Real-Time Tracking of Control Measures for Emerging Infections
| dc.contributor.author | Lipsitch, Marc | en_US |
| dc.contributor.author | Bergstrom, Carl T. | en_US |
| dc.date.accessioned | 2004-10-18T20:57:36Z | en_US |
| dc.date.accessioned | 2007-06-13T19:57:40Z | |
| dc.date.available | 2004-10-18T20:57:36Z | en_US |
| dc.date.available | 2007-06-13T19:57:40Z | |
| dc.date.issued | 2004 | en_US |
| dc.description.abstract | Health officials faced a daunting task with the emergence of severe acute respiratory syndrome (SARS) last year: forecasting the trajectory of an emerging infectious disease and implementing effective control measures, even as the etiologic agent was still being identified. Investigators initially had little to go on beyond crude epidemiologic data such as the timing of new cases (the epidemic curve). With such limited data, it was difficult to disentangle two fundamental epidemiologic quantities: the time from one transmission of the infection to the next, known as the serial interval or generation time, and the average number of secondary cases resulting from each infection, known as the reproductive number....In the current issue of the Journal, Wallinga and Teunis present a statistical escape from this analytical Catch-22. | en_US |
| dc.format.extent | 50832 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | American Journal of Epidemiology. 160:517-519 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1773/1983 | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | American Journal of Epidemiology | en_US |
| dc.subject | disease outbreaks | en_US |
| dc.subject | estimation | en_US |
| dc.subject | infection | en_US |
| dc.subject | statistical models | en_US |
| dc.subject | SARS virus | en_US |
| dc.subject | severe acute respiratory syndrome | en_US |
| dc.title | Real-Time Tracking of Control Measures for Emerging Infections | en_US |
| dc.type | Article | en_US |
