Data Analytics for the COVID-19 Epidemic

Wang, Ranran; Hu, Gang; Jiang, Chi; Lu, Huimin; Zhang, Yin

doi:10.1109/compsac48688.2020.00-83

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…Various studies related to virus transmission and its influence factors have been carried out to predict (a) the spread of the virus [29,30,31,32,33]; (b) the person suspected of being infected [34]; (c) new infection areas [35]; (d) the likelihood of the second and third waves of the epidemic [36]; (e) COVID-19 contamination scenario based on people movement [37]; and (f) the increased number of cases [38]. COVID-19 task-force stakeholders can use these epidemiological predictions to prepare the necessary measures and policies.…”

Section: Healthcarementioning

confidence: 99%

“…Research topics in social psychology covered in the past studies include the relationship between trust and the presence of infectious disease [70]; psychological needs and their satisfaction level during the pandemic [71]; the effect of fear and collectivism on the public prevention against COVID-19 [72]; peoples' preferences to protect the environment they live in [73]. Some of the harmful effects of the pandemic have been studied, including family violence [21], increasing racial sentiment toward Asian people [23], the emergence of incivility and fake news on social media [74,29], and emotional tendency and emotional symptoms of mental disorder facing the outbreak [75,76].…”

Section: Social Lifementioning

confidence: 99%

“…The prediction of the epidemic situation based on COVID-19 data was carried out by Wang R et al [29] by comparing several models. The optimized SIR model uses the least square and particle swarm optimization method and the classical logistic regression model.…”

Section: Sir Mathematical Modelmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

Riswantini

Nugraheni

Munandar

et al. 2021

Preprint

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COVID-19 has induced many problems in various sectors of life for humanity around the world. After one year of pandemic, many studies have been carried out in exposing various technology innovations and applications to combat the coronavirus that has killed more people than most. The pandemic has accelerated the use of Big Data technology to mitigate the threats of COVID-19. This survey aims to explore the Big Data research for COVID-19. We collected and analyzed the relevant academic articles to identify how Big Data technology can cover the challenges faced in overcoming the pandemic. In determining the research areas addressed by the past studies, we highlight the technology contributions to five major areas of healthcare, social life, government policy, business and management, and the environment. We discuss how analytical techniques of machine learning, deep learning, statistics, and mathematics can solve pandemic issues. The Big Data research for COVID-19 used a wide variety of data sources available publicly or privately. At the end of the discussion, we present the data source used in the past studies encompassing government official data, institutional service data, IoT generated data, online media data, and open data. We hope that this survey will clarify the role of Big Data technology in enhancing the research for COVID-19.

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Section: Healthcarementioning

confidence: 99%

Section: Social Lifementioning

confidence: 99%

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A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

Riswantini

Nugraheni

Munandar

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, the COVID‐19 pandemic‐related news is becoming viral on social media sites (Pastor, 2020). The use of social media platforms like Weibo, Twitter, Reddit, Facebook, WeChat, and Instagram and others, aids in understanding public sentiment and maintaining social order during the pandemic by analysing relevant data to assess public sentiment, detect misleading information and so on (Wang, Hu, et al, 2020). When people use online networks, they believe to have more freedom of expression and speech than they have in the real world, where manners and social etiquette can feel restrictive at times.…”

Section: Introductionmentioning

confidence: 99%

Hybrid evolutionary intelligent network for sentiment analysis using Twitter data during COVID‐19 pandemic

Kour,

Gupta

2023

Expert Systems

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COVID‐19 pandemic has impacted many nations, causing physical as well as mental health concerns globally. In most countries, governments enforced strict lockdowns and social distancing, thus affecting people's daily lives. People usually tweet their views on online platforms that is unstructured text with implicit meaning. With the evolution of artificial intelligence in the natural language processing domain, the prediction of sentiments accurately has become a challenge. To contribute as a solution to this, a hybrid approach is proposed for sentiment prediction with the use of an evolutionary‐based approach, transfer‐based learning and machine learning. The proposed approach uses bidirectional encoder representations from transformers (BERT) with genetic algorithm (GA) and support vector machine (SVM), namely, hybrid evolutionary intelligent model (GA‐BERT‐SVM). These approaches aid in extracting important features considering semantics and context present in the text. To avoid the limitations of the backpropagation approach, such as trapping in local minima and overfitting the data, the initial parameters (weights and biases) of the dense layers has been optimized using GA. Additionally, the pretrained BERT layers are utilized without any modification, following a standard transfer learning approach. The BERT embeddings are concatenated with the SVM for training and classification. GridSearchCV and GeneticSearchCV is used for obtaining optimal parameters of SVM. A multi‐classification problem is tackled using a benchmark COVID‐19 dataset, which comprises of Twitter data and is categorized into COVIDSENTI‐A, COVIDSENTI‐B, COVIDSENTI‐C and a combined dataset called COVIDSENTI. Experimental evaluation demonstrates promising results of the proposed model in terms of accuracy, F1‐score, precision and recall, surpassing state‐of‐the‐art approaches.

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“…Coronavirus disease (COVID-19) was first detected on December 8, 2019, in the city of Wuhan, China [2]. Given the fact that Coronavirus (nCoV) is a variant of the Coronavirus that has not been observed yet, containing the outbreak was nearly impossible [3,4].…”

Section: -Introductionmentioning

confidence: 99%

Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

et al. 2022

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The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively. Doi: 10.28991/ESJ-2023-SPER-02 Full Text: PDF

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Data Analytics for the COVID-19 Epidemic

Cited by 12 publications

References 7 publications

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

Hybrid evolutionary intelligent network for sentiment analysis using Twitter data during COVID‐19 pandemic

Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

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