All data presented here cover the first 3 years of the pandemic (2020 - 2022).
Half of the following statements are false. Find out which by interacting with the visualisation dashboard below.
Cases numbers are the confirmed cases, so the number of people who have tested positive for COVID-19. Since the begining of the pandemic, we have known that since everyone was not constantly being tested, the confirmed cases figures must be undervaluing the true spread of the virus. The question is, by how much? A good indication is to look at how many tests have been done for every positive result. If we have done 100 tests for every positive one (100 ratio), our total numbers are probably much more accurate than having done 2 tests for each positive one (2 ratio). Ok, so I can look at the total confirmed cases, and then on the positive ratio, come up with a fancy calculation formula and then I’ll know. Getting closer, but not there yet. Testing is carried out very differently in different parts of the world. Some countries count the number of people tested, others the number of samples tested, and for many countries we don’t even have data. Some countries test widely, some narrowly in high-risk populations, others use track and test systems and so on. All these different approaches cannot be accurately quantified at this point, but some data is better than none.
Death data might be a better metric to gauge the impact of the pandemic at a certain time, but again it is not perfect. COVID-19 deaths are counted slightly differently in different places. Sometimes even in the same place at different times. For instance, in the UK, up until the 12 of August 2020, all deaths following a positive COVID-19 test were counted in the COVID-19 death sum. Then, officials decided to only include deaths occurring up until 28 days after the initial positive test cutting down the total number of deaths by over 5 thousand. In many other countries, COVID-19 needs to be specifically listed in the causes of death of a patient. And of course, COVID-19 is rarely the only cause of death. This is not a new problem. Terminally ill people are very often challenged on multiple fronts.
Another way to look at death data is to simply sum up all deaths occurring over a month (or week) and compare the total monthly (or weekly) sums with previous years. The gross simplification here is that any excess deaths should correspond to COVID-19’s impact. True, but impact is not just people directly dying from COVID-19, but also from the side effects of the pandemic. Like hospitals being full, patients avoid visiting them out of fear and so on. Unfortunately, this data is missing for many countries, including China, India, Indonesia and Pakistan, which combined constitute about half of the Earth’s population.
To get an idea of how badly the healthcare system was hit, we can look at the number of people who were hospitalized or even put into ICUs (intensive care units) due to COVID-19. We have 2 different kinds of metrics here, one about the total number of patients in hospitals (or ICUs) at any given time, and another about new admissions. I am showing data for the first kind of data where possible, but you might notice that we only have new admissions data for some countries, others only report on hospitalizations or ICUs but not both, and for many countries we do not have any relevant data at all.
Of course, all of the above were impacted by the different policies each country imposed on its citizens and borders, adding another level of obscurity when trying to compare these measures. These policies were very diverse and hard to quantify, so in order to provide some visual representation here (as seen on the map chart when selecting "movement restrictions imposed") I have made two simplifications. First, I have only focused on movement restriction policies and in this way ignored a wide variety of other policies including mask wearing, working from home, closure of schools and businesses, and restriction of gatherings. This is not because I have proof that movement restriction policies were more effective in battling COVID-19 than other policies, it is simply because I found these policies to be the most severe and hard to comply to. Second, I am simplyfying these responses by grouping them in 4 categories: "generall lockdown", and "general curfew", when a lockdown or curfew was imposed on the whole country; "non general restrictions", when a movement restricion was only imposed on part of the population, like a localised lockdown or curfew; and "test/vaccination based restrictions" when restrictions were only imposed on people without a proof of vaccination and/or test, most commonly imposed on travel.