Prediction of Covid-19 Waves Using Social Media and Google Search
A Case Study of the US and Canada
Bibliographic Data
| ID | 22081997 |
|---|---|
| Authors | Samira Yousefinaghani (0000-0001-5805-4608, University of Guelph), Rozita Dara (0000-0002-3728-0275, University of Guelph, corresponding author), Samira Mubareka (0000-0001-5012-2311, Sunnybrook Health Science Centre), Shayan Sharif (0000-0002-3158-012X, University of Guelph) |
| Year | 2021 |
| Volume | 9 |
| Pages | 656635-656635 |
| Publication date | 2021-04-16 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2021.656635 |
| PMID | 33937179 |
| OpenAlex | W3153744412 |
| Language | EN |
| Citations received | 12 |
| References cited | 23 |
The ongoing COVID-19 pandemic has posed a severe threat to public health worldwide. In this study, we aimed to evaluate several digital data streams as early warning signals of COVID-19 outbreaks in Canada, the US and their provinces and states. Two types of terms including symptoms and preventive measures were used to filter Twitter and Google Trends data. We visualized and correlated the trends for each source of data against confirmed cases for all provinces and states. Subsequently, we attempted to find anomalies in indicator time-series to understand the lag between the warning signals and real-word outbreak waves. For Canada, we were able to detect a maximum of 83% of initial waves 1 week earlier using Google searches on symptoms. We divided states in the US into two categories: category I if they experienced an initial wave and category II if the states have not experienced the initial wave of the outbreak. For the first category, we found that tweets related to symptoms showed the best prediction performance by predicting 100% of first waves about 2–6 days earlier than other data streams. We were able to only detect up to 6% of second waves in category I. On the other hand, 78% of second waves in states of category II were predictable 1–2 weeks in advance. In addition, we discovered that the most important symptoms in providing early warnings are fever and cough in the US. As the COVID-19 pandemic continues to spread around the world, the work presented here is an initial effort for future COVID-19 outbreaks
Geography · Outbreak · Pandemic · Pathology · Public health · Public health surveillance · Social media · Sociology · World Wide Web · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Demography · Medicine · Misinformation and Its Impacts · Virology
Patterns of public interest in infectious diseases in South Korea, five other major countries, and worldwide
Leveraging social media data for pandemic detection and prediction
CovidTrak
Covid-19 case prediction using emotion trends via Twitter emoji analysis
A Citywide ‘Virus Testing
Deep recurrent models for forecasting infectious diseases
Web-based surveillance of respiratory infection outbreaks
Tweet Analysis for Enhancement of Covid-19 Epidemic Simulation
Modeling approaches for early warning and monitoring of pandemic situations as well as decision support
The role of social media in public health crises caused by infectious disease
Trust and Engagement on Twitter During the Management of Covid-19 Pandemic
Evaluating community resilience through social media during China’s first post-Covid-19 reopening
| Unique citing works | 12 |
|---|---|
| Citations per year | 2,4 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 12 |