ArticlePLoS computational biology2020
Real-time estimation of disease activity in emerging outbreaks using internet search information.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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Who cites it
16 citing papers in PubMed, 24 citations in OpenAlex.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Assessing human judgment forecasts in the rapid spread of the mpox outbreak: insights and challenges for pandemic preparedness.BMC infectious diseases · 2026Article
- A real-time early warning system to anticipate respiratory disease outbreaks using transfer learning.Nature communications · 2026Article
- Scoping review of artificial intelligence via mobile technology and social media for health in Africa.Nature communications · 2025Article
- How can timeliness and accuracy be balanced? The surveillance information reporting mechanism for public health emergencies based on the online direct reporting system.BMC public health · 2025Article
- Hospitalisations in Brazil: an ecological time series analysis of the impact of medical decision support data as an exogenous variable.BMC public health · 2025Article
- 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
- Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation.JMIR public health and surveillance · 2022Article
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- Reliability of COVID-19 data: An evaluation and reflection.PloS one · 2022Article
- Predicting seasonal influenza using supermarket retail records.PLoS computational biology · 2021Article
- An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.Science advances · 2021Article
- Predicting regional influenza epidemics with uncertainty estimation using commuting data in Japan.PloS one · 2021Article
- Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.Journal of medical Internet research · 2020Article
- The opportunities and challenges of an Ebola modeling research coordination group.PLoS neglected tropical diseases · 2020Article
- From data to decisions: The vital role of digital epidemiology for strengthening public health.Digital healthReview
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful "nowcasts" of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. However, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage.
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Registered trials
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.