Why we need crowdsourced data in infectious disease surveillance

Abstract

In infectious disease surveillance, public health data such as environmental, hospital, or census data have been extensively explored to create robust models of disease dynamics. However, this information is also subject to its own biases, including latency, high cost, contributor biases, and imprecise resolution. Simultaneously, new technologies including Internet and mobile phone based tools, now enable information to be garnered directly from individuals at the point of care. Here, we consider how these crowdsourced data offer the opportunity to fill gaps in and augment current epidemiological models. Challenges and methods for overcoming limitations of the data are also reviewed. As more new information sources become mature, incorporating these novel data into epidemiological frameworks will enable us to learn more about infectious disease dynamics.

Fig. 1

a Average percentage of visits to CDC sentinel sites for ILI by week. b Average number of patients seen at sentinel sites by week. Data are for seasons 2000–2011, pandemic seasons and those with 53 weeks excluded. Holiday weeks (shaded areas: 46–48, Thanksgiving and 51–1, New Years) show both an increase in %ILI visits and a decreased amount of patient visits

Fig. 2

Disease events by continent via news reports 2006–2009, compared with WHO disease reports for the same time period