Cluster Analysis of Type A, B, and C clusters reflect an exciting trend. India is a diverse country, and yet we are one. COVID-19 has also made sure our maps look somewhat like that.

Maharashtra, Gujarat, and Tamilnadu are type A – Concentrated urban clusters getting affected

Maharashtra has a significantly high number of cases. It is ranked the top COVID19 affected state with 60,000 cases of the 167,500 cases in the country.

However, a next-level drill-down analysis indicates Mumbai (60%), Thane (14%), and Pune (12%) contributing 86% of the cases and 80.5% of the COVID-19 deaths. Mumbai and suburban Mumbai alone provide to 60 Percent of the evidence.

A very similar trend would be observed even with Tamilnadu where Chennai, Chengalpattu, Tiruvallur, and Kancheepuram together contribute 77% of the cases in Tamil Nadu. These four districts provide 85.5% of the deaths in Tamil Nadu.


Status: Dated 29.05.2020, Source: covid19india.org

 

And in Gujarat, Ahmedabad and Gandhinagar contribute to 74.3% of the COVID-19 cases and 82.5% of COVID19 deaths.

Rajasthan, Karnataka and Kerala are type B – Distributed district clusters spread

We see a fascinating trend with Rajasthan, Karnataka and Kerala. In all three states, the spread of the pandemic is well distributed. That could probably be the reason how these states can isolate, quarantine, and manage the spread. Because more distributed are the facilities and decentralized work plans


Status: Dated 29.05.2020, Source: covid19india.org

 

In Karnataka, even the capital city of Bangalore urban is a minuscule 11.18% of the total infections in the state. Also, if you add the neighboring districts like Mandya, Chikkaballapur, and Bangalore (Rural), the COVID spread is less than 25% of that in the state. It is very evenly distributed across Kalaburagi, Belagavi, Bidar, Vijayanagar in the north, and Dakshin Karnataka on the south.

Similarly, Rajasthan is also evenly spread around a few clusters. Jaipur, capital, has less than 25% of the state’s infection, followed with others like Jodhpur, Udaipur, Pali, Kota, etc.. However, interestingly for Rajasthan in terms of deaths, 47.25% are from Jaipur. This is also possible because Jaipur being a significant city severe cases, may have moved to a Jaipur and Jodhpur.

One could also see that Kerala has again fairly widespread distribution as in the picture above.

Uttar Pradesh and Haryana are type C – The clusters have imported the spread from another state


Status: Dated 29.05.2020, Source: covid19india.org

 

Uttar Pradesh, for all its numbers, has got about 25% of the cases from Agra, GB Nagar, Ghaziabad and Meerut. Ghaziabad and JP Nagar could even be termed as part of Delhi which contribute about 9% of cases in UP. In terms of the many fatalities, again it is concentrated around Agra and Meerut. Presumably, the GB Nagar and Ghaziabad severe cases would amount to Delhi.

And similar is the case with Haryana. Its highest concentration is Gurugram, Faridabad, Jhajjar and Sonipat contributing to 64% of the cases in the state. The number o deaths in these districts are almost zero, possibly because severe cases would amount to Delhi.

The question then would be – Should the entire country with 736 districts be under lockdown, panic, and fear, if this is all about 20-25 districts.

Wouldn’t it have been better in foresight and hindsight than strategically, to have walled the urban clusters and possibly distributed the migrated migrants early-on (with adequate checks and local village level quarantines) to have better control on the infrastructure and facilities?

This possibly again validates the March 26 hypothesis that, if migrants were allowed to move back as early as in March and early April – 1) They would not have been infected by then. 2) And high-urban density and clusters that caused the overload on the system could have been avoided.

The districts and decentralized administration always had a better ability to control and manage resources. Their decision making capability and administrative wisdom was snatched away by centralized decision making with #Lockdown1 #Lockdown2 and #Lockdown3. It was a disaster in the making from the start.

Disclaimer: The information, ideas or opinions appearing in this article are those of the author and do not necessarily reflect the views of N4M Media.

Subscribe N4M's fortnightly analysis, reviews, surveys & offers

LEAVE A REPLY

Please enter your comment!
Please enter your name here

This site uses Akismet to reduce spam. Learn how your comment data is processed.