COVID19 Mortality Rate Analysis based on geographic region.

1. COVID-19 Data Analysis
Written on: 4/28/2020
Data Updated to: 4/27/2020

2. Disclaimer
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3. Introduction
▪ Country/Regional Data and Plots
▪ USA State Data and Plots
▪ USA Models
▪ USA Red vs Blue
▪ Questions
▪ Interesting Metrics from Data

4. REGIONAL ANALYSIS
Breakdown of data by Political/Geographic Regions

5. Deaths by Country
>100 Deaths

6. Deaths by Country
Deaths per 100k of Population
EU in Yellow & US in Blue
A flat line, like China’s line,
means no increase in
deaths.

7. Deaths by Region/Country
>100 Deaths

8. Death Trends by Region
Deaths per 100k of Population

9. UNITED STATES
Analysis the State of affairs in the States of the

10. Deaths by State
>100 Deaths

11. Death Trends by State
Deaths per 100k of Population

12. Mortality Rate (MR) and the Flu
▪ Mortality Rate is defined as:
▪ Number of Deaths per 100,000 people
▪ A Mortality Rate of 10 in the US population of 330MM people corresponds to
33,000 deaths
▪ Note the historical plot of the MR of influenza from 1930 to 2004.
▪ Link to article in the references; it’s a good read
▪ I found this conclusion relevant:
▪ The considerable similarity in mortality seen in pandemic and non-pandemic influenza
seasons challenges common beliefs about the severity of pandemic influenza. The
historical decline in influenza-classed mortality rates suggests that public health and
ecological factors may play a role in influenza mortality risk. Nevertheless, the actual
number of influenza-attributable deaths remains in doubt.
▪ I utilized the estimated 2019 population of various regions (i.e. New
York, USA, Michigan, etc) and deaths attributed to COVID-19 in those
regions to compare the variance in mortality rates across various
regions of the country
▪ By using Mortality Rate, it normalizes the data to aid in better regional
comparisons
▪ Why normalize it?
▪ Different regions or states have different public policy, population density, etc that are
impacting their states’ mortality rates for COVID19
See References #4

13. How to read Statistical Model
▪ The top plot is cumulative COVID-related deaths
over time
▪ Attempts to estimate the total deaths from COVID19
▪ This model is better for showing the efficacy of the
best fit model
▪ It also shows how the impact of changes in behavior
can impact total deaths.
▪ Note, New York early on had an MR tracking close
to 100, which subsequently made a step-change to
~70
▪ The bottom plot is daily death rate over time
▪ This plot better shows the peak death rate as well as
when to expect improvement
▪ The models utilize mortality rate as the variable
▪ Curve fit model, estimating mortality rate based on fit
to actual data and start times for the region
▪ Mortality Rates of 20, 40, and 100 are used to
bracket actual data, and for regional comparison
Reference #3 is the white paper for the numerical model used to generate all the models

14. USA Actual vs Models
Cumulative Plot
Note the impact NY has on the
Mortality Rate of the USA.

15. USA Actual vs Models
Daily Plot Remember the models are base on
population. MR of 40 for “USA” is
higher than for “USA without NY,”
that is why you see two model lines

16. New York Actual vs Models
Cumulative Plot

17. New York Actual vs Models
Daily Plot
Why is there a big spike on Day 34?
As reported by many news agencies and the
President, the states are going back and
reporting to the CDC “probable” deaths of the
China Virus. The daily deaths after April 15th
includes both current deaths + probable
deaths.

18. New Jersey Actual vs Models
Cumulative Plot

19. New Jersey Actual vs Models
Daily Plot

20. Connecticut Actual vs Models
Cumulative Plot
Why set the Mortality Rate High Range @
100?
As a comparison of what this state would look
like if it was like New York

21. Connecticut vs Models
Daily Plot

22. Massachusetts Actual vs Models
Cumulative Plot

23. Massachusetts Actual vs Models
Daily Plot

24. Louisiana Actual vs Models
Cumulative Plot

25. Louisiana Actual vs Models
Daily Plot

26. Michigan Actual vs Models
Cumulative Plot

27. Michigan Actual vs Models
Daily Plot

28. Penn Actual vs Models
Cumulative Plot

29. Penn Actual vs Models
Daily Plot

30. Red vs Blue Deaths (2016 Presidential Election)
Red is Republican and Blue is Democrat
Added Daily Change in MR on right y-axis to show how fast the curve is flattening (zero deaths)

31. Red vs Blue… Why
▪ The Trump Administration (and likely the Constitution) left many decisions to
the States, including decisions to shut down commerce
▪ States like CA shut their whole state down at once; other states, like Texas, left
it up to the cities and counties
▪ This brings up an interesting political dynamic that could impact different
regions at different rates
▪ There has been some interesting information coming out around
Hydroxychloroquine treatments, but some state bureaucracies have either
held up the use of the medicine or limited its use
▪ Mortality Rate is affected by several variables including public policy and
population density

32. Questions I am trying to answer
▪ Is this world coming to an end? (No)
▪ How does this compare to the flu?
▪ When will this end?
▪ What is the effect of different public policies?
▪ Can other states, regions, countries learn from the variability in public
policy in the US?
▪ How bad could it have been without stay-at-home policies?
▪ Is social distancing enough?
▪ Do we have to shut the economy down next time?

33. Interesting thoughts on the Data
▪ NY has a more than ten times higher mortality rate than the rest of the US
▪ With a Mortality rate of 100 in NY:
▪ What caused it to be so high?
▪ What causes the variability in the Mortality rate trends?
▪ Is public policy to blame for both failure and success?
▪ Trump Admin estimated 100k to 250k deaths in the US, this model is currently estimating 45k to 50k deaths in the
US
▪ Where did they get 100k to 250k? Overestimating early trends in NY and applying it to the entire US?
▪ Why are we seeing the trends change upwards after what appeared to be a peak?
▪ This appears to be a change in policy of submitting probable deaths to the CDC for past cases starting April 15th. I have updated all the models
capture the increase MR.
▪ Red States have ~22% of active COVID19 Cases, but ~15% of deaths
▪ Population Density has an impact here but possibly one or more of the following do too:
▪ Public Policy
▪ Population behavioral trends
▪ homeless population
▪ Avg age of population
▪ And many more that I probably don’t understand

34. Next Steps
▪ We are updating the slides daily
▪ I plan on adding some models for Europe and S. America
▪ If you want to see something, send us an email

35. Comments, Questions & Opinions are Welcome
contact@radiusenergysolutions.com

36. Country Region (group) Country Region Country Region (group) Country Region Country Region (group) Country Region Country Region (group)Country Region
Zimbabwe Andorra Andorra United Kingdom Moldova Moldova
Zambia Australia Australia Switzerland Monaco Monaco
Western Sahara Canada Canada Sweden Mongolia Mongolia
West Bank and Gaza Trinidad and Tobago Spain Montenegro Montenegro
Uganda Saint Vincent and the Grenadines Slovakia New Zealand New Zealand
Togo Saint Lucia Romania Norway Norway
Tanzania Saint Kitts and Nevis Portugal Papua New Guinea Papua New Guinea
South Sudan Jamaica Poland Russia Russia
South Africa Haiti Netherlands San Marino San Marino
Sierra Leone Grenada Malta Vietnam
Seychelles Dominican Republic Luxembourg Thailand
Senegal Dominica Lithuania Singapore
Sao Tome and Principe Cuba Latvia Philippines
Rwanda Barbados Italy Malaysia
Nigeria Bahamas Ireland Laos
Niger Antigua and Barbuda Hungary Indonesia
Namibia Panama Greece Cambodia
Mozambique Nicaragua Germany Burma
Mauritius Honduras France Brunei
Mali Guatemala Finland Venezuela
Malawi El Salvador Estonia Uruguay
Madagascar Costa Rica Denmark Suriname
Liberia Belize Cyprus Peru
Kenya Uzbekistan Croatia Paraguay
Guinea-Bissau Kyrgyzstan Bulgaria Guyana
Guinea Kazakhstan Belgium Ecuador
Ghana China China Austria Colombia
Gambia Cruise Ship Cruise Ship Fiji Fiji Chile
Gabon Ukraine Holy See Holy See Brazil
Ethiopia Slovenia Iceland Iceland Bolivia
Eswatini Serbia Japan Japan Argentina
Eritrea North Macedonia Korea, South Korea, South Sri Lanka
Equatorial Guinea Kosovo Liechtenstein Liechtenstein Pakistan
Cote d'Ivoire Czechia Mexico Mexico Nepal
Congo (Kinshasa) Bosnia and Herzegovina United Arab Emirates Maldives
Congo (Brazzaville) Belarus Tunisia India
Chad Albania Syria Bhutan
Central African Republic Sudan Bangladesh
Cameroon Somalia Afghanistan
Cabo Verde Saudi Arabia Taiwan* Taiwan*
Burundi Qatar Timor-Leste Timor-Leste
Burkina Faso Oman US US
Botswana Morocco Turkey
Benin Mauritania Israel
Angola Libya Georgia
Lebanon Azerbaijan
Kuwait Armenia
Jordan
Iraq
Iran
Egypt
Djibouti
Bahrain
Algeria
SE Asia
South America
South Asia
W Asia no ME
EU+CH+UK
Middle East
Africa
Carribean
Central America
Central Asia
Eastern Europe No EU
Countries by Region

37. Reference
1. Data stream is from Tableau via a data.world connection
2. They are indicating that the raw data is from John Hopkins University
3. https://www.hindawi.com/journals/mpe/2012/124029/ Cumulative
Distribution Model
4. Trends in Recorded Influenza Mortality: United States, 1900–2004
1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2374803/
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