Syndromic surveillance of influenza activity in Sweden: an evaluation of three tools

Abstract

SUMMARYAn evaluation was conducted to determine which syndromic surveillance tools complement traditional surveillance by serving as earlier indicators of influenza activity in Sweden. Web queries, medical hotline statistics, and school absenteeism data were evaluated against two traditional surveillance tools. Cross-correlation calculations utilized aggregated weekly data for all-age, nationwide activity for four influenza seasons, from 2009/2010 to 2012/2013. The surveillance tool indicative of earlier influenza activity, by way of statistical and visual evidence, was identified. The web query algorithm and medical hotline statistics performed equally well as each other and to the traditional surveillance tools. School absenteeism data were not reliable resources for influenza surveillance. Overall, the syndromic surveillance tools did not perform with enough consistency in season lead nor in earlier timing of the peak week to be considered as early indicators. They do, however, capture incident cases before they have formally entered the primary healthcare system.

Measuring Global Disease with Wikipedia

“…These traces of disease observations are embedded in search queries [5, 7, 9, 12, 14, 17, 21, 25, 26, 31, 32, 33, 39, 49, 50, 53, 59, 63, 64, 71, 72 73, 77, 78, 81, 85, 87, 90, 97, 103, 104, 109, 119, 126, 127, 131, 132, 141, 142, 144, 146, 157, 158, 162, 163, 166, 168, 169, 170, 173, 177, 179, 180, 182], social media messages [1, 2, 8, 10, 20, 36, 40, 41, 42, 46, 51, 60, 62, 68, 76, 84, 89, 92, 93, 115, 116, 118, 123, 124, 148, 149, 151, 176], web server access logs [57, 79, 101, 105], and combinations thereof [13, 19, 30, 91, 136, 143, 167]. …”

“…The disease surveillance work cited above has been applied to a wide variety of infectious and non-infectious conditions: allergies [87], asthma [136, 176], avian influenza [25], cancer [39], chicken pox [109, 126], chikungunya [109], chlamydia [42, 78, 109], cholera [36, 57], dengue [7, 31, 32, 57, 62, 109], diabetes [42, 60], dysentery [180], Ebola [5, 57], erythromelalgia [63], food poisoning [12], gastroenteritis [45, 50, 71, 126], gonorrhea [77, 78, 109], hand foot and mouth disease [26, 167], heart disease [51, 60], hepatitis [109], HIV/AIDS [57, 76, 177, 180], influenza [1, 2, 8, 9, 10, 13, 19, 20, 21, 30, 33, 40, 41, 43, 46, 48, 53, 57, 59, 68, 72, 73, 79, 81, 84, 85, 89, 90, 91, 92, 93, 97, 101, 103, 104, 105, 109, 115, 116, 118, 123, 124, 126, 131, 132, 141, 14...…”

“…Four of these studies report that forecasting is promising in at least some contexts [50, 79, 87, 109], while four report that it is not [101, 103, 126, 179]. Two studies report their best correlations at an anti-forecasting offset [50, 87].…”

The relationship between seasonal influenza and telephone triage for fever: A population-based study in Osaka, Japan

“…Replacing traditional surveillance with syndromic surveillance is one of the major interests in public health. Several syndromic surveillance models with absenteeism records [1][2][3], drug sales including over-the-counter drugs [4,5], and visits to the emergency department [6,7] have been reported previously. There is also a model to predict the influenza epidemic from search engine data on the Internet [8,9].…”

Epidemiological and statistical basis for detection and prediction of influenza epidemics

“…It has been stated that methods based on syndromic surveillance data may serve as complements to traditional surveillance by provision of earlier indications of influenza activity (Friesema et al 2009, Ma et al 2015. Following this, the potential of using several different syndromic data sources as a tool in local influenza surveillance was examined at the beginning of this thesis (in the eHealth study).…”