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Diff for: google_symptoms_exploration/plans.md

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## Plans
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- Overview: Old GHT, new Google Symptoms. Go through previous notebook (copy
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that notebook into this directory)
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- Demo correlations notebook: very crude time analysis where we aggregate over
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all counties and examine the correlation with respect to time
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- Open problems; each one can be its own notebook:
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- Amount of missingness by county, symptom. Should we just discard some
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of the symptoms because it's available so rarely? Many of the symptoms
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seem _a priori_ totally unrelated to COVID-19, e.g., excessive hoarding,
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should we toss those out to make our lives easier?
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- Overwhelming number of symptoms reported: 422. How to efficiently
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perform a correlations analysis for all of them at once? (We would
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want to examine over time; across counties; at various time lags
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against cases)
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- Correlations analysis can give us a sense of what symptoms to include
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in a new indicator; then we would need to weigh them appropriately
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into the new indicator. Unexpected correlations (that are not
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spurious) may provide new insight.
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- My view on two approaches to a new indicator (we could do both, assuming
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we have the people):
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- "Scientifically guided approach": consult medical / public health
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literature & experts to obtain a subset of symptoms, weigh them
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appropriately, this is the new indicator. Google considered such an
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approach, but decided to defer construction to users because of
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lack of uniform advice across different agencies with which they
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consulted. PRO: May give us signal beyond (often unreliable) case
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reporting.
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- "Brute force approach": set up supervised problem(s) against cases
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(at various lags), through in all of the symptoms, see what allows us
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to best mimic cases. PRO: Should be well-correlated with cases; if
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trained against appropriately backfilled cases, it can give a "nowcast"
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of cases.
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