What to know before forecasting the flu
| dc.contributor.author | Chakraborty, Prithwish | en |
| dc.contributor.author | Lewis, Bryan L. | en |
| dc.contributor.author | Eubank, Stephen | en |
| dc.contributor.author | Brownstein, John S. | en |
| dc.contributor.author | Marathe, Madhav V. | en |
| dc.contributor.author | Ramakrishnan, Naren | en |
| dc.contributor.department | Computer Science | en |
| dc.contributor.department | Discovery Analytics Center | en |
| dc.contributor.department | Fralin Life Sciences Institute | en |
| dc.date.accessioned | 2018-10-15T18:46:20Z | en |
| dc.date.available | 2018-10-15T18:46:20Z | en |
| dc.date.issued | 2018-10-12 | en |
| dc.description.abstract | Accurate and timely influenza (flu) forecasting has gained significant traction in recent times. If done well, such forecasting can aid in deploying effective public health measures. Unlike other statistical or machine learning problems, however, flu forecasting brings unique challenges and considerations stemming from the nature of the surveillance apparatus and the end utility of forecasts. This article presents a set of considerations for flu forecasters to take into account prior to applying forecasting algorithms. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1371/journal.pcbi.1005964 | en |
| dc.identifier.issue | 10 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/85375 | en |
| dc.identifier.volume | 14 | en |
| dc.language.iso | en_US | en |
| dc.publisher | PLOS | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | What to know before forecasting the flu | en |
| dc.title.serial | PLOS Computational Biology | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |