster B, Bonten MM, Collins GS, Debray TP, De Vos M, Haller MC, Heinze G, Moons KG, Riley RD et al (2020) Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal. BMJ 369 (8242):m1328
Yang S, Jiang L, Cao Z, Wang L, Cao J, Feng R, Zhang Z, Xue X, Shi Y, Shan F (2020) Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study. Ann Transl Med 8(7):450
Qi X, Jiang Z, Yu Q, Shao C, Zhang H, Yue H, Ma B, Wang Y, Liu C, Meng X et al (2020) Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-COV-2 infection: a multicenter study, preprint on webpage at https://doi.org/10.1101/2020.02.29.20029603v1
Yue H, Yu Q, Liu C, Huang Y, Jiang Z, Shao C, Zhang H, Ma B, Wang Y, Xie G et al (2020) Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-COV-2 infection: a multicenter study. Ann Transl Med 8(14):859
Shi W, Peng X, Liu T, Cheng Z, Lu H, Yang S, Zhang J, Li F, Wang M, Zhang X et al (2020) Deep learning-based quantitative computed tomography model in predicting the severity of COVID-19: a retrospective study in 196 patients. Lancet 9(3):216
Zivkovic M, Bacanin N, Venkatachalam K, Nayyar A, Djordjevic A, Strumberger I, Al-Turjman F (2021) COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain Cities Soc 66:102669
Feng C, Huang Z, Wang L, Chen X, Zhai Y, Zhu F, Chen H, Wang Y, Su X, Huang S et al (2020) A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics, preprint on webpage at https://doi.org/10.1101/2020.03.19.20039099v1
Yan L, Zhang H-T, Xiao Y, Wang M, Guo Y, Sun C, Tang X, Jing L, Li S, Zhang M et al (2020) Prediction of criticality in patients with severe COVID-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan, preprint on webpage at https://doi.org/10.1101/2020.02.27.20028027v2
Linda W (2020) A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. J Netw Comput Appl 20:1–12
Bandyopadhyay SK, Dutta S (2020) Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release, preprint on webpage at https://doi.org/10.1101/2020.03.25.20043505v1
Hollister M (2020) AI can help with the COVID-19 crisis-but the right human input is key. World Econ Forum 30:1–4
Muurlink OT, Stephenson P, Islam MZ, Taylor-Robinson AW (2018) Long-term predictors of dengue outbreaks in Bangladesh: a data mining approach. Infect Dis Model 3:322–330
Chenar SS, Deng Z (2018) Development of genetic programming-based model for predicting oyster norovirus outbreak risks. Water Res 128:20–37
Agarwal N, Koti SR, Saran S, Kumar AS (2018) Data mining techniques for predicting dengue outbreak in geospatial domain using weather parameters for new Delhi, India. Curr Sci 114(11):2281–2291
Koike F, Morimoto N (2018) Supervised forecasting of the range expansion of novel non-indigenous organisms: alien pest organisms and the 2009 h1n1 flu pandemic. Glob Ecol Biogeogr 27(8):991–1000
Tapak L, Hamidi O, Fathian M, Karami M (2019) Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran. BMC Res Notes 12(1):353
Anno S, Hara T, Kai H, Lee M-A, Chang Y, Oyoshi K, Mizukami Y, Tadono T (2019) Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning. Geospat Health 14(2):183–194
Liang R, Lu Y, Qu X, Su Q, Li C, Xia S, Liu Y, Zhang Q, Cao X, Chen Q et al (2020) Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data. Transbound Emerg Dis 67(2):935–946
Finkelstein J, cheol Jeong I (2017) Machine learning approaches to personalize early prediction of asthma exacerbations. Ann N Y Acad Sci 1387(1):153
Battineni G, Chintalapudi N, Amenta F (2019) Machine learning in medicine: performance calculation of dementia prediction by support vector machines (SVM). Inform Med Unlocked 16:100200
Olivera AR, Roesler V, Iochpe C, Schmidt MI, Vigo Á, Barreto SM, Duncan BB (2017) Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes-elsa-Brasil: accuracy study. Sao Paulo Med J 135(3):234–246
Chen Y, Luo Y, Huang W, Hu D, Zheng R-Q, Cong S-Z, Meng F-K, Yang H, Lin H-J, Sun Y et al (2017) Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis b. Comput Biol Med 89:18–23
Shousha HI, Awad AH, Omran DA, Elnegouly MM, Mabrouk M (2017) Data mining machine learning algorithms using il28b genotype and biochemical markers best predicted advanced liver fibrosis in chronic HCV. Jpn J Infect Dis 71(1):51–57
Zhou L-Q, Wang J-Y, Yu S-Y, Wu G-G, Wei Q, Deng Y-B, Wu X-L, Cui X-W, Dietrich CF (2019) Artificial intelligence in medical imaging of the liver. World J Gastroenterol 25(6):672
Dinga R, Marquand AF, Veltman DJ, Beekman AT, Schoevers RA, van Hemert AM, Penninx BW, Schmaal L (2018) Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. Transl Psychiatry 8(1):1–11
Pal R, Sekh AA, Kar S, Prasad DK (2020) Neural network based country wise risk prediction of COVID-19, preprint on webpage at arXiv:2004.00959
Punn NS, Sonbhadra SK, Agarwal S (2020) COVID-19 epidemic analysis using machine learning and deep learning algorithms, preprint on webpage at https://doi.org/10.1101/2020.04.08.20057679v2
Ye Y, Hou S, Fan Y, Qian Y, Zhang Y, Sun S, Peng Q, Laparo K (2020) $$\alpha $$-satellite: an AI-driven system and benchmark datasets for hierarchical community-level risk assessment to help combat COVID-19, preprint on webpage at arXiv:2003.12232
Vasileios L, Majumder MS, Elad Y-T, Edelstein M, Moura S, Yohhei H, Rangaka MX, McKendry RA, Cox IJ (2021) Tracking COVID-19 using online search. NPJ Digit Med 4(1):1–11
Heldt FS, Vizcaychipi MP, Peacock S, Cinelli M, McLachlan L, Andreotti F, Jovanović S, Dürichen R, Lipunova N, Fletcher RA et al (2021) Early risk assessment for COVID-19 patients from emergency department data using machine learning. Sci Rep 11(1):1–13
Wong ZS, Zhou J, Zhang Q (2019) Artificial intelligence for infectious disease big data analytics. Infect Dis Health 24(1):44–48
Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E (2020) Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis, preprint on webpage at arXiv:2003.05037
Pirouz B, ShaffieeHaghshenas S, ShaffieeHaghshenas S, Piro P (2020) Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (newtype of coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability 12(6):2427
Smeulders A, Van Ginneken A (1989) An analysis of pathology knowledge and decision making for the development of artificial intelligence-based consulting systems. Anal Quant Cytol Histol 11(3):154–165
McCall B (2020) COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health 2(4):e166–e167
Whitelaw S, Mamas MA, Topol E, Van Spall HGC (2020) Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digit Health 2(8):e435–e440
Booth AL, Abels E, McCaffrey P (2021) Development of a prognostic model for mortality in COVID-19 infection using machine learning. Mod Pathol 34(3):522–531
Chun A (2020) In a time of coronavirus, chinasinvestment in AI is payingoff in a bigway. https://www.scmp.com/comment/opinion/article/3075553/time-coronavirus-chinas-investment-ai-paying-big-way. Accessed 17 July 2021
Dickson B (2020) Why AI might be the most effective weapon we have to fight COVID-19. https://bit.ly/3qd0KDB. Accessed 17 July 2021
Rivas A (2020) Drones and artificial intelligence to enforce social isolation during COVID-19 outbreak. https://linkmn.gr/dOoW5O. Accessed 17 July 2021
(2020) How AI, big data and machine learning can be used against the corona virus. https://ars.electronica.art/aeblog/en/2020/03/19/ki-corona-part1/. Accessed 15 Jan 2021
Bogoch II, Watts A, Thomas-Bachli A, Huber C, Kraemer MU, Khan K (2020) Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel. J Travel Med 27(2):taaa008
BlueDot: Outbreak risk software (2020) https://bluedot.global/. Accessed 6 June 2020
HealthMap (2020) http://www.diseasedaily.org/. Accessed 22 Aug 2020
Narin A, Kaya C, Pamuk Z (2020) Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks, preprint on webpage at arXiv:2003.10849
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792
Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of xception and resnet50v2. Inform Med Unlocked 19:100360100360
Wang P, Zheng X, Li J, Zhu B (2020) Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. Chaos Solitons Fractals 139:110058
Panwar H, Gupta P, Siddiqui MK, Morales-Menendez R, Singh V (2020) Application of deep learning for fast detection of COVID-19 in X-rays using ncovnet. Chaos Solitons Fractals 138:109944
Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X et al (2020) A deep learning algorithm using CT images to screen for corona virus disease (COVID-19), preprint on webpage at https://doi.org/10.1101/2020.02.14.20023028v5
Bai X, Fang C, Zhou Y, Bai S, Liu Z, Xia L, Chen Q, Xu Y, Xia T, Gong S et al (2020) Predicting COVID-19 malignant progression with AI techniques, preprint on webpage at https://doi.org/10.1101/2020.03.20.20037325v2
Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, Chen J, Zhao H, Jie Y, Wang R et al (2020) Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images, preprint on webpage at https://doi.org/10.1101/2020.02.23.20026930v1
Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Wang X (2020) Deep learning-based detection for COVID-19 from chest CT using weak label, preprint on webpage at https://doi.org/10.1101/2020.03.12.20027185v2
Liu B, Liu P, Dai L, Yang Y, Xie P, Tan Y, Du J, Shan W, Zhao C, Zhong Q et al (2021) Assisting scalable diagnosis automatically via CT images in the combat against COVID-19. Sci Rep 11(1):1–8
Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, Chen Q, Huang S, Yang M, Yang X et al (2020) Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci Rep 10(1):1–11
Ucar F, Korkmaz D (2020) Covidiagnosis-net: Deep bayes-squeezenet based diagnostic of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 140:109761
Brunese L, Mercaldo F, Reginelli A, Santone A (2020) Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed 196:105608
Rahaman MM, Li C, Yao Y, Kulwa F, Rahman MA, Wang Q, Qi S, Kong F, Zhu X, Zhao X (2020) Identification of COVID-19 samples from chest X-ray images using deep learning: a comparison of transfer learning approaches. J X Ray Sci Technol 28(5):1–19
Rajpal S, Kumar N, Rajpal A (2020) Cov-elm classifier: an extreme learning machine based identification of COVID-19 using chest-ray images, preprint on webpage at arXiv:2007.08637
Sarkar J, Chakrabarti P (2020) A machine learning model reveals older age and delayed hospitalization as predictors of mortality in patients with COVID-19, preprint on webpage at https://doi.org/10.1101/2020.03.25.20043331v1
Du S, Wang J, Zhang H, Cui W, Kang Z, Yang T, Lou B, Chi Y, Long H, Ma M et al (2020) Predicting COVID-19 using hybrid AI model. Lancet 50(7):2891–2904
Assaf D, Gutman Y, Neuman Y, Segal G, Amit S, Gefen-Halevi S, Shilo N, Epstein A, Mor-Cohen R, Biber A et al (2020) Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med 15:1–9
Hofmarcher M, Mayr A, Rumetshofer E, Ruch P, Renz P, Schimunek J, Seidl P, Vall A, Widrich M, Hochreiter S et al (2020) Large-scale ligand-based virtual screening for SARS-COV-2 inhibitors using deep neural networks, preprint on webpage at arXiv:2004.00979
Beck BR, Shin B, Choi Y, Park S, Kang K (2020) Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-COV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J 18:784–790
Shin B, Park S, Kang K, Ho JC (2019) Self-attention based molecule representation for predicting drug-target interaction. In: Machine learning for healthcare conference. PMLR, pp 230–248
Moskal M, Beker W, Roszak R, Gajewska EP, Wołos A, Molga K, Szymkuć S, Grzybowski BA (2020) Suggestions for second-pass anti-COVID-19 drugs based on the artificial intelligence measures of molecular similarity, shape and pharmacophore distribution, preprint on webpage at https://chemrxiv.org/ndownloader/files/22217781
Hu F, Jiang J, Yin P (2020) Prediction of potential commercially inhibitors against SARS-COV-2 by multi-task deep model, preprint on webpage at arXiv:2003.00728
Kadioglu O, Saeed M, Johannes Greten H, Efferth T (2021) Identification of novel compounds against three targets of SARS COV-2 coronavirus by combined virtual screening and supervised machine learning. Bull World Health Organ 133:104359
Zhavoronkov A, Aladinskiy V, Zhebrak A, Zagribelnyy B, Terentiev V, Bezrukov D, Polykovskiy D, Shayakhmetov R, Filimonov A, Orekhov P et al (2020) Potential covid-2019 3c-like protease inhibitors designed using generative deep learning approaches. 2020. chemrxiv, preprint on webpage at https://doi.org/10.26434/chemrxiv.12301457.v1
McKee DL, Sternberg A, Stange U, Laufer S, Naujokat C (2020) Candidate drugs against SARS-COV-2 and COVID-19. Pharmacol Res 157:104859
Pan X, Dong L, Yang N, Chen D, Peng C (2020) Potential drugs for the treatment of the novel coronavirus pneumonia (COVID-19) in China. Virus Res 286:198057
Jin Z, Liu J-Y, Feng R, Ji L, Jin Z-L, Li H-B (2020) Drug treatment of coronavirus disease 2019 (COVID-19) in China. Eur J Pharmacol 883:173326
Lu H (2020) Drug treatment options for the 2019-new coronavirus (2019-ncov). Biosci Trends 14(1):69–71
Yang X, Liu Y, Liu Y, Yang Q, Wu X, Huang X, Liu H, Cai W, Ma G (2020) Medication therapy strategies for the coronavirus disease 2019 (COVID-19): recent progress and challenges. Expert Rev Clin Pharmacol 13(9):957–975
Grippo A, Assenza G, Scarpino M, Broglia L, Cilea R, Galimberti CA, Lanzo G, Michelucci R, Tassi L, Vergari M et al (2020) Electroencephalography during SARS-COV-2 outbreak: practical recommendations from the task force of the Italian society of neurophysiology (sinc), the Italian league against epilepsy (lice), and the Italian association of neurophysiology technologists (aitn). Neurol Sci 41(9):2345–2351
Hazafa A, Ur-Rahman K, Haq I-U, Jahan N, Mumtaz M, Farman M, Naeem H, Abbas F, Naeem M, Sadiqa S et al (2020) The broad-spectrum antiviral recommendations for drug discovery against COVID-19. Drug Metab Rev 52(3):408–424
Siddiqui AJ, Jahan S, Ashraf SA, Alreshidi M, Ashraf M, Patel M, Snoussi M, Singh R, Adnan M (2020) Current status and strategic possibilities on potential use of combinational drug therapy against COVID-19 caused by SARS-COV-2. J Biomol Struct Dyn 40:1–14
Khuroo MS, Khuroo M, Khuroo MS, Sofi AA, Khuroo NS (2020) COVID-19 vaccines: a race against time in the middle of death and devastation!. J Clin Exp Hepatol 10:610–621
Chen J, Li K, Zhang Z, Li K, Yu PS (2020) A survey on applications of artificial intelligence in fighting against COVID-19, preprint on webpage at arXiv:2007.02202
Qiao R, Tran NH, Shan B, Ghodsi A, Li M (2020) Personalized workflow to identify optimal t-cell epitopes for peptide-based vaccines against COVID-19, preprint on webpage at arXiv:2003.10650
Herst CV, Burkholz S, Sidney J, Sette A, Harris PE, Massey S, Brasel T, Cunha-Neto E, Rosa DS, Chao WCH et al (2020) An effective ctl peptide vaccine for ebola zaire based on survivors’ cd8+ targeting of a particular nucleocapsid protein epitope with potential implications for COVID-19 vaccine design. Vaccine 38:4464–4475
Ward D, Higgins M, Phelan J, Hibberd ML, Campino S, Clark TG (2021) An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets. bioRxiv 13(1):4
Sarkar B, Ullah MA, Johora FT, Taniya MA, Araf Y (2020) The essential facts of Wuhan novel corona virus outbreak in China and epitope-based vaccine designing against 2019-ncov, preprint on webpage at https://doi.org/10.1101/2020.02.05.935072v2
Rahman MS, Hoque MN, Islam MR, Akter S, Rubayet-Ul-Alam A, Siddique MA, Saha O, Rahaman MM, Sultana M, Crandall KA et al (2020) Epitope-based chimeric peptide vaccine design against s, m and e proteins of SARS-COV-2 etiologic agent of global pandemic COVID-19: an in silico approach. PeerJ 8:e9572
Prachar M, Justesen S, Steen-Jensen DB, Thorgrimsen SP, Jurgons E, Winther O, Bagger F (2020) COVID-19 vaccine candidates: prediction and validation of 174 SARS-COV-2 epitopes, preprint on webpage at https://doi.org/10.1101/2020.03.20.000794v4
Nguyen DD, Gao K, Wang R, Wei G (2020) Machine intelligence design of 2019-ncov drugs, preprint on webpage at https://doi.org/10.1101/2020.01.30.927889v1
Singh R, Singh R, Bhatia A (2018) Sentiment analysis using machine learning technique to predict outbreaks and epidemics. Int J Adv Sci Res 3(2):19–24
Rekha Hanumanthu S (2020) Role of intelligent computing in COVID-19 prognosis: a state-of-the-art review. Chaos Solitons Fractals 138:109947
Patel BN, Rosenberg L, Willcox G, Baltaxe D, Lyons M, Irvin J, Rajpurkar P, Amrhein T, Gupta R, Halabi S et al (2019) Human–machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digit Med 2(1):1–10
Rajendrakumar AL, Nair ATN, Nangia C, Chourasia PK, Chourasia MK, Syed MG, Nair AS, Nair AB, Koya MSF (2020) Epidemic landscape and forecasting of SARS-COV-2 in India. J Epidemiol Glob Health 11(1):55–59
Mondal S, Ghosh S (2020) Fear of exponential growth in covid19 data of India and future sketching, preprint on webpage at https://doi.org/10.1101/2020.04.09.20058933v1
Chatterjee K, Chatterjee K, Kumar A, Shankar S (2020) Healthcare impact of COVID-19 epidemic in India: a stochastic mathematical model. Med J Armed Forces India 76(2):147–155
Ghosal S, Sengupta S, Majumder M, Sinha B (2020) Linear regression analysis to predict the number of deaths in India due to SARS-COV-2 at 6 weeks from day 0 (100 cases-march 14th 2020). Diabetes Metab Syndr Clin Res Rev 14(4):311–315
Sujatha R, Chatterjee J et al (2020) A machine learning methodology for forecasting of the COVID-19 cases in India, preprint on webpage at https://doi.org/10.36227/techrxiv.12143685.v1
Singh R, Adhikari R (2020) Age-structured impact of social distancing on the COVID-19 epidemic in India, preprint on webpage at arXiv:2003.12055
Virk JS, Ali SA, Kaur G (2020) Recent update on COVID-19 in India: is locking down the country enough? Preprint on webpage at https://doi.org/10.1101/2020.04.06.20053124v2
Ranjan R (2020) Predictions for COVID-19 outbreak in India using epidemiological models, preprint on webpage at https://doi.org/10.1101/2020.04.02.20051466v1
Biswas S, Mukherjee M (2020) Risk assessment of ncovid-19 pandemic in India: a mathematical model and simulation, preprint on webpage at https://doi.org/10.1101/2020.04.10.20060830v1
DAS A, Mishra S, Gopalan SS (2020) Predicting community mortality risk due to COVID-19 using machine learning and development of a prediction tool, preprint on webpage at https://doi.org/10.1101/2020.04.27.20081794v2
Chakraborty T, Ghosh I (2020) Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: a data-driven analysis. Chaos Solitons Fractals 135:109850
Gupta R, Pal SK, Pandey G (2020) A comprehensive analysis of COVID-19 outbreak situation in India, preprint on webpage at https://doi.org/10.1101/2020.04.08.20058347v2
Ray D, Salvatore M, Bhattacharyya R, Wang L, Du J, Mohammed S, Purkayastha S, Halder A, Rix A, Barker D et al (2020) Predictions, role of interventions and effects of a historic national lockdown in India’s response to the COVID-19 pandemic: data science call to arms. Harv Data Sci Rev 2020(Suppl 1):1–45
Bhardwaj R (2020) A predictive model for the evolution of COVID-19. Trans Indian Natl Acad Eng 5:133–140
Singh S, Parmar KS, Makkhan SJS, Kaur J, Peshoria S, Kumar J (2020) Study of arima and least square support vector machine (LS-SVM) models for the prediction of SARS-COV-2 confirmed cases in the most affected countries. Chaos Solitons Fractals 139:110086
Tuan NH, Mohammadi H, Rezapour S (2020) A mathematical model for COVID-19 transmission by using the caputo fractional derivative. Chaos Solitons Fractals 140:110107
Arti M, Bhatnagar K (2020) Modeling and predictions for COVID 19 spread in India, preprint on webpage at https://doi.org/10.13140/RG.2.2.11427.81444
Rai B, Shukla A, Dwivedi LK (2020) COVID-19 in India: predictions, reproduction number and public health preparedness, preprint on webpage at https://doi.org/10.1101/2020.04.09.20059261v1
Croccolo F, Roman HE (2020) Spreading of infections on random graphs: a percolation-type model for COVID-19. Chaos Solitons Fractals 139:110077
Shuja J, Alanazi E, Alasmary W, Alashaikh A (2020) COVID-19 open source data sets: a comprehensive survey. Appl Intell 51:1296–1325
Xu B, Kraemer MU, Gutierrez B, Mekaru S, Sewalk K, Loskill A, Wang L, Cohn E, Hill S, Zarebski A et al (2020) Open access epidemiological data from the COVID-19 outbreak. Lancet Infect Dis 20(5):534
Frazer JS, Shard A, Herdman J (2020) Involvement of the open-source community in combating the worldwide COVID-19 pandemic: a review. J Med Eng Technol 44(4):169–176
Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X (2020) Artificial intelligence and machine learning to fight COVID-19. Physiol Genom 54(4):200–202
Pham Q-V, Nguyen DC, Hwang W-J, Pathirana PN et al (2020) Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts, preprint on webpage at https://www.preprints.org/manuscript/202004.0383/v1
Textual Data Set T1 (2020) https://datahub.io/core/covid-19. Accessed 15 Aug 2020
Medical Data Set M1 (2020) https://ai.nscc-tj.cn/thai/deploy/public/pneumonia_ct. Accessed 15 Aug 2020
Textual Data Set T2 (2020) https://github.com/CSSEGISandData/COVID-19. Accessed 15 Aug 2020
Medical Data Set M2 (2020) https://zenodo.org/record/3757476. Accessed 15 Aug 2020
Textual Data Set T3 (2020) https://ncov.dxy.cn/ncovh5/view/pneumonia. Accessed 15 Aug 2020
Medical Data Set M3 (2020) https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark. Accessed 15 Aug 2020
Textual Data Set T4 (2020) https://www.arcgis.com/apps/opsdashboard/index.html. Accessed 15 Aug 2020
Medical Data Set M4 (2020) http://medicalsegmentation.com/covid19/. Accessed 15 Aug 2020
Textual Data Set T5 (2020) https://www.kaggle.com/covid-19-contributions. Accessed 15 Aug 2020
Medical Data Set M5 (2020) https://www.sirm.org/en/category/articles/covid-19-database/. Accessed 15 Aug 2020
Textual Data Set T6 (2020) https://github.com/WeileiZeng/Open-Source-COVID-19. Accessed 15 Aug 2020
Medical Data Set M6 (2020) https://coronacases.org/. Accessed 15 Aug 2020
Textual Data Set T7 (2020) https://dataverse.harvard.edu/dataverse/2019ncov. Accessed 15 Aug 2020
Medical Data Set M7 (2020) https://www.bsti.org.uk/training-and-education/covid-19-bsti-imaging-database/. Accessed 15 Aug 2020
Textual Data Set T8 (2020) https://www.kaggle.com/lachmann12/world-population-demographics-by-age-2019. Accessed 15 Aug 2020
Medical Data Set M8 (2020) https://www.sirm.org/en/category/articles/covid-19-database/. Accessed 15 Aug 2020
Textual Data Set T9 (2020) https://github.com/Emergent-Epidemics/covid19_npi_china. Accessed 15 Aug 2020
Medical Data Set M9 (2020) https://radiopaedia.org/articles/covid-19-3. Accessed 15 Aug 2020
Textual Data Set T10 (2020) https://www.ecdc.europa.eu/en/covid-19-pandemic. Accessed 15 Aug 2020
Textual Data Set T11 (2020) https://github.com/BayesForDays/coronada. Accessed 15 Aug 2020
Textual Data Set T12 (2020) https://www.kaggle.com/smid80/coronavirus-covid19-tweets. Accessed 15 Aug 2020
Speech Data Set S1 (2020) https://coswara.iisc.ac.in/. Accessed 15 Aug 2020
Textual Data Set T13 (2020) https://covidscholar.org. Accessed 15 Aug 2020
Speech Data Set S2 (2020) https://github.com/iiscleap/Coswara-Data. Accessed 15 Aug 2020
Textual Data Set T14 (2020) https://www.kaggle.com/chaibapat/google-mobility. Accessed 15 Aug 2020
Speech Data Set S3 (2020) https://www.covid-19-sounds.org/en/. Accessed 15 Aug 2020
Textual Data Set T15 (2020) https://www.apple.com/covid19/mobility. Accessed 15 Aug 2020
Speech Data Set S4 (2020) https://cvd.lti.cmu.edu/. Accessed 15 Aug 2020
Textual Data Set T16 (2020) https://geods.geography.wisc.edu/covid19/physical-distancing/. Accessed 15 Aug 2020
Speech Data Set S5 (2020) https://coughvid.epfl.ch/. Accessed 15 Aug 2020
Textual Data Set T17 (2020) http://qianxi.baidu.com/. Accessed 15 Aug 2020
Speech Data Set S6 (2020) http://virufy.org/. Accessed 15 Aug 2020
Textual Data Set T18 (2020) https://www.google.com/covid19/mobility/. Accessed 15 Aug 2020
Speech Data Set S7 (2020) https://github.com/virufy/covid. Accessed 15 Aug 2020
Xu B, Gutierrez B, Mekaru S, Sewalk K, Goodwin L, Loskill A, Cohn EL, Hswen Y, Hill SC, Cobo MM et al (2020) Epidemiological data from the COVID-19 outbreak, real-time case information. Sci Data 7(1):1–6
Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, Eggo RM, Sun F, Jit M, Munday JD et al (2020) Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 20(5):553–558
Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M (2020) Application of the arima model on the COVID-2019 epidemic dataset. Data Brief 29:105340
Lachmann A (2020) Correcting under-reported COVID-19 case numbers: estimating the true scale of the pandemic, preprint on webpage at https://doi.org/10.1101/2020.03.14.20036178v2
Obeid JS, Davis M, Turner M, Meystre SM, Heider PM, O’Bryan EC, Lenert LA (2020) An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: a case report. J Am Med Inform Assoc 27(8):1321–1325
Kasilingam D, Prabhakaran SS, Dinesh Kumar R, Rajagopal V, Santhosh Kumar T, Soundararaj A (2020) Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning. Transbound Emerg Dis 68(3):1001–1018
Zheng N, Du S, Wang J, Zhang H, Cui W, Kang Z, Yang T, Lou B, Chi Y, Long H et al (2020) Predicting COVID-19 in china using hybrid AI model. IEEE Trans Cybern 50(7):2891–2894
Kraemer MU, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM, Du Plessis L, Faria NR, Li R, Hanage WP et al (2020) The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368(6490):493–497
Anzai A, Kobayashi T, Linton NM, Kinoshita R, Hayashi K, Suzuki A, Yang Y, Jung S-M, Miyama T, Akhmetzhanov AR et al (2020) Assessing the impact of reduced travel on export at ion dynamics of novel coronavirus infection (COVID-19). J Clin Med 9(2):601
Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, Wesolowski A, Santillana M, Zhang C, Du X et al (2020) Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China. medRxiv 585(7825):410–413
Wells CR, Sah P, Moghadas SM, Pandey A, Shoukat A, Wang Y, Wang Z, Meyers LA, Singer BH, Galvani AP (2020) Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak. Proc Natl Acad Sci 117(13):7504–7509
Tian H, Liu Y, Li Y, Wu C-H, Chen B, Kraemer MU, Li B, Cai J, Xu B, Yang Q et al (2020) An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in china. Science 368(6491):638–642
Kleinberg B, van der Vegt I, Mozes M (2020) Measuring emotions in the COVID-19 real world worry dataset, preprint on webpage at arXiv:2004.04225
Banda JM, Tekumalla R, Wang G, Yu J, Liu T, Ding Y, Artemova K, Tutubalina E, Chowell G (2020) A large-scale COVID-19 twitter chatter dataset for open scientific research—an international collaboration, preprint on webpage at arXiv:2004.03688
Covid-19: The first public coronavirus twitter dataset (2020) https://github.com/echen102/COVID-19-TweetIDs. Accessed 08 Jan 2021
Alqurashi S, Alhindi A, Alanazi E (2020) Large arabic twitter dataset on COVID-19, preprint on webpage at arXiv:2004.04315
Yu J (2020) Open access institutional and news media tweet dataset for COVID-19 social science research, preprint on webpage at arXiv:2004.01791
Zarei K, Farahbakhsh R, Crespi N, Tyson G (2020) A first instagram dataset on COVID-19, preprint on webpage at arXiv:2004.12226
Sarker A, Lakamana S, Hogg-Bremer W, Xie A, Al-Garadi MA, Yang Y-C (2020) Self-reported COVID-19 symptoms on twitter: an analysis and a research resource. J Am Med Inform Assoc 27(8):1310–1315
Ahamed S, Samad M (2020) Information mining for COVID-19 research from a large volume of scientific literature, preprint on webpage at arXiv:2004.02085
Fister I Jr, Fister K, Fister I (2020) Discovering associations in COVID-19 related research papers, preprint on webpage at arXiv:2004.03397
Adhikari SP, Meng S, Wu Y-J, Mao Y-P, Ye R-X, Wang Q-Z, Sun C, Sylvia S, Rozelle S, Raat H et al (2020) Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infect Dis Poverty 9(1):1–12
Arksey H, O’Malley L (2005) Scoping studies: towards a methodological framework. Int J Soc Res Methodol 8(1):19–32
Moons KG, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S (2019) Probast: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 170(1):W1–W33
Chen E, Lerman K, Ferrara E (2020) Tracking social media discourse about the COVID-19 pandemic: development of a public coronavirus twitter data set. JMIR Public Health Surveill 6(2):e19273
Alamo T, Reina DG, Mammarella M, Abella A (2020) Open data resources for fighting COVID-19, preprint on webpage at arXiv:2004.06111
Cohen JP, Bertin P, Frappier V (2019) Chester: a web delivered locally computed chest X-ray disease prediction system, preprint on webpage at arXiv:1901.11210
Zhao J, Zhang Y, He X, Xie P (2020) COVID-CT-dataset: a CT scan dataset about COVID-19, preprint on webpage at https://covid-19.conacyt.mx/jspui/handle/1000/4157
Khan SH, Sohail A, Zafar M, Khan A (2020) Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network, preprint on webpage at https://doi.org/10.13140/RG.2.2.35868.64646
Savadjiev P, Chong J, Dohan A, Vakalopoulou M, Reinhold C, Paragios N, Gallix B (2019) Demystification of AI-driven medical image interpretation: past, present and future. Eur Radiol 29(3):1616–1624
Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shi Y (2020) Lung infection quantification of COVID-19 in CT images with deep learning, preprint on webpage at arXiv:2003.04655
Jun M, Cheng G, Yixin W, Xingle A, Jiantao G, Ziqi Y, Minqing Z, Xin L, Xueyuan D, Shucheng C, et al. (2020) COVID-19 CT lung and infection segmentation dataset. https://doi.org/10.5281/zenodo.3757476
Ma J, Wang Y, An X, Ge C, Yu Z, Chen J, Zhu Q, Dong G, He J, He Z, Cao T, Zhu Y, Nie Z, Yang X (2021) Toward data-efficient learning: a benchmark for COVID-19 CT lung and infection segmentation. Med Phys 48(3):1197–1210
Rajinikanth V, Dey N, Raj ANJ, Hassanien A, Santosh K, Raja N (2020) Harmony-search and otsu based system for coronavirus disease (COVID-19) detection using lung CT scan images, preprint on webpage at arXiv:2004.03431
Apostolopoulos ID, Aznaouridis SI, Tzani MA (2020) Extracting possibly representative COVID-19 biomarkers from x-ray images with deep learning approach and image data related to pulmonary diseases. J Med Biol Eng 40:462–469
Lin ZQ, Shafiee M, Bochkarev S, Jules MS, Wang X, Wong A (2019) Explaining with impact: a machine-centric strategy to quantify the performance of explain ability algorithms, preprint on webpage at https://doi.org/10.1101/2020.05.10.20097063v1
Wang L, Lin ZQ, Wong A (2020) COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10(1):1–12
Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131
Born J, Brändle G, Cossio M, Disdier M, Goulet J, Roulin J, Wiedemann N (2020) Pocovid-net: automatic detection of COVID-19 from a new lung ultrasound imaging dataset (pocus), preprint on webpage at arXiv:2004.12084
Sharma A, Rani S, Gupta D (2020) Artificial intelligence-based classification of chest X-ray images into COVID-19 and other infectious diseases. Int J Biomed Imaging 2020:1–10
Imran A, Posokhova I, Qureshi HN, Masood U, Riaz S, Ali K, John CN, Hussain I, Nabeel M (2020) Ai4covid-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked 20:100378
Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, Xia T, Cicuta P, Mascolo C (2020) Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data, preprint on webpage at arXiv:2006.05919
Sharma N, Krishnan P, Kumar R, Ramoji S, Chetupalli SR, Ghosh PK, Ganapathy S et al (2020) Coswara—a database of breathing, cough, and voice sounds for COVID-19 diagnosis, preprint on webpage at arXiv:2005.10548
Faezipour M, Abuzneid A (2020) Smartphone-based self-testing of COVID-19 using breathing sounds. Telemed e-Health 26(10):1202–1205
Trivedy S, Goyal M, Mohapatra PR, Mukherjee A (2020) Design and development of smartphone-enabled spirometer with a disease classification system using convolutional neural network. IEEE Trans Instrum Meas 69(9):7125–7135
Han J, Qian K, Song M, Yang Z, Ren Z, Liu S, Liu J, Zheng H, Ji W, Koike T et al (2020) An early study on intelligent analysis of speech under COVID-19: Severity, sleep quality, fatigue, and anxiety, preprint on webpage at arXiv:2005.00096
Yao H, Zhang N, Zhang R, Duan M, Xie T, Pan J, Peng E, Huang J, Zhang Y, Xu X et al (2020) Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests. Front Cell Dev Biol 8:683
Kim AW, Adam EK, Bechayda SA, Kuzawa CW (2020) Early life stress and HPA axis function independently predict adult depressive symptoms in metropolitan Cebu, Philippines. Am J Phys Anthropol 173(3):448–462
Kim AW, Nyengerai T, Mendenhall E (2020) Evaluating the mental health impacts of the COVID-19 pandemic in urban South Africa: perceived risk of COVID-19 infection and childhood trauma predict adult depressive symptoms, preprint on webpage at https://doi.org/10.1101/2020.06.13.20130120v1
Nour M, Cömert Z, Polat K (2020) A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization. Appl Soft Comput 97:106580
Heidari M, Mirniaharikandehei S, Khuzani AZ, Danala G, Qiu Y, Zheng B (2020) Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int J Med Inform 144:104284
Farid AA, Selim GI, Awad H, Khater A (2020) A novel approach of CT images feature analysis and prediction to screen for corona virus disease (COVID-19). Int J Sci Eng Res 11(3):1–9
Mbuvha R, Marwala T (2020) Bayesian inference of COVID-19 spreading rates in South Africa. medRxiv 15(8):e0237126
Lai C-C, Hsu C-Y, Jen H-H, Yen M-F, Chan C-C, Chen H-H (2020) Bayesian approach for modelling the dynamic of COVID-19 outbreak on the diamond princess cruise ship, preprint on webpage at https://doi.org/10.1101/2020.06.21.20136465v1
Karmakar S, Das S (2020) Evaluating the impact of COVID-19 on cyberbullying through Bayesian trend analysis. In: Proceedings of the European interdisciplinary cybersecurity conference (EICC) co-located with European Cyber Week. pp 1–6
Campbell F, Cori A, Ferguson N, Jombart T (2019) Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data. PLoS Comput Biol 15(3):e1006930
Jewell CP, Kypraios T, Neal P, Roberts GO et al (2009) Bayesian analysis for emerging infectious diseases. Bayesian Anal 4(3):465–496
Franco-Villoria M, Ventrucci M, Rue H et al (2019) A unified view on Bayesian varying coefficient models. Electron J Stat 13(2):5334–5359
Albahri AS, Hamid RA et al (2020) Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review. J Med Syst 44(7):122
Medel-Ramírez C, Medel-Lopez H (2020) Data mining for the study of the epidemic (SARS-COV-2) COVID-19: algorithm for the identification of patients (SARS-COV-2) COVID 19 in Mexico. Available at SSRN 3619549Preprint on webpage at https://doi.org/10.2139/ssrn.3619549
Kumar S (2020) Monitoring novel corona virus (COVID-19) infections in India by cluster analysis. Ann Data Sci 7(3):417–425
Ding Z, Qin Z, Qin Z (2017) Frequent symptom sets identification from uncertain medical data in differentially private way. Sci Program 2017:1–10
Gurwitz D (2020) Repurposing current therapeutics for treating COVID-19: a vital role of prescription records data mining. Drug Dev Res 81:777–781
Wahbeh A, Nasralah T, Al-Ramahi M, El-Gayar O (2020) Mining physicians’ opinions on social media to obtain insights into COVID-19: mixed methods analysis. JMIR Public Health Surveill 6(2):e19276
Liu J, Zhou J, Yao J, Zhang X, Li L, Xu X, He X, Wang B, Fu S, Niu T et al (2020) Impact of meteorological factors on the COVID-19 transmission: a multi-city study in China. Sci Total Environ 726:138513
Wang J, Tang K, Feng K, Lv Wf (2020) Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in china and the united states, preprint on webpage at https://doi.org/10.2139/ssrn.3551767
Fang Y, Nie Y, Penny M (2020) Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: a data-driven analysis. J Med Virol 92(6):645–659
Rodriguez-Diaz CE, Guilamo-Ramos V, Mena L, Hall E, Honermann B, Crowley JS, Baral S, Prado GJ, Marzan-Rodriguez M, Beyrer C et al (2020) Risk for COVID-19 infection and death among latinos in the united states: examining heterogeneity in transmission dynamics. Ann Epidemiol 52:46–53
Jaiswal A, Gianchandani N, Singh D, Kumar V, Kaur M (2020) Classification of the COVID-19 infected patients using densenet201 based deep transfer learning. J Biomol Struct Dyn 40:1–8
Tindale L, Coombe M, Stockdale JE, Garlock E, Lau WYV, Saraswat M, Lee Y-HB, Zhang L, Chen D, Wallinga J et al (2020) Transmission interval estimates suggest pre-symptomatic spread of COVID-19, preprint on webpage at https://doi.org/10.1101/2020.03.03.20029983v1
Nishiura H, Linton NM, Akhmetzhanov AR (2020) Serial interval of novel coronavirus (COVID-19) infections. Int J Infect Dis 93:284–286
Lopez CE, Vasu M, Gallemore C (2020) Understanding the perception of COVID-19 policies by mining a multilanguage twitter dataset, preprint on webpage at arXiv:2003.10359