In this and other outbreaks, digital disease surveillance has supplemented the critical laboratory studies and work in the trenches by public health officials and epidemiologists, by leveraging widespread use of the Internet, mobile phones, and social media.
Many of these added insights come from the general population, whose access to technology enables rapid information flow. In 2013, there are 6.8 billion cell-phone subscribers; 2.7 billion people are online; and by the end of the year, there will be more than 2 billion mobile broadband subscriptions worldwide. A large percentage of the online population publicly shares information on social media services: in both the United States and China, for example, more than half the population with access to the Internet uses social media services.
Digital data can be used in at least four ways for studying infectious-disease dynamics. First, they can be used for early detection of disease outbreaks. This capacity was illustrated most recently in China, when a hospital employee uploaded an image of the medical record of a patient with H7N9 infection to Sina Weibo, a popular Chinese social network similar to Twitter. The post was promptly deleted, but it appears to have accelerated the government's acknowledgment of four new cases (see figure, Panel B). More generally, because digital surveillance is not limited by the hierarchies of traditional public health infrastructure, geographic communication barriers, and geopolitical obstacles, it has improved the timeliness of outbreak detection substantially in recent years.
Second, these data can be used to continuously monitor disease levels. With proper filtering by automated systems (see the Journal's H7N9 HealthMap tracking system [http://healthmap.org/h7n9]), analyst-driven systems (e.g., the Global Public Health Intelligence Network of Canada), vigilant journalists on Twitter (e.g., Crawford Kilian [@Crof] and Helen Branswell [@HelenBranswell]), and crowd-sourced systems (e.g., FluTrackers and ProMED-mail), informal data sources such as news media, e-mail lists, blogs, and social media can complement formal public health surveillance by offering real-time clues to disease dynamics. Internet-based surveillance systems provided important early epidemic intelligence during the 2003 outbreak of severe acute respiratory syndrome (SARS) and the 2009 H1N1 influenza pandemic, enhancing transparency by rapidly publicizing outbreak information.3
Third, Internet-based data from social media can be used to assess disease-relevant health-related behaviors and sentiments relevant to disease control. During the H1N1 pandemic, sentiments about vaccination extracted from Twitter were shown to correlate well geographically with subsequent vaccination coverage throughout the United States. Such analyses could provide important information to aid in planning and in the distribution of limited resources, as well as improving public health communications efforts.
Fourth, these data provide researchers with an additional method for examining the period before an outbreak came to light. Despite international agreement that transparency is critical during an outbreak, accusations of delayed reporting are common and can be difficult to dispel. Time-series analysis of the volume of influenza-related searches on the Chinese Web search engine Baidu shows a low level of activity in the months leading up to the first announced H7N9 cases, which suggests that widespread unreported outbreaks were not festering before the announcement.