Name
Abstract | ||
|---|---|---|
Dengue is a mosquito-borne disease that threatens over half of the world's population. Despite being endemic to more than 100 countries, government-led efforts and tools for timely identification and tracking 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, dengue-related Google search trends 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/states: 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1371/journal.pcbi.1005607 | PLOS COMPUTATIONAL BIOLOGY |
Field | DocType | Volume |
Data science,Population,Disease,Environmental protection,Biology,Disease surveillance,Bioinformatics,Dengue fever,The Internet,Government | Journal | 13 |
Issue | Citations | PageRank |
7 | 2 | 0.40 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shihao Yang | 1 | 2 | 0.40 |
| S. C. Kou | 2 | 4 | 1.67 |
| Fred Lu | 3 | 2 | 0.74 |
| John S Brownstein | 4 | 191 | 21.62 |
| Nicholas Brooke | 5 | 2 | 0.40 |
| Mauricio Santillana | 6 | 41 | 5.20 |