Web Queries as a Source for Syndromic Surveillance

Abstract

In the field of syndromic surveillance, various sources are exploited for outbreak detection, monitoring and prediction. This paper describes a study on queries submitted to a medical web site, with influenza as a case study. The hypothesis of the work was that queries on influenza and influenza-like illness would provide a basis for the estimation of the timing of the peak and the intensity of the yearly influenza outbreaks that would be as good as the existing laboratory and sentinel surveillance. We calculated the occurrence of various queries related to influenza from search logs submitted to a Swedish medical web site for two influenza seasons. These figures were subsequently used to generate two models, one to estimate the number of laboratory verified influenza cases and one to estimate the proportion of patients with influenza-like illness reported by selected General Practitioners in Sweden. We applied an approach designed for highly correlated data, partial least squares regression. In our work, we found that certain web queries on influenza follow the same pattern as that obtained by the two other surveillance systems for influenza epidemics, and that they have equal power for the estimation of the influenza burden in society. Web queries give a unique access to ill individuals who are not (yet) seeking care. This paper shows the potential of web queries as an accurate, cheap and labour extensive source for syndromic surveillance.

Digital epidemiology reveals global childhood disease seasonality and the effects of immunization

“…Over the past decade, the Internet has become a significant health resource for the general public and health professionals (10,11). Internet query platforms, such as Google Trends, have provided powerful and accessible resources for identifying outbreaks and for implementing intervention strategies (12)(13)(14). Research on infectious disease informationseeking behavior has demonstrated that Internet queries can complement traditional surveillance by providing a rapid and efficient means of obtaining large epidemiological datasets (13,(15)(16)(17)(18).…”

“…Research on infectious disease informationseeking behavior has demonstrated that Internet queries can complement traditional surveillance by providing a rapid and efficient means of obtaining large epidemiological datasets (13,(15)(16)(17)(18). For example, epidemiological information contained within Google Trends has been used in the study of rotavirus, norovirus, and influenza (14,15,17,18). These tools offer substantial promise for the global monitoring of diseases in countries that lack clinical surveillance but have sufficient Internet coverage to allow for surveillance via digital epidemiology.…”

Global Disease Monitoring and Forecasting with Wikipedia

“…When appropriately trained, these methods can be quite accurate; for example, many of the cited models can produce near real-time estimates of case counts with correlations upwards of r = 0.95. The collection of disease surveillance work cited above has estimated incidence for a wide variety of infectious and noninfectious conditions: avian influenza [52], cancer [55], chicken pox [67], cholera [81], dengue [50,53,84], dysentery [76], gastroenteritis [56,61,67], gonorrhea [64], hand foot and mouth disease (HFMD) [72], HIV/AIDS [75,76], influenza [34,36,54,57,59,62,63,65,67,68,71,74,[77][78][79][80]82,83,[85][86][87][88][89][90][91][92][93], kidney stones [51], listeriosis [70], malaria [66], methicillin-resistant Staphylococcus aureus (MRSA) [58]<...>…”

A Survey of Current Work in Medical Text Mining---Data Source Perspective

“…Search-term surveillance may provide an additional tool for disease surveillance. Hulth, Rydevik & Linde (2009) calculated the occurrence of various queries related to influenza from search logs submitted to a Swedish medical web site for two influenza seasons [9]. These figures were subsequently used to generate two models, one to estimate the number of laboratory verified influenza cases and one to estimate the proportion of patients with influenza-like illness reported by selected General Practitioners in Sweden.…”