A look at two different Datasets (infection data & mobility data to make some predictions about Corona Virus. The main takeaways: 1. Without a vaccine Corona is here to stay for 18 months till herd immunity. We need to have cyclical lockdowns of 2 weeks lockdown 6 weeks opening. 2. The structure of a city dictates whether a lockdown works or not. Rural and Nature heavy cities like Utah can't follow the same strategy like NY or Manhattan.

1. Lockdown
Optimization
How do we optimize
isolation

2. Agenda
- Description of the problem
- Two pronged approach
- Where to lockdown?
- How long to lockdown
- How long to lockdown?
- Data
- Methodology
- Assumptions
- Models and Findings
- Where to lockdown?
- Data
- Methodology
- Assumptions
- Models and Findings
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3. What is the problem?
Two ways to solve a virus
- Vaccine
- Herd Immunity
Vaccine
- At least two years until most of the population is vaccinated
Herd Immunity
- Virus dies down once 60% of the population has been exposed to the virus
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4. Epidemiology basics
SEIR
Model
s = S/N,
i = I/N,
r = R/N
ds/dt = −βsi
di/dt = βsi − νi
dr/dt = νi
where, β effective contact rate ν removal rate
(R0) = β/ν
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5. How Long to Lockdown?
There is a human cost to the lockdown
“Based on social security data for the United States, Sullivan and von Wachter (2009)
estimate that increased mortality rate due to unemployment can persist up to 20
years after the job loss and lead to an average loss of life expectancy from 1 to 1.5
years.” (IMF, 2010)
So we can measure the extrinsic costs of locking down based on the economy
slowing down.
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6. How Long to Lockdown?
If countries have to keep switching between Lockdown and Opening up, what is
the optimum balance to minimize loss of life?
A fixed cyclical pattern might be the new normal and easier to manage societal
fear and anxiety.
Used RO and R1 values from other modelers
Built 2 year model projects for the US
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7. How Long - Example: 2 weeks open 3 weeks lockdown
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● Over the course of 2 years,
under this scenario approx
8.4 m people will die
● Accumulated death does not
rise linearly. Between day
200-350 is the sharpest
increase
● Highest daily new dead
value is 83.000

8. How Long - Example: 2 weeks open 3 weeks lockdown
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● In the US there will be a
total of approx 138 million
Covid-19 infections
● Total infections does not
rise linearly. Between day
150-350 is the sharpest
increase
● Highest infected at one
point in time is 19.5 million

9. How Long - Example: 2 weeks open 3 weeks lockdown
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● Whilst approx 8 m people
will die of Covid-19, only
800.000 will die from
unemployment
● Accumulated death does not
rise linearly. Between day
200-350 is the sharpest
increase
● A high proportion of human
lives by job is occurs early

10. Strategy comparison: Total death count over time
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● The 2 weeks open 3
weeks lockdown
strategy has the highest
total death toll.
● 2 weeks open 6 weeks
closed strategy has the
lowest number of dead
● The 2 weeks 6 weeks
closed strategy is the
ideal solution if the goal
is to minimize death and
is therefore the safest.

11. Strategy comparison: New daily dead
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● All 3 strategies
yield high death
figures at the
beginning of the
implementation of
the strategy.
● For the 2 week
open 3 weeks
closed strategy one
can see a spike in
fatal Covid-19
cases after every 2
week open period

12. Where to Lockdown?
Communities throughout the world have relied upon NPIs such as physical
distancing to slow the spread of the COVID-19 virus.
But were rates of mobility decreasing and rates of distancing increasing?
Where have physical distancing measures ranging from school and restaurant
closures to stay-at-home orders in countries affected the rate of infection?
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13. US-City Analysis
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14. Data
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Mobility Data
Geographical Granularity: County, State
Key Variables:
○ Retail and Recreation
○ Parks and Recreations
○ Groceries and Pharmacies
○ Workplaces/Office
○ Public Transit
○ Residential Areas
Cases Data
Geographical Granularity: County, State
Key Variables:
○ Confirmed cases
○ Cases per day
R-Values Data
Geographical Granularity: County
Key Variable:
○ R-Value

15. Data Pre-Processing
15
Melt the dates
into column
„Date“
Cases Data Mobility Data R Values
Source: John Hopkins
University
Source: Google &
Apple
Source: covid19-
projections.com
Calculate new
cases per day
Filter US
Create time
series
Filter US
Create lagged
dates (+12 d)
Create time
series
Join by
State/County/
Date
Aggregate
using weighted
average
Join by
State/Date

16. New York City, NY
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Megacities were
obedient of the
lockdown rules
More policing and
enforcement
resources
Highly urban cities
have less accessible
parks

17. Seattle, WA
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Some major cities
still had accessible
mobility to parks and
places of recreation
The mobily trend of
parks is reflected in
the trend of New
Cases

18. Union County, NJ
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More difficult to
police and enforce
rules in rural areas
The lockdown
effective April 20 has
a positive effect upon
the New Cases

19. Salt Lake City, UT
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Some cities never
experienced a decline
in recreational
mobility
A consistent average
New Cases amount is
a consequence

20. Regression Analysis
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21. Regression analysis
Independent variable:
● Post-mitigation R0
Dependent variables:
● Pre-mitigation R0,
● Residential percent change,
● Parks percent change,
● Grocery and pharmacy percent change.
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22. Regression analysis
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Significant variables: Pre-mitigation
R0 & Residential percent change.