Covid TimeMap visualizes the most relevant Covid-19 data around the globe and across time.
Compiling Data Click 
and 
buttons for navigation instructions and more info about the data.
Compiling Data Click and buttons for navigation instructions and more info about the data.
| All|% | Today | |
|---|---|---|
| Total Cases | ||
| Total Deaths | ||
| Active Cases | ||
| Daily Cases | ||
| Daily Deaths |
| All|% | Today | |
|---|---|---|
| Total Cases | ||
| Total Deaths | ||
| Active Cases | ||
| Daily Cases | ||
| Daily Deaths |
| All|% | Today | |
|---|---|---|
| Total Cases | ||
| Total Deaths | ||
| Active Cases | ||
| Daily Cases | ||
| Daily Deaths |
Calculated as the total accumulative cases for that day minus total cases from 14 days earlier multiplied by the survival rate (1 - deaths/cases) for each location. The 14 days is the estimated average of days from when a person tests positive to when they either recover from the illness or die. Earlier in the pandemic many sites were reporting active cases defined as total cases minus total deaths minus “confirmed” recoveries. Unfortunately, tracking proven recoveries is incredibly difficult and the data for “confirmed” recoveries is terrible, so reporting active cases in that way is very problematic. Instead, I believe this way of calculating active cases is the most meaningful way of conveying the current threat of the virus at any given time. It resembles a 7-day or 14-day moving average of daily cases, but incorporates the number of deaths into that equation. Visually it doesn’t look much different, but it shows less of a lag in the rise and fall of cases and I think it is a more accurate way of describing the numbers so it is easier to wrap your head around it’s importance. Basically, it is meant to represent the amount of people currently contagious at any given time.
Active Casesn = ( Casesn - Cases(n-14) ) x ( 1 - Deathn / Casesn )
The total accumulated cases/deaths reported for that day. Numbers do not always match the official total reported cases/deaths for each location as there have been numerous anomalies in the data for most countries, which detract from the animation and often present a less accurate visualization of the situation. These anomalies are typically a cause of an obvious single day misreporting, a change in the case/death reporting criteria, or a sudden release of cases/deaths accumulated from previous days. For single day anomalies, data is fixed by averaging the day before and the day after. For large sustained increases/decreases corresponding to changes in criteria or lagged reports, data is fixed by adding/subtracting that days difference proportionally to all previous days.
The 7-day moving average calculated as the average of that day’s new cases/deaths and the previous six days of data. For daily cases, this reflects the same trends shown using active cases, but does not take into account the death rate. I prefer the calculation for active cases as it better describes the current state of infectious cases and responds more directly to changes in reported cases.
Each data type is reflected as the share of the population for each reporting location. Those numbers are displayed as 1 in xx number of people (e.g., “1 in 50” cases means 1 person is infected out of every group of 50 people and “1 in 1.5K” cases means 1 out of every group of 1,500 people). I recommend always viewing the data as a per capita calculation, both here and from other sites, because it is absolutely necessary for making any kind of comparison of the impact of the virus between locations.
This setting overlays a map of population density across the globe extracting unpopulated areas from the map and reducing the opacity of low populated areas under 100 persons per km2. This overlay is important for visually correcting for the varying geographical sizes of reporting locations. Without this overlay, large low populated areas will be over represented within the visualization. The overlay currently doesn't show in satellite imagery mode.
As of now, social distancing and masks are our best defense, as individuals, against the ever growing spread of Covid-19. This can be scary given it is the same strategy used 100 years ago during the 1918 influenza, which claimed the lives of over 50 million people. However, today we have at least one tool that is far more advanced than anyone could have ever imagined in 1918...
Data... Massive amounts of data in all shapes and sizes.
Data is an incredibly powerful tool for managing the spread of a virus. The virus does not spread with ill will or intention. It relies on us as hosts to replicate and disperse itself. That means understanding our own behavior as individuals and as a society is critical for reducing the harm of this pandemic. That requires both the collection and analysis of quality data, as well as the broad understanding of it by the public. As a scientist and science communicator, I am amazed by the worldwide effort to collect, compile, analyze, and distribute information about this pandemic to the public by doctors, social workers, scientists, and journalists. Understanding that effort was my initial motivation for making this website. After weeks of religiously checking the New York Times Coronavirus Map, John Hopkins Covid-19 Dashboard, and Worldometers websites, I figured I should take a look under the hood to see where all this data comes from.
What I found was that it is easy to fall down that rabbit hole and get lost, but nevertheless there are countless people working tirelessly behind the scenes to compile the most reliable data possible. The data is definitely not perfect, but in Science that should always be a given. But what is so important for us to keep in mind as we digest this constant flow of statistics, scientific studies, and headlines is that all these numbers are meaningless without proper context. An article claiming another milestone of a total cases for a country means very little without context like population size, density, testing capacity, positive test rate, and how rapidly this number is changing.
That’s why I made this website. It is an attempt to visualize as much of the context as possible at once. Putting aside the numbers to highlight the broader patterns of impact seen in the colors changing over time and space. It helps link trends in cases and deaths relative to time, space, and population. You can click on any point and view the exact number for any given place and time, but the numbers are hidden by default as they are often a distraction and point of confusion.
I recommend starting with the animation of “Active Cases Per Capita” on the flat map to see the trends and pattern of how the virus has spread across the globe. Turning on the Population Density Overlay in the settings menu will help correct for large low populated areas of land. This softens the shock of seeing the bright red blocks of some of the larger less populated regions, and takes into account both population and population density. Next, turn on 3D Data and switch into the 3D Map mode to view the animation of totals deaths as a column extending out from each country over the Active Cases data. Total deaths is not a adequate paremeter for making comparisons between locations, but it is a good visualization for appreciating the profound impact this pandemic has taken on our world.
There are other important factors that I would love to include, like data on testing, age, class, and race, but the data on that information is not as widely available at the moment. If you have any questions or comments, please email them to covidtimemap@gmail.com
Buried Ice - Virtual Tour of Antarctic Dry Valleys
The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University
Socioeconomic Data and Applications Center(SEDAC)
* The New York Times and John Hopkins have compiled their data from a large range of sources for each location. I have tracked the data through many of these sources to better understand the variabilities between each, but I am using their compiled data for the starting point of this visualization. I recommend diving deeper into the sources listed through these websites if interested.
