Emergency department and ‘Google flu trends’ data as syndromic surveillance indicators for seasonal influenza

Abstract

We evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data - total ED visits attributed to influenza-like illness (ILI) ('ED ILI volume') and percentage of visits attributed to ILI ('ED ILI percent') - and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.

Can Google Trends data improve forecasting of Lyme disease incidence?

“…Google Trends, Twitter and other social media platform data offer an interesting tool to monitor public attention with regard to specific infectious diseases (Al‐Surimi, Khalifa, Bahkali, El‐Metwally, & Househ, ; Goff, Kullar, & Newland, ; Martin, Xu, & Yasui, ). Several studies have shown that this quantifiable attention is a good proxy for disease activity (Dugas et al, ; Klembczyk et al, ; Martin, Lee, & Yasui, ; Pollett et al, ; Strauss, Castro, Reintjes, & Torres, ; Thompson, Malik, Gumel, Strome, & Mahmud, ). Thus, these data can help to monitor and predict infectious diseases, especially in developing areas where traditional epidemiologic surveillance faces multiple challenges (Gluskin, Johansson, Santillana, & Brownstein, ; Strauss et al, ; Teng et al, ).…”

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]. …”

“…We found 8 studies that did this. One used ridge regression [13], three used ordinary least squares [13, 57, 97], one “applied multiple linear regression analysis with a stepwise method” [169], one used a log-odds linear model with no fitting algorithm specified [167], and the rest did not specify a fitting algorithm [77, 131, 157]. We suspect that the latter four used ordinary least squares, as this is the most common linear regression algorithm.…”

“…Six of these studies reported positive results, i.e., that the approach was successful or promising in at least some contexts [13, 57, 77, 131, 167, 169], while two reported negative results [97, 157]. …”

A look back: investigating Google Flu Trends during the influenza A(H1N1)pdm09 pandemic in Canada, 2009–2010

“…GFT performance during the 2009 H1N1 pandemic has also been described in other countries [4][5][6][7]. In Canada, GFT estimates during the 2009 H1N1 pandemic have been examined on a provincial level in Manitoba [8,9]; however, to my knowledge, they have not been examined nationally in this country during that time. Although beginning in August 2015, Google stopped posting real-time GFT estimates online, GFT estimates are still available to some researchers [10] and previous estimates remain publicly available [11].…”