Author Summary As public health agencies strive to harness big data to improve outbreak surveillance, they face the challenge of extracting meaningful information that can be directly used to improve public health, without incurring additional costs. In this article, we address the question: Which nodes in a social network should be selectively monitored to detect and monitor outbreaks as early and accurately as possible? We derive best-case performance scenarios, and show that a practical strategy for data collection recruiting friends of randomly selected individuals is expected to perform reasonably well, in terms of the timing and reliability of the epidemiological information collected.
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