ArticleNPJ digital medicine2021
Internet search patterns reveal clinical course of COVID-19 disease progression and pandemic spread across 32 countries.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends in symptom prevalence and sequential onset of SARS-CoV-2 infection from 2020 to 2022 in East and Southeast Asia: a trajectory pattern exploration based on summary data.Archives of public health = Archives belges de sante publique · 2024Pooled it
- Learning from pandemics: Feature selection and clustering for robust digital disease nowcasting.PloS one · 2026Article
- Inferring skin-brain-skin connections from infodemiology data using dynamic Bayesian networks.Scientific reports · 2024Article
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.JMIR public health and surveillance · 2023Article
- The effects of contemporaneous air pollution on COVID-19 morbidity and mortality.Journal of environmental economics and management · 2023Article
- Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States.Science advances · 2023Article
- Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.Frontiers in public health · 2023Article
- Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.NPJ digital medicine · 2022Article
- Web and social media searches highlight menstrual irregularities as a global concern in COVID-19 vaccinations.Scientific reports · 2022Article
- Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence.Infectious Disease Modelling · 2022Article
- Article
- Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives.Frontiers in public health · 2022Article
- Article
- The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis.Scientific reports · 2021Article
- Do responses to the COVID-19 pandemic anticipate a long-lasting shift towards peer-to-peer production or degrowth?Sustainable production and consumption · 2021Article
- Immune transcriptomes from hospitalized patients infected with the SARS-CoV-2 variants B.1.1.7 and B.1.1.7 carrying the E484K escape mutation.medRxiv : the preprint server for health sciences · 2021Article
- Using Internet Search Queries to Assess Public Awareness of the Healthy Cities Approach: A Case Study in Shenzhen, China.International journal of environmental research and public health · 2021Article
- An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.Science advances · 2021Article
- How do Google searches for symptoms, news and unemployment interact during COVID-19? A Lotka-Volterra analysis of google trends data.Quality & quantity · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
Effective public health response to novel pandemics relies on accurate and timely surveillance of pandemic spread, as well as characterization of the clinical course of the disease in affected individuals. We sought to determine whether Internet search patterns can be useful for tracking COVID-19 spread, and whether these data could also be useful in understanding the clinical progression of the disease in 32 countries across six continents. Temporal correlation analyses were conducted to characterize the relationships between a range of COVID-19 symptom-specific search terms and reported COVID-19 cases and deaths for each country from January 1 through April 20, 2020. Increases in COVID-19 symptom-related searches preceded increases in reported COVID-19 cases and deaths by an average of 18.53 days (95% CI 15.98-21.08) and 22.16 days (20.33-23.99), respectively. Cross-country ensemble averaging was used to derive average temporal profiles for each search term, which were combined to create a search-data-based view of the clinical course of disease progression. Internet search patterns revealed a clear temporal pattern of disease progression for COVID-19: Initial symptoms of fever, dry cough, sore throat and chills were followed by shortness of breath an average of 5.22 days (3.30-7.14) after initial symptom onset, matching the clinical course reported in the medical literature. This study shows that Internet search data can be useful for characterizing the detailed clinical course of a disease. These data are available in real-time at population scale, providing important benefits as a complementary resource for tracking pandemics, especially before widespread laboratory testing is available.
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