India: Coronavirus state-wise estimates

5 min readMar 26, 2020

Right before starting off with the article, let me clarify that these estimates are really conjectures. Due to paucity of data, this is the best I could do at the moment and I must impress upon the reader that this is, at best, an informed guess, but a guess nevertheless.

After seeing overwhelming evidence of the threat that India’s senior citizens are facing due to the novel coronavirus, we want to find out what could be expected for each state under each country-like scenario (See this article for more). There are two factors that come to mind when we think of this exercise.

An important point before we start off. The following analysis excludes Chandigarh, Delhi, Puducherry, Dadra and Nagar Haveli and Daman and Diu, as they are cities.

First, we look at state-wise distribution of the elderly population to gauge which segments are highly exposed.

Figure 1 maps India’s population density for the age group: 60 and above. The Northern belt, states including Punjab, Delhi¹, Haryana, Uttar Pradesh, Uttarakhand, Bihar and Sikkim, amongst themselves share 30% of the old population but only 15% of the land area. Goa, Kerala and the north-eastern states too have high densities.

People of age 75 and above constitute about 1.98% of the population². Figure 2 gives the same map for this higher age group.

The map doesn’t change much except that we find a disproportionately large share of Nagaland, Mizoram and Manipur.

A crude idea of whether population density is indeed affecting the infection rate can be obtained by taking a look at the relation between the two. Figure 3 plots the distribution of infections as on 26–03 against population density. The general trend seems to be negative⁵, going against the expectation, however, Kerala is one state that does seem to exhibit growing number of cases and has a high population density of old-age people. Another reason behind this result could be the representation of North-Eastern states, Goa, Bihar and Jharkhand. All these states have high population densities but have not/ have recently seen their first coronavirus cases.

A better estimate is obtained if we drop the North-Eastern states and Goa, Figure 4 shows that the relationship becomes slightly positive, indicating population densities could play a role. States like Uttar Pradesh, Punjab and Haryana have high population densities but witnessed their first cases quite later than Kerala did⁸. These are the crucial states that need to enforce a strict lockdown, or they could go into a spiral of cases.

Figure 4: Excludes Goa and the North-Eastern states (including Sikkim)

Second, while estimating the number of infections and deaths for each State, we must take care of the fact that cities and densely-packed urban areas will have higher community transmission than others. An example of this is seen in the US where Washington, California and New York show a large number of cases³. For India too, a positive relation is seen.

Figure 5⁶ plots percentage of infections by state against the number of cities that have a population over 1 lac⁷. We can see that, in this graph, Kerala does not fit the bill while Maharashtra does. This makes us conjecture that both- the degree of urbanization as well as population density of the old- matters. Andhra Pradesh, Uttar Pradesh and West Bengal are the states at risk according to this factor.

Figure 6 shows a map that plots the inter-state distribution of coronavirus cases as on 26–03.

Figure 6: Distribution of Coronavirus cases as on 23–03. Darker shaded regions have higher infections. Source⁴

Estimates

Having stated our concerns regarding important factors, we restrict estimates to extrapolating from the current distribution of cases among states. Given the lack of data, these are currently the best estimates for the distribution of cases. If current US situation is anything to go by, we would expect the number of cases to rise rapidly in metropolitan cities and areas with larger elderly population as compared to the rest. Here we present estimates if India were to follow the path travelled by these countries:

Figure 7 shows estimated cases in each state using the estimates in here. Corresponding estimated deaths is given in Figure 8. Note that the estimates for the US is based on data up to 24–03, and hence it represents a stage before the booming of the cases and deaths⁹.

These figures and the previous analyses show how important the coming weeks is for India. The effectiveness of the lockdown will be a major determinant as to which country-like scenario we tend towards.