Interactively compare trends of COVID-19 cases on a dateline.

*How to I make selections?* Double-click the entry in the legend. Then *click* the current and worst trend entry, or any other entry. Double-click on your entry to bring everything back up.

Viral infections always grow exponentially in the early part of the infection before it saturates the population. This gives rise to sharply rising trends. It is difficult to discern the exact rate of expnential growth of these curves, and log analysis is required as in the following graph.

*What is a log graph?* Values on the vertical axis increase in proportional chunks - 1, 10, 100, ...

*Why log graphs?* In a log graph, exponential growth appears as a straight line. The slope of this straight line gives the speed of infection - the steeper it is, the faster the infection spreads.

*What is important about this?* Although New Cases fluctuate much more than Total Cases, it also behaves as an exponential in the early part of an infection. New Cases is an indicator of the number of patients that will need to be hospitalized, where ~20% of cases will require hospitalization.

Cases divided by the population, multiplied by 100,000

Comparing cases per 100,000 provides a better level of comparison.

Cases divided by the population, multiplied by 100,000

The most effective comparisons between different exponentials is to compares the slopes of the straight line segments on a log graph.