Virus detection and mathematical modeling have gone through rapid developments in the past decade. Both offer new insights into the epidemiology of infectious disease and characterization of future risk; however, modeling has not yet been applied to designing the best surveillance strategies for viral and pathogen discovery. We review recent developments and propose methods to integrate viral and pathogen discovery and mathematical modeling through optimal surveillance theory, arguing for a more targeted approach to novel virus detection guided by the principles of adaptive management and structured decision-making.
Citation Information
| Publication Year | 2013 |
|---|---|
| Title | Surveillance theory applied to virus detection: a case for targeted discovery |
| DOI | 10.2217/fvl.13.105 |
| Authors | Tiffany L. Bogich, Simon J. Anthony, James D. Nichols |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Future Virology |
| Index ID | 70055519 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Patuxent Wildlife Research Center |