

Ask YC: This is our tech, how can we make this into a product that people want? - anmol
http://www.newscientist.com/article/mg20827824.800-cellphones-reveal-emerging-disease-outbreaks.html

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pbhjpbhj
> _When Madan trained software to hunt for this signature in the cellphone
> data, a daily check correctly identified flu victims 90 per cent of the
> time._

I'm very surprised by this - it strikes me that I'd expect it to be hard to
tell from such data that it wasn't a stomach upset, tiredness, onset of
disease or simply a heavy cold. Indeed 90% sounds like it might be higher than
correct identification via an interview with a doctor.

> _[...] in Rwanda between 2006 and 2009. He saw a clear reduction in people's
> movement, which may have been due to the disease. But the outbreak was
> caused by floods, which also limited mobility. Distinguishing between the
> two possible causes on the basis of phone data alone was impossible, he
> says._

Makes me the 90% figure highly suspect.

That aside I wonder if it wouldn't be easier to have a smart phone app that
gives you epidemiology info in return for entering your health status for the
day. To encourage continued use a sort of diary function and stats could be
added. Wouldn't this be more predictive than monitoring movement, and less
intrusive?

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anmol
The 90% is a misquote, good catch.

The actual accuracy is between 60-90%, depending on clusters of symptoms, and
apparently that was stripped somewhere during editing. Oh well.

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andrewljohnson
With a range that large, I would be highly suspect that when you did get 90%,
it was just fitting the data. And how would you even confirm if they had the
flu - even a doctor could misdiagnose mild flu, which adds more noise.

60% really means very little if it's a binary choice - flu or no. You'll be
right 50% of the times no mater what.

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moultano
>60% really means very little if it's a binary choice - flu or no. You'll be
right 50% of the times no mater what.

What? Just because something is binary doesn't mean it happens half the time.

~~~
andrewljohnson
Hmm, yeah... good point on that. I'll leave my comment, but I take it back :)

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anmol
With severely unbalanced classes, overall accuracy is not a good metric. So we
use recall of the symptom class, and how it varies with the precision. Recall
= 0.6 to 0.9. All results are based on cross-validation.

There is a lot more sophistication we can add (e.g. markov properties), these
are almost first-order results.

Nonetheless important to validate with other populations. That would be a big
part of testing any product.

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ABrandt
I think a very strong possibility lies with the very experiment described in
the article. Flu is a huge issue at college campuses, high schools, etc. Every
level of education involves cramming a bunch of individuals in to small spaces
with high amounts of contact (more so than in a prison, for example). In
higher education, sexual activity and drinking (i.e. sharing cups) further
increases risk of infection. My campus of 5,000 undergrads turned into a
textbook example of mass hysteria during last year's swine flu scare. I can
only imagine how many hours of education must have been lost through the whole
ordeal.

So build an enterprise level system and sell it to individual universities.
The school installs your application on each student's phone and receive an
aggregate view of anonymous data through a back-end interface. This could give
them a real-time look of how the infection is spreading through their
community and allow them to be far more responsive in prevention measures.
What if they could receive warning that the 3rd floor of Residence Hall A had
signs of a potential outbreak? Just bring in those students for a preemptive
checkup and stop the bug dead in its tracks.

Of course there is a boat load of legal and privacy issues to sort out for
this type of intrusive monitoring. Nothing that couldn't be overcome I don't
think (IANAL). I know that my school installs an app on all of our athlete's
Facebook accounts for monitoring purposes so this isn't too far off from that.

Good luck with whatever path you choose! I'm always open to discussing startup
strategy so feel free to drop me a line.

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mikesimonsen
Anmol - you're building an information business. And at scale, IMHO, they're
fabulous. Health information seems like a massive market, and this seems like
unique data. (Think about financial consumers who care about health data in
addition to the pharmas.) I'll assume your app scales so that you get critical
mass of input, but once you have that in your sights, here's how we
constructed our information business.

In my experience, the best information businesses are founded on a core of
primary research - data that you find/generate that no one else has. Once you
build that database, you build the products. You might be surprised, but
here's what many information products look like - I'm a fan of doing it in
this order:

1\. A big honking CSV file of the primary data. For customers that have their
own models, which your early adopters will likely have. Ours did.

2\. A CSV file of derivative analytics (the output of your prediction tools
perhaps?). For customers who want your algorithmic take on the underlying
data.

3\. Reports (manually written). People buy your analysis of the data. We found
a majority of the potential market doesn't want Tools or data files (even if
they're Wall Street "quants"). They just want answers - and by virtue of
having the data, you're an instant expert. Reports have the nice benefit of
being A) tangible B) easy to consume and C) subscribe-able (repeat revenue!)
They have a bonus of being delicious chum for the media sharks.

4\. Reports (automated, for scale). Subscribers get the latest insights each
week, month, etc. Best not to automate until you see which of #3 are most
popular.

5\. Now come the technology Tools. #3 and #4 above will be excellent test beds
for your prediction tools. Doing it in this order, you won't have to build any
front end applications until you understand what predictions people pay for.
One way to build a tools-based product line is to give the tech away for free
and have the customer subscribe to the latest data.

You might also find that a great consumer-facing website is powerful for the
information business. We don't have those skills in our company, but have
found that when we pull it off even a little, institutional buyers begin as
personal, individual consumers. A great consumer facing site/app is a perfect
way to reach them.

rock on.

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SwellJoe
At the direct-to-consumers level, think "I've fallen and I can't get up!"
Automatic detection of illness and alerts for the old and infirm could be a
potential market, though research on college students is not going to be at
all effective for monitoring old people. And, if it can only detect trends
rather than a specific individual situation, then it won't work in this
context.

As others mentioned, making it a standard part of all phones might be
something you could sell to governments, particularly in areas where flu
frequently becomes epidemic.

It's a public health issue, which isn't usually an extremely easy to go-to-
market area, I would think. Since it doesn't cure people of the flu, and
doesn't help prevent it in any particular individual, it has nearly zero
market value for any individual. I wouldn't even have a reason to install it
for free...what good would it do me?

Is there some element of your research and tech that can be generalized to
spot other kinds of trends? Trendspotting is extremely valuable, particularly
to marketers...Google would probably acquire a company that can effectively
trendspot from mobile devices, and even if they didn't, a mobile ad platform
with trendspotting would be incredibly lucrative.

~~~
anmol
The challenge with 'think I've fallen' is that you constantly have to ping the
accelerometer. From a battery-life perspective, that kills the smartphone.
Also, its hard to distinguish those pings from

For that particular application, its better to use custom body-worn hardware
(e.g. fitbit).

Regarding spotting trends in face-to-face networks, we've done similar
analysis political opinions during the 2008 election campaign.

Results here: <http://web.media.mit.edu/~anmol/political-2.pdf>

~~~
anmol
__pings from device forgotten at home. sorry about typo.

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tophat02
My personal opinion: if you can REALLY do what you claim then you should get
every country's equivalent of the CDC working with you to MANDATE that this be
part of every phone, and then of course you get the revenue from IP licensing.

In fact, I would broaden the scope a bit: make it like the "Emergency
Broadcast System" for phones, only two-way.

Lots of privacy implications and the carriers and handset manufacturers would
hate it, but I can certainly see it doing long term good.

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anmol
Thoughts? We're thinking of individual-level prediction tools, but there are
other ways of using this approach. e.g. at the community / city scale with
mobile operator data.

~~~
psawaya
I don't know if you'd get the same quality of data, but it seems like you
could get the same kind of data from a user's Foursquare (location) and
Facebook page.

Now that I think about it, I imagine most young people use Facebook
messages/wall posts more than phone calls and texts.

Or perhaps a browser plugin that anonymously reports browsing habits.

~~~
anmol
The demographics that seem important (e.g. baby-boomers), use mobile phones
but not necessarily FSq.

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ThomPete
Interesting.

Questions

1\. How fast can you spot a trend? 2\. How is the tracking working? 3\. How
precise is it.

I could imagine traffic would be a good area to look into.

~~~
pbhjpbhj
>3\. How precise is it.

I've just been away in the Midlands (UK) and used Google Maps on a mobile for
the first time. The triangulation of position was only accurate to about 1500m
radius and then would switch between positions quite often. Such info would
look much like a user that was moving quite a lot - I expect it could be
filtered out but this sort precision (and lack of accuracy) appears common in
the UK.

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quizbiz
I would market this to pharmaceuticals as a way to effectively gauge demand
geography. The value added for them would be supply chain optimization.

~~~
anmol
good idea. we had an initial chat with one of the major ones. They'd like to
see more traction of course.

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araneae
Have you looked into SBIRs? www.sbir.gov/

BTW, small world, my SO knows Eagle :P

~~~
anmol
very briefly. We're comparing the time + effort spent crafting an application
(which apparently is quite large, for first-timers) and not fitting the ask VS
simply building it and approaching consumers / partners.

If you have particular insight on any SBIR proposals related to our work, do
share. Thanks!

ps. yes, Nathan is a friend.

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meatsock
just make an app that warns you if any of you friends cell or computer use
changes. I'd pay to see which of my friends might be getting sick. Luckily,
each new health scare will drive up demand.

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gojomo
Seems like you have something that public health officials would want, and
_maybe_ mobile carriers, if the privacy issues can be solved and having it
installed wins them some points with their regulators.

If you want to make something businesses want to pay for, see if the same
predictive models work for forecasting product demand and other economic
activity. What is the effect of a movie release on patterns of communication
and movement? Can profiles be (semi-anonymously) correlated with other
tweets/purchases, so you know that learning about a certain product, or buying
it, changes behavior in some way?

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zaidf
Apply to YC:)

