COVID-19 stats

What is all this?

The purpose of this website is to track the development of COVID-19 pandemic worldwide and in particular countries in order to have a better idea of what's going on and what to expect. There are many other trackers with different focuses: here we focus on understanding the dynamics of the infection numbers in different countries.

The website is updated daily and is always showing yesterday's data (we think that it's good enough at this point).

The list below explains in more detail what each graph and parameter means and how things are calculated.

New cases
The number of officialy reported infected people based on JHU data (see below). Everyone, who has been officially diagnosed with COVID-19 will be counted in this number. It's almost certainly below the actual number of infected people because of delays in reporting and not everyone being tested, but this is the best basic data that we have.
Deaths
The number of officialy reported deaths from COVID-19 based on JHU data.
Recovered
The number of people who have recovered from the disease. Some part of this is estimated (at least in some countries) because people with light symptoms are not hospitalized and their condition is not always tracked.
Active cases
The difference between New cases and the sum of Deaths and Recovered. This is the number of people that are currently sick.
X per day
The change rate of X: the difference between todays number and the number of the previous day. This is a discrete equivalent of derivative of X.
X per day change
The change in the rate of change of X: the difference between todays rate of change and yesterday's rate of change. This is similar to 2nd derivative of X.
Implied R
Transmission rate, calculated based on the New cases per day of the last two weeks. We take a weighted average of the new cases in the last days (centered between 5 and 6 days ago) and divide the today's new cases by that number. It shows how many new people were infected by one person, who was officially counted as infected about a week ago. When R is above 1, the number of infected people grows exponentially, when it's below 1, it decays.
Period
The period control allows you to see all data or only last month, last two weeks or last week.
Smoothing
The data as is contains lots of jumps. Some of these jumps are real, some are artifacts of reporting. By averaging the numbers over time we can get a less jittery curve where the main trends are easier to see. Higher levels of smoothing result in more averaging. Note: Smoothing is always applied to the main statistic and the rate of change, rate of change of rate of change and R are calculated from smoothed data.
SmoothType
There are different ways to average over time: SMA is a simple moving average, where the numbers of previous L days are added to today's number and divided by L+1 (where L is the smoothing level); EMA is exponential moving average, that starts with the original number and then each day divides yesterday's EMA by (L+1)/L and adds today's number divided by L+1; FWD repeatedly averages each day with the average of the day before it and the day after it (except for the first and the last) — it uses future data, so it's less backwards-looking but it also overweighs the last data point.
Stacked
The graphs can stack countries data on top of each other. This makes it easier to see the total amount (over all countries) and how each country relates to the total. We don't stack the change in the rate of change because these numbers are often negative and adding them looks confusing. We also don't stack the implied R because transmission rates are not additive (so R of the whole is not equal to the sum of R's of the parts).

More information