A modeling framework to accelerate food-borne outbreak investigations
| Autor: | Sondra R. Renly, James H. Kaufman, Kun Hu, Stefan Edlund, Matthew Davis |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
0301 basic medicine
Exploit Operations research Epidemiology 030106 microbiology Food-borne infections Food safety 03 medical and health sciences Geospatial modeling Likelihood-based method Duration (project management) business.industry Outbreaks Outbreak food and beverages Public health informatics Geospatial data Product (business) Identification (information) Risk analysis (engineering) Business Food borne outbreak Food Science Biotechnology |
| Zdroj: | Food Control. :53-58 |
| ISSN: | 0956-7135 |
| DOI: | 10.1016/j.foodcont.2015.05.017 |
| Popis: | Food safety procedures are critical to reducing pathogen caused food-borne disease (FBD). However there is no way to completely eliminate the risk of consuming contaminated products. When prevention efforts fail, rapid identification of the contaminated product is essential. The medical and economic losses incurred grow with the duration of the outbreak. In this paper we show that before an outbreak occurs, analysis of food sales data, as a proactive intervention, can provide useful product intelligence that we can exploit during an outbreak investigation to accelerate the identification process. Using real grocery retail sales data from Germany, we have implemented a likelihood-based approach to study how such data can be used to accelerate the investigation during the early stages of an outbreak. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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