Nicholas Christakis, a sociologist and physician at Yale's Human Nature Lab, discusses pandemic predictability and the nuances of early detection. He emphasizes the shortcomings of traditional tracking methods and introduces the Hunala app, designed to analyze health risks using network data. Christakis explains how social networks can act as early warning systems and shares insights on super spreaders' impact. Additionally, he explores the delicate balance between technology and civil liberties in enhancing public health responses.
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insights INSIGHT
Predictability of Pandemics
Pandemics are predictable, but their intensity and timing are not.
Testing is crucial for identifying treatment/quarantine candidates and for policymakers to understand the disease's spread.
question_answer ANECDOTE
Hunala App and Network Sensors
The Hunala app, like "Waze for respiratory disease," crowdsources information about infection risks.
It uses social network data to provide personalized risk assessments, leveraging the "canaries in a coal mine" concept.
insights INSIGHT
Early Warning vs. Rapid Detection
There's a crucial difference between early warning and rapid detection of diseases.
Current disease tracking is often delayed, like receiving a weather forecast after experiencing the weather.
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Released on September 11, 2001, *The Blueprint* is widely regarded as one of the greatest hip-hop albums of all time. It was created during a tumultuous period in Jay-Z's life, including legal issues and feuds with other rappers. The album features a soul-based soundscape, with notable tracks like 'Izzo (H.O.V.A.)' and 'Takeover.' Despite its release coinciding with the September 11 attacks, it debuted at number one on the US charts and has since been selected for preservation in the United States National Recording Registry for its cultural and historical significance.
Pandemics are predictable; what's not predictable is the intensity, or the precise timing of arrival. That's where early detection -- not just rapid warning (as with something like Google Flu Trends back in the day), or even delayed warnings (as with CDC flu trackers and such) -- comes in. Because unfortunately, many disease tracking efforts old and new are "like watching the weather forecast a week after you've experienced that weather", observes a16z general partner Jorge Conde.
And this matters for saving lives; for load balancing and allocating resources (ventilators, PPE, supplies); getting back to work; and much more. Even a two-week advantage could have made a huge difference! Which is what sociologist and physician Nicholas Christakis (who directs the Human Nature Lab, part of the Yale Institute for Network Science, and also author of the book Blueprint) learned from the H1N1 pandemic. Specifically, the role of social network "sensors" -- where friends in one's network graph can be like canaries in the proverbial coal mine to help detect pandemics earlier.
In fact, the lab recently released an app called Hunala (which uses information crowdsourced among networks) to determine one's likelihood of contracting flu/ influenza-like or other respiratory illnesses through a personalized daily assessment of risk. Kind of like Waze, but for illnesses not car accidents. So in this episode of the a16z Podcast, the two take that analogy far. They also discuss the role of other mobility data and population flows in China for where and when the pandemic spread; the nuances behind "superspreaders"; how bad is the coronavirus, really; and the near future of "bio-surveillance" -- not just from a personal risk perspective, but from a global public-health perspective... Can we get the holy grail here without sacrificing privacy and agency?
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