ArticleScientific reports2021
Incorporating human mobility data improves forecasts of Dengue fever in Thailand.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- Assessing human judgment forecasts in the rapid spread of the mpox outbreak: insights and challenges for pandemic preparedness.BMC infectious diseases · 2026Article
- Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors.Infectious Disease Modelling · 2026Article
- Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
- Article
- Nonlinear and lagged effects of climate variability on dengue incidence in an urban megacity: a distributed lag non-linear model (DLNM) based study in Bangkok, Thailand.BMC public health · 2025Article
- Characterization of the most austral autochthonous dengue outbreak reported in the world (city of Bahía Blanca, Argentina, January-June 2024). A cross-sectional study.Lancet regional health. Americas · 2025Article
- Shifting Dynamics of Dengue Virus Serotype 2 and Emergence of Cosmopolitan Genotype, Costa Rica, 2024.Emerging infectious diseases · 2025Article
- Investigating linkages between human movement and meteorological variables on dengue outbreaks in the Pacific Islands.PLoS neglected tropical diseases · 2025Article
- Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States.Proceedings of the National Academy of Sciences of the United States of America · 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
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Bias in mobility datasets drives divergence in modeled outbreak dynamics.Communications medicine · 2025Article
- Rapid human movement and dengue transmission in Bangladesh: a spatial and temporal analysis based on different policy measures of COVID-19 pandemic and Eid festival.Infectious diseases of poverty · 2024Article
- Impact of the COVID-19 pandemic on dengue in Brazil: Interrupted time series analysis of changes in surveillance and transmission.PLoS neglected tropical diseases · 2024Article
- Little disease but lots of bites: social, urbanistic, and entomological risk factors of human exposure to Aedes aegypti in South Texas, U.S.PLoS neglected tropical diseases · 2024Article
- Human mobility and the infectious disease transmission: A systematic review.Geo-spatial information science = Diqui kongjian xinxi kexue xuebao · 2024Article
- Effects of human mobility on the spread of Dengue in the region of Caldas, Colombia.PLoS neglected tropical diseases · 2023Article
- Estimation of mobility and population in Spain during different phases of the COVID-19 pandemic from mobile phone data.Scientific reports · 2023Article
- Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction.PLoS neglected tropical diseases · 2023Article
- Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm.PLoS neglected tropical diseases · 2023Article
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11 authors.
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Abstract
Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.
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