H1N1

Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other’s ‘tweets’, or short messages limited to 140 characters. The service has more than 190 million registered users and processes about 55 million tweets per day. Despite a high level of chatter, the Twitter stream does contain useful information, and, because tweets are often sent from handheld platforms on location, they convey more immediacy than other social networking systems.

Objective

This paper describes a system that uses Twitter to estimate influenza-like illness levels by geographic region.

The H1N1 outbreak in the spring of 2009 in NYC originated in a school in Queens before spreading to others nearby. Active surveillance established epidemiological links between students at the school and new cases at other schools through household connections. Such findings suggest that spatial cluster detection methods should be useful for identifying new influenza outbreaks in school-aged children. As school-to-school transmission should occur between those with high levels of interaction, existing cluster detection methods can be improved by accurately characterizing these links. We establish a prospective surveillance system that detects outbreaks in NYC schools using a flexible spatial scan statistic (FlexScan), with clusters identified on a network constructed from student interactions.

Objective

To improve cluster detection of influenza-like illness within New York City (NYC) public schools using school health and absenteeism data by characterizing the degree to which schools interact.