# Dennis' COVID-19 Visualizer

### Welcome!

I built this tool to better visualize COVID-19 data through the lens of simple mathematics. I hope you find the graphics helpful when trying to get a grasp on this complex situation. This is a good faith project, please use with discretion

#### Legal Stuff

##### TL;DR

Basically, I'm not a medical professional nor an epidemiologist. The data shown here are entirely sourced from public sources listed below. This page does NOT represent my opinion, it's merely a reflection of a standard set of mathematics applied to the set of publicly available data. All projection and forecast shown on this page are purely results from mathematical computation.

#### Data Source and Credits

The data is sourced directly from JHU's daily reports CSSEGISandData/COVID-19 the additional computation on this dataset is performed on my server on a daily basis

Credits to the following amazing tools and frameworks

Materialize Chart.js Moment.js JQuery MathJax

Please email me for ideas, bugs, improvements, etc...

#### Total Cases To Date

Data As Of

• functionsMethodology

#### Fitted Logistic Curves

*For beginners check out this Video by 3Blue1Brown on the math behind pandemic growth

The total confirmed, deaths, and recovery curves of a pandemic roughly follow a logistic curve over its course in a community. Of course, there are a lot more factors that come into play when modeling an pandemic. This study makes NO attempts to account for explicit factors.

Each curve in the dataset is fitted to the following ideal logistic model

$$f(x;A,\mu ,\sigma)=A[1-\frac{1}{1+e^{\alpha}}]$$

Where

$$\alpha=\frac{x-\mu}{\sigma}$$

In this model, A accounts for the amplitude of the curve, essentially predicting the ceiling of the pandemic

𝜇 represents the center of the logistic curve, it marks the estimated inflection point of the pandemic

𝜎 represents the growth factor of the curve, aka the "sharpness"

Each curve is fed through a least-square gradient decent optimizer against the ideal model to compute the parameters. Note that not all datasets can be successfully fitted, the optimizer can't converge on those datasets with too little data points or non-ideal shape.

I decided to plot ahead the fitted curve 10 days ahead of now so you could visualize the current trend. Please also note that this is NOT a prediction of the course of the pandemic, it's merely a rough forecast from the current known data points. No one can predict the future.

#### Derivatives of Logarithmic Data

During exponential growth, the log plot shows a linear growth. The slope of the log chart therefore informs the speed of the growth. The slope chart s(t) is defined as

$$s(t)=\frac{\mathrm{d} }{\mathrm{d} t}log_{10}(y)$$

Simalar logic folows. In a polynomial function, the second derivative indicates concavity. The second derivative on the linearized data gives us the concavity of the growth curve. Positive value on this curve means accelerating spread, while negative value points to decelerating spread. The concavity chart is defined as

$$c(t)=\frac{\mathrm{d^2} }{\mathrm{d} t^2}log_{10}(y)$$

### Active Cases "The Curve"

• 1 in 7  chance: Getting the flu this year

• 1 in 50  chance: A person has red hair

• 1 in 186  chance: Being audited by the IRS this year

• 1 in 215  chance: A stranger is named Kevin in the US

• 1 in 500  chance: A baby is born with extra fingers or toes

• 1 in 563  chance: Catching a ball at a major league ballgame

• 1 in 1296  chance: Rolling all 6s on 4 dice

• 1 in 2380  chance: Dying from a stroke this year

• 1 in 4644  chance: Being injured while using a chain saw this year

• 1 in 6000  chance: A coin toss lands on its edge

• 1 in 10000  chance: The next bill you touch is a counterfeit

• 1 in 12000  chance: Finding a pearl in an oyster

• 1 in 25000  chance: Assaulted by firearm in the US this year

• 1 in 50000  chance: You have ALS

• 1 in 119012  chance: Getting executed legally in lifetime

• 1 in 218106  chance: Die from lightning strike in lifetime

• 1 in 649740  chance: Getting a royal flush in your first hand of poker

• 1 in 662000  chance: Winning an Olympic gold medal in lifetime

• 1 in 3748067  chance: Getting attacked by a shark in lifetime

• 1 in 12100000  chance: Becoming an astronaut in lifetime