2016/12/08 by Shihao Yang, S. C. Kou, Yang, Shihao +9
Mathematics · Medicine · #Applications (stat.AP) #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #FOS: Biological sciences #FOS: Computer and information sciences #Mosquito-borne diseases and control #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1612.02812
openalex publication_date 2016/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Dengue is a mosquito-borne disease that threatens more than half of the world's population. Despite being endemic to over 100 countries, government-led efforts and mechanisms to timely identify and track the emergence of new infections are still lacking in many affected areas. Multiple methodologies that leverage the use of Internet-based data sources have been proposed as a way to complement dengue surveillance efforts. Among these, the trends in dengue-related Google searches have been shown to correlate with dengue activity. We extend a methodological framework, initially proposed and validated for flu surveillance, to produce near real-time estimates of dengue cases in five countries/regions: Mexico, Brazil, Thailand, Singapore and Taiwan. Our result shows that our modeling framework can be used to improve the tracking of dengue activity in multiple locations around the world.