Saving Lives in the times of Wuhan COVID-19 – One chart at a time

Tracking and making sense of the Virus spread is no mean job. Particularly when there is not much known about it. We can build all the fancy models we want, but they won’t work as expected because – not much is known, there is no data set that will train the model to prove the effectiveness.

There are some known facts about viruses of all types. They exhibit a typical pattern. If a virus spread is unchecked, the outbreak rises over time and plateaus when it runs out of available hosts [1]. Some hosts exhibit higher resistance to the virus and left relatively unharmed, and some are highly prone to the worst effects. Containment is the only strategy left to Countries around the world till the time a vaccine is found.

One of the significant indicators of the outbreak is the growth rate – How long did it take for the number of confirmed cases to double? If the number of cases go up by a fixed number over a fixed period, we call that a linear growth. Like 500 every three days. However, if it keeps doubling within a set period, say five days, we call it exponential growth. Highly impacted countries are experiencing exponential growth – doubling every 4 to 5 days.


Of course, testing is vital to ascertain this growth factor, but contradictory opinions exist on the excessive amount of importance laid on just testing. One clear verdict arises from the arguments that tests per million is a metric that is no longer an indicator of the effectiveness of information collection.

The above chart is a significant one that tells whether the countries are doing enough to contain the spread or not. Monitoring that carefully and acting based on that should help countries flatten the curve soon.

The next one is a GeoSpatial chart that can help identify the red-zones. It is fairly straightforward to interpret this. While the previous chart gives you a macro trend understanding, this one gives you the spatial view of where precisely the need for isolation arises and stricter measures need to be enforced. Adding a layer of capacity will throw tremendous insights around the availability of hospital beds, supplies and enable the state machinery to mobilize resources to the areas of need.

A bigger lesson for the future control of pandemics, experts tend to agree is to first identify the most susceptible routes, more like a network map and target those lines initially. A simple flight network map will give this information efficiently. In the case of Wuhan COVID-19, an animated chart that I had come across throws a spotlight on the spread of this virus and the routes it took. You can go through that here

The biggest question on everyone’s mind is – when will this end? How will we go back to ‘normal’? There are many strategies that people have bandied about, but the best strategies are the simplest ones. Using Geography and Data Science to sequentially cluster and ‘clean’ geographic areas is the only way we can come out of this pandemic. In India, there is widespread acceptance that our lockdown will get extended and that the ‘staggered’ return to normalcy will happen village by village, district by district, and state by state. Many countries around the world are adopting this approach to ‘decentralized, regionalized controlled return’.

Geospatial and Data Analytics has shown that the world is unprepared for handling pandemic situations such as this. In a world where we share data with so many entities, it is ironic that the one time we really needed to share data, we didn’t. Data and maps alone are only half the answer. The other is how we make use of the information in bringing people together to solve the problem. Understanding what the issues are don’t mean we know how to solve them.

There is much to be learnt during these testing times, and we sincerely believe the world will emerge stronger once we coast through this. How we leverage Data and mine it to derive the insights will ensure we will never have to go through this again in the future.

We are willing to wager that this will usher in a greater emphasis on Maps, Data & its usage.


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