Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index

Qin, Lei; Sun, Qiang; Wang, Yidan; Wu, Ke; Chen, Mingchih; Shia, Ben‐Chang; Wu, Szu-Yuan

doi:10.3390/ijerph17072365

Cited by 176 publications

(136 citation statements)

References 36 publications

(45 reference statements)

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“…Similarly, Qin and coworkers [16] exploited Big Data to predict the number of new COVID-19 cases, either suspected or confirmed. In more detail, authors utilized a lagged series of "Social media search indexes" (SMSI) for various keywords, including COVID-19 clinical symptoms (such as dry cough, fever, chest distress, and pneumonia).…”

Section: Short-term Applications Of Artificial Intelligence and Big Dmentioning

confidence: 99%

How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

Bragazzi

Dai

Damiani

et al. 2020

IJERPH

308

193

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SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.

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Section: Short-term Applications Of Artificial Intelligence and Big Dmentioning

confidence: 99%

How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

Bragazzi

Dai

Damiani

et al. 2020

IJERPH

308

193

View full text Add to dashboard Cite

show abstract

“…Based on big data models such as communication dynamics and risk level distribution of the incidence rate and close contacts, predictive analytics estimated epidemic peaks and inflection points, which allowed the differential allocation of resources to regional hospitals. Big data searches on internet platforms and susceptible-exposed-infectious-removed (SEIR) transmission modeling can predict COVID-19 transmission trends [ 43 ]. Using population migration data to fill in the dynamic propagation SEIR model combined with AI methods trained with SARS data, Yang et al [ 44 ] predicted a COVID-19 pandemic curve.…”

Section: Resultsmentioning

confidence: 99%

Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations

Wu¹,

Wang²,

Nicholas³

et al. 2020

J Med Internet Res

123

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Background In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease’s rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19. Objective The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations. Methods We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China’s new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China’s response to the epidemic and to provide lessons for other countries’ prevention and control of COVID-19. Results In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus’s sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system. Conclusions China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.

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“…This is because keyword search trends related to COVID-19 on search engines proved tremendously helpful in predicting and monitoring the spread of the virus outbreak. Qin et al [ 14 ] developed a prediction technique based on the lagged series of social media search indexes to forecast the number of new suspected COVID-19 cases. The considered social media search indexes include common COVID-19 symptoms such as dry cough, fever, pneumonia, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, Hernandez-Matamoros et al [ 13 ] have developed an ARIMA model to predict the spread of the virus, while considering some factors like the population and the number of infected cases. There are other studies that focus on collecting and analyzing posts related to COVID-19 from social media sites [ 14 , 15 , 16 ]. This is because the keyword search trends related to COVID-19 on search engines proved to be tremendously helpful in predicting and monitoring the spread of the virus outbreak.…”

Section: Introductionmentioning

confidence: 99%

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Eltoukhy

Shaban

Chan

et al. 2020

IJERPH

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The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

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Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index

Cited by 176 publications

References 36 publications

How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

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