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Event Horizon - COVID-19
Coronavirus COVID-19 Global Risk Assessment
As of , total cases of COVID-19 have been confirmed worldwide. Of these, cases are in Mainland China and cases are in other countries.
This site compiles results obtained from a computational / mathematical model for the expected global spread of the novel coronavirus that originated in the Chinese province of Hubei in December 2019. The foundation of the model is the worldwide air transporation network that connects over 4000 airports with more than 50000 flight routes.
The model accounts for not only the current distribution of confirmed cases in Mainland China but also airport closures that were implemented as a mitigation strategy, e.g. Wuhan Airport. For comparison, the initial import risk estimates pre-quarantine are provided here.
This network theoretic model is based on the concept of effective distance and is an extension of a model introduced in the 2013 paper The Hidden Geometry of Complex, Network-Driven Contagion Phenomena, D. Brockmann & D. Helbing, Science: 342, 1337-1342 (2013).
Global risk assessment - current synopsis
If you are uncertain what relative import risk means or how to interpret it, it is recommended that you first have a look at the “What is relative import risk?" section.
The current outbreak of the COVID-19 virus started in Wuhan City, Hubei Province, China. While the first cases were reported as early as December 8, 2019, the outbreak gained global attention on December 31, 2019, when the World Health Organization (WHO) was alerted to “several cases of pneumonia” by an unknown virus.
The new virus was soon identified as a novel coronavirus and named COVID-19. It belongs to the family of viruses that include the common cold and viruses such as SARS and MERS. On January 20, 2020, it was confirmed that the coronavirus can be transmitted between humans, greatly increasing the risk of global spread.
The spread of the virus on an international scale is dominated by air travel. Wuhan, the seventh largest city in China with 11 million residents, was a major domestic air transportation hub with many connecting international flights before the city was effectively quarantined and the Wuhan airport closed on January 23, 2020. By then, the virus had already spread to other Chinese provinces as well as other countries.
As of , total cases of COVID-19 have been confirmed. Of these, there are cases in Mainland China and cases in other countries.
All case data on this site is provided by Johns Hopkins University's Center for Systems Science and Engineering (CSSE) via their online GIS dashboard that tracks the spread of COVID-19 in realtime and offers up-to-date, downloadable datasets of confirmed cases.
Modeling the spread of COVID-19
The computational model estimates the
for airports, countries, and continental regions worldwide.
Relative import risk
By looking at air travel passenger numbers, we can estimate how likely it is that the virus spreads to other areas. The busier a flight route, the more probable it is that an infected passenger travels this route. Using these probabilistic concepts, we calculate the relative import risk to other airports. When calculating the import risk, we also take into account connecting flights and travel routes that involve multiple destintions.
Most probable spreading routes
Given an outbreak location and an origin airport close to it, the model identifies the most probable spreading routes to all other airports in the worldwide air transportation network. Even though passengers can take different routes to a final destination, global spreading patterns are dominated by the most probable paths. When viewed from the origin airport (the root node), these paths make up a shortest path tree that shows how the spreading process can reach all other airports in the network. These trees are important for identifying what airports play a pivotal role in distributing the spreading process thoughout the network.
An interactive visualization of the most probable routes and effective distances is offered in the Route Analysis & Effective Distance section.
Effective distance and expected arrival time
In the figure above, the vertical distance to the root node represents the effective distance to the outbreak location. The notion of effective distance was derived from traffic flux statistics and introduced in the paper The Hidden Geometry of Complex, Network-Driven Contagion Phenomena, D. Brockmann & D. Helbing, Science: 342, 1337-1342 (2013). In short, two airports are effectively close to one another if there is substantial air travel between them.
Effective distance has been shown to be a much better predictor of expected arrival time of an epidemic than traditional geodesic distances.