Data as of 01/09/23

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Things you can do on each graph

• Hover your mouse over a trend line to find which location it refers to.
• Single click on a location in the legend to show and hide that location's trend line.
• Double click on a location in the legend to show only that location OR show ALL locations in that graph.
• Click and drag on a graph to zoom into a particular area.

Data Sources

• Covid-19 cases and deaths data from https://github.com/nytimes/covid-19-data.
• Population and Land Area data from random places I found information from the internet. Most was sourced via US Census data and tend to be at least 10 years old.

Death Risks by Age Group

New cases, Daily Deaths and 2-week Mortality for US

This is a combination plot showing a 7 day moving average of number of new cases per day, number of deaths per day, and a 2-week mortality rate for the US overall. Note that the scale for the mortality rate appears along the right hand side of the graph, while the scale for cases and deaths is along the left.

The 2-week mortality rate is calculated by taking the total number of deaths that occurred by each date, and dividing it by the total number of new cases that occurred 2 weeks earlier. Therefore, the desired line should be trending downward indicating that the number of deaths compared against the number of cases found is going down. This could be due to more people being tested and testing as positive while the absolute mortality of covid-19 stays constant, but it also could be due to better treatment of those infected. Note that a .03 mortality rate is believed to be about 30x higher than that of influenza. But also remember that disregarding age, a .03 mortality rate implies that if you are infected, you have a 97% chance of surviving.

New Cases and Deaths by State

New Cases and Deaths in Locations Interesting to Me

State New Cases and Death Breakdowns

State and County Total cases

The simplest count is just a total number of cases per state and then per interesting county. Nothing more than what each state is reporting as new cases on a daily basis.

State and County cases adjusted for population

Total cases is one thing, but to better get a sense of how different states and counties may be handling the virus compared to others, you can adjust the graphs to account for the number of people who live in each state. A state that has 100,000 people vs one that has 8,000,000 people should obviously have far fewer total cases because they have 80x less people. By factoring in the population, a state or county with a low population may show a slope as high as other states with far more people. This would indicate that the lower population state isn't likely effectively quarantining nearly as much as a higher population state.

State cases adjusted for population density

Each state and county obviously has a population and an area in which this population lives. *Pretend* for a moment that Texas only has 1,000,000 people total. Also *pretend* that Rhode Island has 1,000,000 people. The land area of Rhode Island is much, much smaler than that of Texas. So, if both states have a total of 5,000 covid-19 cases over the same number of days since the outbreak began in that state, you can say with high confidence that the people in Texas are likely not effectively managing to stay as safe as they are in Rhode Island.

This graph factors in this consideration for comparison between states and counties

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City total cases adjusted for population

You can do the same types of adjustments as we did for state and county at the city level. A city that has 100,000 people vs 8,000,000 people will obviously look far better with regard to total cases because they have 80x less people. By factoring in the population of a city, this is difference is accounted for.

City total cases adjusted for population density

Same trends as described for state cases adjusted for population density, but applied at the city level instead. The intent of this graph is to discount the consideration that some cities growth rates are so fast because those cities are so densely populated. This was a common explanation as to why New York was growing so much faster than other cities. Though even when taking density into account, New York's trend still beats all others, but other cities are much closer!

City deaths adjusted for population density

See description above concerning cases adjusted for population density. This is the same, but is about deaths, not just cases.