The infection rate is peaking in the Himalayan state of Uttarakhand. The recent data trends and analysis show a sharp spike in the infection rate across the 13 districts of the state. From 09 August to 09 September, the infection rate in the state increased from almost 5% to 6%. Infection rate at the district level varies from a low of 2.18% in Pithoragarh to 9.52% in Nainital.

(Infection rate in different districts of Uttarakhand)

Difference in hill and plain areas:

It is noteworthy that infection rates in all hill districts are lower than the state average, while in the plain districts they are much higher. The average infection rate for the nine hill districts is 2.49 while for the four plain districts (including Nainital) the average rate is 8.19.

Is Uttarakhand testing enough?

An interesting insight emerging from the data is the close relationship between the number of tests and infection rate i.e., the more the number of tests, the higher the infection rate. This inevitably leads to the question: are we testing enough? Is there a case for ramping up the number of tests, especially in the hill districts, to get a better fix on the infection rate?

One way of trying to solve this conundrum is to calculate the proportion of the population that is being tested at present. If the infection rate increases with an increase in the percentage of the total population being tested, then it can be claimed that the more we test the better will be our estimate of the rate of infection.

An exercise to test this proposition is presented in the table below based on the population of the state and the districts.

In order to make some sense of the data in this table and draw some tentative conclusions, we have grouped the districts into three categories based on high, medium and low scores on both the parameters – percentage of population tested as per the estimated population in 2020 and rate of infection.

Thus, we have the following nine combinations:

(1) high infection rate and high testing

(2) high infection rate and medium testing

(3) high infection rate and low testing

(4) medium infection rate and high testing

(5) medium infection rate and medium testing

(6) medium infection rate and low testing

(7) low infection rate and high testing;

(8) low infection rate and medium testing

(9) low infection rate and low testing.

(Cutt-off values chosen for two parameters)


(Percentage of the population tested as per 2011 and 2020 estimates and infection rate 2020)


The picture that emerges from this table supports the proposition that an increase in the percentage of the population tested is associated with relatively lower rates of infection. The relationship between these two variables, however, may not be as straight forward as this suggests.

We also find that the mountain districts have relatively high to medium testing with a correspondingly low to medium infection rate. On the other hand, the four plain districts have a relatively low rate of testing with a high rate of infection.

Perhaps what we are seeing is the effect of the low population combined with low population density in the mountain districts which may inhibit the spread of the virus. On the other hand higher population density in the plain districts, especially in the larger cities, perhaps facilitates the spread of the virus. These are issues for further analysis.

While on the subject of rates of testing it may be mentioned that it is difficult to say what should be the ideal or optimum level of testing. Hence all one can argue for is an increase in testing for viruses without specifying any target value.

Author is the former Chairman of Fourth State Finance Commission, Govt. of Uttarakhand and ex Vice-Chancellor, Kumaon University.

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