ArticleInternational journal of environmental research and public health2020
Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review.Journal of medical Internet research · 2022Pooled it
- Infoveillance of COVID-19 Infections in Dentistry Using Platform X: Descriptive Study.Journal of medical Internet research · 2025Article
- CoViNAR: a context-aware social media dataset for pandemic severity level prediction and analysis.Frontiers in artificial intelligence · 2025Article
- A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets.JMIR formative research · 2024Article
- Online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP): an infodemiology study.BMC health services research · 2023Article
- Harnessing digital technology for COVID-19 response in Uganda: lessons and implications for future public health emergencies.BMJ global health · 2023Review
- Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.Journal of medical Internet research · 2023Article
- A new data integration framework for Covid-19 social media information.Scientific reports · 2023Article
- Article
- Australasian Institute of Digital Health Summit 2022-Automated Social Media Surveillance for Detection of Vaccine Safety Signals: A Validation Study.Applied clinical informatics · 2023Article
- Influenza surveillance with Baidu index and attention-based long short-term memory model.PloS one · 2023Article
- Evolution of Public Opinion on COVID-19 Vaccination in Japan: Large-Scale Twitter Data Analysis.Journal of medical Internet research · 2022Article
- Twitter conversations predict the daily confirmed COVID-19 cases.Applied soft computing · 2022Article
- A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning.Computers in biology and medicine · 2022Article
- Social media mining under the COVID-19 context: Progress, challenges, and opportunities.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- The Built Environment Assessment of Residential Areas in Wuhan during the Coronavirus Disease (COVID-19) Outbreak.International journal of environmental research and public health · 2022Article
- Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries.JMIR public health and surveillance · 2022Article
- Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan.International journal of environmental research and public health · 2022Article
- Analysis and prediction of COVID-19 epidemic in South Africa.ISA transactions · 2022Article
- Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach.Journal of medical Internet research · 2022Article
11 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6-9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments' health departments to locate potential and high-risk outbreak areas.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.