ArticlePLoS computational biology2019
Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Recommendations for the analysis of leading indicators in epidemic surveillance.PLOS digital health · 2026Review
- Epidemiological and digital syndromic surveillance data on dengue, chikungunya, and SARI in Brazil.Scientific data · 2026Article
- Evaluating Google Trends as a proxy for symptom incidence: insights from the winter COVID-19 infection study in England 2023/24.Epidemiology and infection · 2025Article
- Real-time forecasting of data revisions in epidemic surveillance streams.PLoS computational biology · 2025Article
- Ensemble approaches for short-term dengue fever forecasts: A global evaluation study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information.PLoS neglected tropical diseases · 2025Article
- Google search interests and new cases of COVID-19 in Bangladesh: a vector autoregression analysis for disease surveillance.BMC research notes · 2025Article
- A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.Epidemics · 2025Article
- Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.Scientific reports · 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
- Heightened religiosity proactively and reactively responds to the COVID-19 pandemic across the globe: Novel insights from the parasite-stress theory of sociality and the behavioral immune system theory.International journal of intercultural relations : IJIR · 2022Article
- Sparse representations of high dimensional neural data.Scientific reports · 2022Article
- Influenza, dengue and common cold detection using LSTM with fully connected neural network and keywords selection.BioData mining · 2022Article
- Trends of Online Search of COVID-19 Related Terms in Cyprus.Epidemiologia (Basel, Switzerland) · 2021Article
- Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.PloS one · 2021Article
- A call for an ethical framework when using social media data for artificial intelligence applications in public health research.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dengue and influenza-like illness (ILI) are two of the leading causes of viral infection in the world and it is estimated that more than half the world's population is at risk for developing these infections. It is therefore important to develop accurate methods for forecasting dengue and ILI incidences. Since data from multiple sources (such as dengue and ILI case counts, electronic health records and frequency of multiple internet search terms from Google Trends) can improve forecasts, standard time series analysis methods are inadequate to estimate all the parameter values from the limited amount of data available if we use multiple sources. In this paper, we use a computationally efficient implementation of the known variable selection method that we call the Autoregressive Likelihood Ratio (ARLR) method. This method combines sparse representation of time series data, electronic health records data (for ILI) and Google Trends data to forecast dengue and ILI incidences. This sparse representation method uses an algorithm that maximizes an appropriate likelihood ratio at every step. Using numerical experiments, we demonstrate that our method recovers the underlying sparse model much more accurately than the lasso method. We apply our method to dengue case count data from five countries/states: Brazil, Mexico, Singapore, Taiwan, and Thailand and to ILI case count data from the United States. Numerical experiments show that our method outperforms existing time series forecasting methods in forecasting the dengue and ILI case counts. In particular, our method gives a 18 percent forecast error reduction over a leading method that also uses data from multiple sources. It also performs better than other methods in predicting the peak value of the case count and the peak time.
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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.