Applications of Big Data Analytics to Control COVID-19 Pandemic

Alsunaidi, Shikah J.; Almuhaideb, Abdullah M.; Ibrahim, N. S.; Shaikh, Fatema; Alqudaihi, Kawther S.; Alhaidari, Fahd; Khan, Irfan Ullah; Aslam, Nida; Alshahrani, Mohammed

doi:10.3390/s21072282

Cited by 100 publications

(55 citation statements)

References 78 publications

(151 reference statements)

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“…It is critical to use modern technology to combat respiratory infections in order to provide more convenient service and avoid disease outbreaks in the future. Big data and AI have demonstrated a high level of capability in disease control and diagnosis [97], [98]. The devices will gain a better understanding of virus structure and disease spread by combining both technologies.…”

Section: ) Development Of New Ai Algorithmsmentioning

confidence: 99%

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

et al. 2021

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Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques. INDEX TERMSArtificial intelligence (AI), cough detection, 2019 novel coronavirus disease (Covid-19), respiratory illness diagnosis, cough-based diagnosis.

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Section: ) Development Of New Ai Algorithmsmentioning

confidence: 99%

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

et al. 2021

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“…Regional distribution and healthcare consumption per region, prediction of the pandemic's course was also analysed, among others. 45 Wu et al have discussed the utilization of Big Data technology in China when the COVID-19 pandemic broke in Wuhan to contain and control COVID-19. They identified some difficulties such as low efficiency of data collection and data quality, inefficient use of data limitations in data sharing and privacy concerns.…”

Section: Big Datamentioning

confidence: 99%

A review of emerging innovations in COVID-19

Saggi¹

2021

Int J Community Med Public Health

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The COVID-19 pandemic has globally impacted humanity. Human health, productivity, social life and function is affected. Every country has felt effects, domestic as well as international. Emerging technologies also known as disruptive technologies have played a significant role in the pandemic. This literature review is a manifest overview on the utilization of existing technologies during the COVID-19 pandemic. The strengths, weakness, opportunities and threats of the innovative technologies under review have been summarized. Their benefits and further scope of disruptive innovations have been reviewed. The review aims to identify and highlight approaches/gaps for improvement and future application.

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“…In a word, these spatiotemporal geographic data can represent the characteristics of epidemic risk in urban space, providing a new research perspective and solution to problems related to epidemic risks in relation to urban geography (Bachir et al, 2019;Sharifi and Khavarian-Garmsir, 2020). Compared with statistical survey data about the epidemic, spatiotemporal geographic epidemiological data have spatiotemporal continuity, and their strong data volume, analysis and processing mode, display capability and other advantages greatly compensate for the insufficient amount of statistical survey data in research on epidemic analysis (Silva et al, 2018 ;Alsunaidi et al, 2021). Therefore, spatiotemporal geographic epidemiological data can play an important auxiliary role in assessing and simulating COVID-19 risk (Hu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

Zhou

Wang

et al. 2021

Front. Environ. Sci.

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COVID-19 is a highly infectious disease and public health hazard that has been wreaking havoc around the world; thus, assessing and simulating the risk of the current pandemic is crucial to its management and prevention. The severe situation of COVID-19 around the world cannot be ignored, and there are signs of a second outbreak; therefore, the accurate assessment and prediction of COVID-19 risks, as well as the prevention and control of COVID-19, will remain the top priority of major public health agencies for the foreseeable future. In this study, the risk of the epidemic in Guangzhou was first assessed through logistic regression (LR) on the basis of Tencent-migration data and urban point of interest (POI) data, and then the regional distribution of high- and low-risk epidemic outbreaks in Guangzhou in February 2021 was predicted. The main factors affecting the distribution of the epidemic were also analyzed by using geographical detectors. The results show that the number of cases mainly exhibited a declining and then increasing trend in 2020, and the high-risk areas were concentrated in areas with resident populations and floating populations. In addition, in February 2021, the “Spring Festival travel rush” in China was predicted to be the peak period of population movement. The epidemic risk value was also predicted to reach its highest level at external transportation stations, such as Baiyun Airport and Guangzhou South Railway Station. The accuracy verification showed that the prediction accuracy exceeded 99%. Finally, the interaction between the resident population and floating population could explain the risk of COVID-19 to the highest degree, which indicates that the effective control of population agglomeration and interaction is conducive to the prevention and control of COVID-19. This study identifies and predicts high-risk areas of the epidemic, which has important practical value for urban public health prevention and control and containment of the second outbreak of COVID-19.

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Applications of Big Data Analytics to Control COVID-19 Pandemic

Cited by 100 publications

References 78 publications

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

A review of emerging innovations in COVID-19

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

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