Updated: 2020-10-28 11:00:48 PDT

Original version created 2020-09-21. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease varies widely between political jurisdiction. There have been several point observations, sich has here.

However, wider correlations between politcal speech and governance have not been drawn.

This analysis seeks to fill partially that gap. It includes:
1. Analysis of COVID spread by poitical leadership affilation.
2. What-if analysis based on critical turning points in disease evolution.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Statistics

Total Cases and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Cases and Deaths Per 100k Population

This is similar data with the data normalized to the population. The conclusions don’t change dramatically, but do reflect the sparser population of Republican led States.

State Analysis

Trend Chart of Per Capita Deaths

Grouped by Governor Party Affiliation. Hypothesis is that political leadership plays a large role in influencing COVID outcomes.

We can see distinct differences between the populations, with a few exceptions.

Total Cases per 100k Population by State

How about total cases? Of the Top 12 states, only one is Demoncratically led.

Total Deaths by State Per 100k Population

Deaths by state show a different trend. The High “Death per capita” states are dominated by the early outbreak last March and April. Of the Top 12, it’s an even split.

Deaths by State since 2020-07-04 Per 100k Population

Many State Governors relaxed staty-at-home orders as early as Mid May. Subequently, cases surged, resulting in a spike in deaths. Again, 10 of the Top 12 are Republican-led.

Effective Reproduction Rate \(R_e\)

The effective Reproduction Rate \(R_e\) shows clear differences between Replublican and Demoncratic-led States.

National Maps

The key indicator for disease forcasting is the Effective Reproduction Rate \(R_e\), which is a measure of how many new cases each existing case of disease creates.

When a lot of people are sick in a population without mass-immunity you want \(R_e \ll 1\) or \(log_2\)(\(R_e\)) < 0) to acheive negative disease growth.

After achieving negative growth, the next phase of recovery is maintaining consistently lower levels of disease to a level where disease cases can be micro-managed. There’s no clear agreement on how few that is, but I’ve seen estimates as low as 500 cases per day across the US (about 0.16 cases per 100k population).

An estimate of the disease “toll” is the number of officially tallied deaths. It is fully agreed this vastly underestimates that actual cost of the disease. Death counts are almost certainly underestimated and this does not reflect any long lasting health effects on those who recover.

State Level Data

Analysis of Total COVID Cases Per Capita by Governor’s Party Affiliation

Analysis of Daily COVID Cases Per Capita by Governor’s Party Affiliation

Correlation to House of Representatives

Covid cases per capita.

Covid deaths per capita.

County Data

While the State-Level Data tell as remarkable story, it is also interesting to look at County-level data. COunty level data on presidential elections are downloaded from here


Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Wash Your Hands!
Socially Distance!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 532.7 seconds to compute.
2020-10-28 11:09:41

version history

Today is 2020-10-28.
37 days ago: Branched from Regional COVID ANALYSIS.
37 days ago: Branched from Regional COVID ANALYSIS.
29 days ago: Added regression at county level.
17 days ago: Added density histograms of vote versus recent death rate per 100k.
16 days ago: Added link to this data on recent House elections.
4 days ago: modified state governor plots and added House Results analysis.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.