Why does this turn negative at 40 timesteps?
!conda install -y pandas matplotlib || pip install -y pandas matplotlib
%matplotlib inline
import pandas as pd
nrange = range(100)
df = pd.DataFrame(index=nrange)
df['cases'] = 3**df.index
df.tail()
df.plot()
df['casesfmt'] = df['cases'].apply(
lambda x: '{:,}'.format(x))
...
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On Tue, Mar 24, 2020, 9:49 AM Wes Turner <wes.tur...@gmail.com> wrote:
> https://en.wikipedia.org/wiki/Epidemiology
>
> https://en.wikipedia.org/wiki/Mathematical_modelling_of_infectious_disease
>
> https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology
>
> - Why we wear masks when we go outside
>
> https://en.wikipedia.org/wiki/Technological_singularity
>
> https://en.wikipedia.org/wiki/Technology_adoption_life_cycle#See_also
>
> On Tue, Mar 24, 2020, 9:36 AM Wes Turner <wes.tur...@gmail.com> wrote:
>
>> A thread to share [collections of] resources, curriculum ideas, etc.
>> about and for during the COVID-19 epidemic
>>
>> Lots of analyses, some data, some helpful contributions, lots of people
>> learning about exponential growth
>>
>> One video I saw mentioned that a person normal flu infects about 1.3-1.4
>> other people, but COVID-19 is closer to 3; so what's wrong with this
>> analysis?
>>
>> 1**1.3
>> 1**3
>>
>> 1.3**n
>> 3**n
>>
>> '{:,}'.format(7e9)
>> 1*(3**x) = 7e9
>> # solve for x with logarithms
>>
>> # Where is the limit with which controls?
>>
>>
>> ## Notebook idea
>> Growth curves: polynomials of degree 0 through 10 ('desic'), exponential,
>> logistic
>>
>> - Exponential growth and epidemics
>> https://youtu.be/Kas0tIxDvrg
>>
>>
>> ## Prompt re: positive, helpful, constructive tone; morale; and amateur
>> data science
>>
>> Here's a prompt for students and teachers alike:
>> Respond to this re: amateur data science, tone, attitude, responsibility:
>> https://www.reddit.com/r/datascience/comments/fm17ja/to_all_data_scientists_out_there_crowdsourcing/
>>
>> ```quote
>>
>> FWIU, there are many unquantified variables:
>>
>> - pre-existing conditions (impossible to factor in without having access
>> to electronic health records; such as those volunteered as part of the
>> Precision Medicine initiative)
>> - policy response
>> - population density
>> - number of hospital beds per capita
>> - number of ventilators per capita
>> - production rate of masks per capita
>> - medical equipment intellectual property right liabilities per territory
>> - treatment protocols
>> - sanitation protocols
>>
>> So, it **is** useful to learn to model exponential growth that's actually
>> logistic due to e.g. herd immunity, hours of sunlight (UVC), effective
>> containment policies.
>>
>> Analyses that compare various qualitative and quantitative aspects of
>> government and community responses and subsequent growth curves should be
>> commended, recognized, and encouraged to continue trying to better predict
>> potential costs.
>>
>> (You can tag epidemiology tools with e.g. "epidemiology"
>> https://github.com/topics/epidemiology )
>>
>> Are these unqualified resources better spent on other efforts like
>> staying at home and learning data science; rather than asserting
>> superiority over and inadequacy of others? Inclusion criteria for
>> meta-analyses.
>>
>> - "Call to Action to the Tech Community on New Machine Readable COVID-19
>> Dataset" (March 16, 2020)
>>
>> https://www.whitehouse.gov/briefings-statements/call-action-tech-community-new-machine-readable-covid-19-dataset/
>>
>> > “We need to come together as companies, governments, and scientists
>> and work to bring our best technologies to bear across biomedicine,
>> epidemiology, AI, and other sciences. The COVID-19 literature resource and
>> challenge will stimulate efforts that can accelerate the path to solutions
>> on COVID-19.”
>>
>> - https://www.kaggle.com/tags/covid19
>> - "COVID-19 Open Research Dataset Challenge (CORD-19): An AI
>> challenge with AI2, CZI, MSR, Georgetown, NIH & The White House"
>>
>> https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
>> -
>> https://en.wikipedia.org/wiki/Precision_medicine#Precision_Medicine_Initiative
>> ```
>>
>>
>> ## NIH FigShare instance
>>
>>
>> https://www.niaid.nih.gov/news-events/rapidly-share-discover-and-cite-covid19-research-results-generated-niaid-awards
>>
>> > NIH is assessing the role of a generalist repository for NIH-funded
>> research and has launched the NIH Figshare instance, a pilot project with
>> the generalist repository Figshare
>>
>> You can archive a tag of a [topic-labeled] GitHub repository [containing
>> notebooks] with FigShare.
>>
>>
>> ## Resource Collections
>>
>> https://github.com/topics/2019-ncov
>>
>> https://github.com/topics/covid-19
>>
>> https://github.com/topics/epidemiology?l=python
>>
>> https://github.com/topics/epidemiology?l=jupyter+notebook
>>
>> Objectively-scored Kaggle competitions:
>> https://www.kaggle.com/tags/covid19
>>
>> https://github.com/soroushchehresa/awesome-coronavirus
>>
>>
>> On Tue, Mar 24, 2020, 6:35 AM kirby urner <kirby.ur...@gmail.com> wrote:
>>
>>> Awesome!
>>>
>>> If you do any kind of Youtube on this specific SIR model I hope you'll
>>> link it from the cell and share it here.
>>>
>>> I see some Youtubes like that already (SIR models, including in Python),
>>> but everyone codes a little differently.
>>>
>>> I'd like to go back to high school and do it all again from a student
>>> perspective, now that the curriculum and tools are so vastly different.
>>>
>>> High school should be for any age, and one keeps going back every 10
>>> years or so. Learn it the new way.
>>>
>>> Kirby
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>>
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