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Comparison of statistical algorithms for syndromic surveillance aberration detection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Morbey, Roger A. Noufaily, Angela Colón-González, Felipe D. Elliot, Alex J. Harcourt, Sally Smith, Gillian E. |
| Copyright Year | 2018 |
| Abstract | Introduction Syndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the 'rising activity, multi-level mixed effects, indicator emphasis' (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods. |
| File Format | PDF HTM / HTML |
| DOI | 10.5210/ojphi.v10i1.8302 |
| Volume Number | 10 |
| Alternate Webpage(s) | http://ojphi.org/ojs/index.php/ojphi/article/download/8302/7409 |
| Journal | Online Journal of Public Health Informatics |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
