It might seem obvious that reducing our movement will reduce infection spread. However, it is not obvious how much it will reduce. Will it be enough to stop the virus? Will it only slow down?
Simulations help us study the possible scenarios of spread of infection. They help us study how we can intervene, and how much impact our strategies of intervention may have. They allow us to measure whether our interventions will have a positive or negative impact.
Simulations are not meant to capture every detail of the real world. It is sufficient if they capture the essential aspects. For e.g. the simulations here populate all land areas with equal probability. In the real world, different areas have different population densities. So, this simulation is not meant to capture that aspect.
In real life, most people are present in the vicinity of their locations most of the time, and occasionally travel to other places. The simulation is intended to capture this aspect.
To see visualizations of real-world spread of COVID-19, refer:
Note that the simulations on this page are random simulations. So, you may sometimes need to re-run an experiment 2-3 times to see the intended effect.
When population is high, infection can spread more easily. Experimenting with different population sizes for the world, we can see the actual impact of this.
Click this to see:
While there is nothing much we can do to change the population of our neighbourhood, this does give an indication of how the infection may spread in areas with different population densities.
When people hover (i.e. move) around quite a lot in their neighbourhoods then, the infection spreads a lot more easily. How much more?
See this when people just double their movement:
Typically, you will notice that an entire continent gets infected quite quickly. Due to travellers, other continents also get infected.
This is perhaps the most important parameter among the studied parameters here. While there is nothing much we can do about the population of our neighbourhood, we can easily change how much we move around. Seeing how much this impacts the spread of infection, hopefully this will inspire us to restrict our movement.
When people double their travel, see:
When people travel more, we might expect the infection to spread a lot faster. We can see this happening. However, it is not as dramatic as when we increase hover distance. One reason is that in the simulation, people travel in isolation, and so they are not affected and do not affect others during travel. This may not capture real-life very well.
Still, the number of people in real-life who travel is much much less than those who do not travel. So, hover-disance is more critical than travel probability.
In real-life, we do not have much control over other parameters like:
You can change these parameters in the simulation to see their effects.
There are other variables used in the simulation, like the duration of infection. In our simulation, we have taken the duration to be 15 days after which a person may become immune (turns white) or die (black).
The most important take away of the current experiments is that by reducing our movement, as in social distancing, we can significantly reduce infection spread. Try to further reduce the hover distance to see the improvement.
When hover-distance is halved, see:
When hover-distance is further halved, see:
This stops the virus!
One more experiment to try: Reduce the "hover distance" parameter during simulation and click "update". See that further spread can be stopped even after the virus has already spread.