Welcome to the After-On podcast. I'm your host, Rob Reed, bringing you conversations

with thinkers, founders and scientists. They're in depth and unheard, and are structured

to bring you up to a top percentile understanding of something important. So, whether you're

into startups or ideas, a techie or lit major, take your time, engage your mind, and you'll

be glad you did. Hello listeners, and welcome to the first episode of the 2021 series of the

After-On podcast. It has been quite a while since my last real episode, so I have a fair

amount of housekeeping to cover today. But before getting into that, a quick preview

of the episode. Today we're going to talk to data scientists and author Seth Stevens-Dividowitz.

Seth is a New York Times contributor, a Google alum, and the author of Everybody Lies, Big Data,

New Data, and what the internet can tell us about who we really are. As you'll soon hear,

Seth analyzes giant pools of digital data like the geographic origin and frequency of the queries

we type into Google and other search engines to reveal surprising realities about society,

and the human psyche. He and I cover a lot of subjects in our interview, starting out with one

that's of urgent interest to me, which is the potential for search engine data to pinpoint

sudden outbreaks of disease, days, or even weeks before local public health authorities become

aware of them. As you'll soon hear, Seth and others have clearly demonstrated this potential

in their work. Work that society should expand on dramatically to see if we can create a global

early warning system for outbreaks of all kinds, above all novel clusters of symptoms that could

signal the rise of a new pathogen. If the world had picked up on this sort of signal just two to

three weeks before things spun out of control in Wuhan, the vast tragedy of the COVID pandemic might

have been averted. Now, it may or may not be possible to build a truly reliable outbreak monitor

from internet data. As Seth and I discuss, unfreatening signals might easily be mistaken for outbreaks,

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