A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media
Abstract
:1. Introduction
2. Background and Motivation
2.1. Pandemics in the Last Century
2.1.1. Spanish Flu Pandemic (1918–1919)
2.1.2. Asian Flu Pandemic (1957–1958)
2.1.3. Hong Kong Flu Pandemic (1968–1969)
2.1.4. Severe Acute Respiratory Syndrome (SARS/2002–2004)
2.1.5. COVID-19 (2019–Present)
2.2. Comparison of Existing Research
3. Major Components of the COVID-19 Taxonomy
3.1. Digital Activism
3.2. Artificial Intelligence and Machine Learning (ML)
3.3. Social Network Analysis (SNA)
3.4. Social Media
4. Digital Activism and Social Media Conspiracies
4.1. Digital Activism
4.1.1. Online Office Activities
4.1.2. Online Education System
4.2. Social Media
4.2.1. Social Media Search Indexes (SMSI)
4.2.2. Misinformation on Social Media
4.2.3. Impact of Rumors about COVID-19
4.2.4. Collection and Publication of Datasets of Social Media
5. Artificial Intelligence and Machine Learning (ML)
5.1. Forecasting and Identification of Pandemic
5.2. Application of AI in COVID-19
5.2.1. AI-Powered Diagnosis of COVID-19
5.2.2. CNN for COVID-19 Screening Using Chest X-ray
5.3. Blockchain and AI for COVID-19 Self-Testing
5.4. Application of Internet of Things in COVID-19
6. Social Network Analysis (SNA)
6.1. Social Network Analysis for Twitter Data
6.2. Effects of Social Grooming on Incivility
6.3. Economics and Social Consequences
7. Discussion and Future Directions
7.1. Findings of the Research
7.2. Future Work
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Disease | Spanish Flu | Asian Flu | Hong Kong Flu | SARS | COVID-19 |
Causative Agent | H1N1 | H2N2 | H3N2 | SARS-CoV | SARS-CoV-2 |
Years(s) | 1918–1919 | 1957–1958 | 1968–1969 | 2002–2004 | 2019–Present |
Death Numbers | Approx. 50 Million | Approx. 1.1 Million | Approx. 1 Million | 774 | 6.59 M (till 11-02-2022) |
Classification | Pandemic | Pandemic | Pandemic | Outbreak | Pandemic |
Ref. No. | Artificial Intelligence | Machine Learning | Deep Learning | Internet of Things | Blockchain | Social Network Analysis | Impact of Social Media |
---|---|---|---|---|---|---|---|
[8] | ✓ | × | × | × | × | × | × |
[9] | ✓ | ✓ | × | × | × | × | × |
[10] | ✓ | × | × | ✓ | × | × | × |
[11] | × | × | × | ✓ | × | × | × |
[12] | ✓ | ✓ | × | × | × | × | × |
[13] | × | × | × | × | ✓ | × | × |
[14] | ✓ | ✓ | ✓ | × | × | × | × |
[15] | × | × | ✓ | × | × | × | × |
[16] | × | × | × | ✓ | × | × | × |
[17] | × | ✓ | × | × | × | × | ✓ |
[18] | × | × | × | × | × | ✓ | × |
[19] | × | × | × | × | × | × | ✓ |
[20] | ✓ | ✓ | × | × | × | × | × |
[21] | ✓ | × | × | × | × | × | × |
Our Survey | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Ref. No. | Technique | Models | Outperforming Model |
---|---|---|---|
[56] | Machine Learning | SVM, LESSO, LR, ES | ES (exponential smoothing) |
[57] | Machine Learning | LR and PR | PR (polynomial regression) |
[58] | Hybrid AI-based | ISI with NLP and LSTM | - |
[59] | Machine Learning | LR, VAR, MLP | MLP (multi-linear perceptron) |
[60] | Machine Learning | Linear Model, SVM, RF, DT, NN | RF (random forest) |
[66] | Hybrid | MLP-ICA, ANFIS | - |
[69] | Hybrid | SVM, LR, NB | NB (Naive Bayes) |
Ref. No. | Model | Dataset Size | Accuracy |
---|---|---|---|
[74] | CovidX-Net Family | 50 Chest X-ray Images | 90% |
[78] | CNN | 106 Chest X-ray Images | 96.3% |
[80] | ML Classification | - | 96% |
[83] | COVID-Net | 13,975 X-ray Images | 93.3% |
[87] | DL-CRC | - | 94.61% |
[88] | ResNet Family | 3 different datasets | 99% |
[90] | InceptionV3 | 260 X-ray Images | 100% |
[91] | Full 3D, Hybrid 3D | 1280 Patients Images | 90% |
[92] | U-Net AI model | 2447 Images and 2120 Images Datasets | 92.3% |
Ref. No. | Location | Database API | Database Size/Collection | SNA Technique |
---|---|---|---|---|
[25] | Global | - | Semantic Network Analysis | |
[111] | Global | 310 Million Tweets | Social Network Analysis, Sentiment Analysis | |
[112] | United Kingdom | 27 March 2020–4 April 2020 | Content Analysis, Social Network Analysis | |
[115] | South Korea | 9958 Tweets | Social Network Analysis | |
[121] | China | - | 1212 Patients | Statistical Analysis, Social Network Analysis |
[122] | USA, Canada | - | 3075 People | Network Analysis |
Ref. No. | Purpose | Technique |
---|---|---|
[55] | Forecasting of COVID-19 | Combination of machine learning techniques |
[56] | Forecasting and identification of pandemic | Exponential smoothing |
[57] | Analysis and prediction of COVID-19 in India | Polynomial regression |
[58] | Prediction of COVID-19 | Hybrid-AI-based model |
[60] | Detection of CoVID-19 active, deaths and recover cases in India | Linear model, SVM, decision tree, random forest and neural network |
[65] | Prediction and reporting of COVID-19 cases | Machnie learning algorithms |
[66] | Prediction of COVID-19 outbreak | MLP and ANFIS |
[68] | Forecasting of COVID-19 patients | Random forest algorithm |
[70] | Diagnosis of COVID-19 | AI-based models (RNN, GAN, LSTM, ELM) |
[74] | COVID-19 patients diagnosis | COVIDXNet architecture |
[75] | Detection of COVID-19 and forecasting of disease | AI-based smartphone sensor |
[80] | Classification of the COVID-19 test results using joint analysis | Developed two classification models based on ML |
[81] | Improving PCR test results | CNN model (Mobile Net) |
[82] | Lungs infection segmentation | Deep learning techniques |
[86] | Classification of disease | Patch-based CNN |
[87] | Classification of COVID-19 | Deep learning Chest Radiographic Classifier (DL-CRC) |
[88] | Classification of COVID-19 | ResNet-50 |
[89] | Detection of COVID-19 | CNN-ACGAN |
[90] | Detection of COVID-19 | CNN model (InceptionV3) |
[91] | Prediction of COVID-19 patients | Full 3D model and hybrid 3D model |
[92] | Screening of COVID-19 patients | U-net based model |
[94] | Prediction of COVID-19 | DenseNet121, deep learning neural feature extractor with bagging tree classifier |
[96] | Developed self-testing smartphone application (xRCovid) | ML-classifier-based application to classify serological RDT results |
[114] | Identification of fake and not-fake tweets on Twitter | Logistic regression, naive Bayes, decision tree, SVM, random forest, K-nearest neighbors |
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Butt, M.J.; Malik, A.K.; Qamar, N.; Yar, S.; Malik, A.J.; Rauf, U. A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media. Sensors 2023, 23, 5543. https://doi.org/10.3390/s23125543
Butt MJ, Malik AK, Qamar N, Yar S, Malik AJ, Rauf U. A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media. Sensors. 2023; 23(12):5543. https://doi.org/10.3390/s23125543
Chicago/Turabian StyleButt, Muhammad Junaid, Ahmad Kamran Malik, Nafees Qamar, Samad Yar, Arif Jamal Malik, and Usman Rauf. 2023. "A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media" Sensors 23, no. 12: 5543. https://doi.org/10.3390/s23125543
APA StyleButt, M. J., Malik, A. K., Qamar, N., Yar, S., Malik, A. J., & Rauf, U. (2023). A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media. Sensors, 23(12), 5543. https://doi.org/10.3390/s23125543