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Which states are least-affected from coronavirus in India | India News – Times of India


NEW DELHI: Some of the remotest and most sparsely populated regions of the country have been able to shield themselves from the coronavirus pandemic.
Total number of cases

The states of Sikkim and Mizoram and the Union territories of Ladakh, Andaman & Nicobar Islands, Dadra & Nagar Haveli and Daman & Diu have the least number of coronavirus cases in the country.
Limited connectivity to the outside world and low population density seem to have worked in favour of these states and UTs in the fight against the pandemic.
However, to term these UTs and states as the “least-affected” in the country could be misleading as the number of cases alone are not sufficient to measure the intensity of the pandemic.
Caseload per million

A host of parameters help analyse the actual severity of fast-spreading virus such as Covid-19. One such parameter is caseload per million of population.
The cases-per-million parameter gives an idea of the penetration or the density of the virus in a given population.
It is observed that states in the central part of the country have the least number of cases per million. The states of Bihar, Madhya Pradesh and Uttar Pradesh have low value of cases/million.
Uttar Pradesh has the fifth highest number of cases in the country. However, it has the third-lowest number of cases per million. It is also the largest populated state with over 200 million people. A low value of case/million is an indication that the virus may not have deeply penetrated the densely populated state. A low value of case/million could also be a result of low testing.
Total tests

Testing forms a crucial aspect of measuring the intensity of a pandemic. Epidemiologists have said that testing provides a window into the pandemic.
If a region is not conducting sufficient tests relative to its population then it would never know the extent of the spread of the virus.
Among all the states, Uttar Pradesh has conducted the highest number of tests — 14.31 million. Bihar, which earlier showed low values of cases/million, has extensively tested its people. It has so far conducted in excess of 10 million coronavirus tests. Madhya Pradesh, on the other hand, has tested less than 3 million people. Tamil Nadu and Maharashtra too have extensively tested their residents.
Once again, owing to the vast difference in the population of the states, it is important that the above figures be analysed against the population of respective states.
Tests per million

Uttar Pradesh has over 200 million people. Despite conducting the largest number of tests in the country, data shows that testing in the state is not as extensive as it should have been.
The state, as per the records of the ministry of health, has conducted just over 70,000 tests per million — below the national average of 80,000 tests per million. These low testing figures, despite the massive population in these states, can be one of the reasons for low caseloads in these states.
Delhi (262023), Arunachal Pradesh (223246), Goa (199946), J&K (178877), Andhra Pradesh (153738) and Assam (145023) have the highest value of tests per million.
Test positivity rate

There is another parameter that gives us even better insight into analysing the severity of a virus — test positivity rate.
Test positivity rate is defined as the percentage of tests that are coming out as positive.
It helps scientists measure the extent of the spread of a virus in a community. It also answers if enough tests are being conducted for the number of people who are getting infected.
The test positivity rate would be high when the number of positive tests is too high, or the number of tests being conducted is too low. A high value of test positivity is a signal for inadequate testing. On the other hand, a low positivity rate means that the level of coronavirus transmission, relative to the amount of testing, is low at this point in time.
A test positivity rate that remains consistently below 5% for two weeks is the recommended limit proposed by the World Health Organisation.
In this list, Bihar has the lowest positivity rate of 2.1 followed by Gujarat (2.9), Jharkhand (3.2) and Uttar Pradesh (3.9).

The measure of severity of coronavirus has to be further investigated based on fatalities.
Mizoram reported its first Covid death earlier this week. There were seven states and Union territories with less than 100 Covid deaths.
Case fatality rate

However, an analysis of the absolute number of deaths will not help decipher the lethality of the spread of the virus.
If the number of cases is a function of the population, then a measure of deaths due to coronavirus is dependent on the number of confirmed cases.
The case fatality rate, which is a ratio of deaths to confirmed cases, helps in better understanding of the coronavirus lethality.
A low value of fatality rate means fewer deaths are happening to the spread of the virus.
Fatality is also a function of testing. An efficient testing infrastructure can help in early detection of cases. Consequently, infected people can be treated early, thereby reducing the number of deaths.
On the other hand, lack of testing would result in Covid deaths getting registered as non-Covid deaths. A person who is not tested and gets infected may die due to other ailments. He/She will not be registered as a Covid death.
Uttar Pradesh has the fourth-highest number of deaths in the country. However, it has a fatality rate of 1.46 — lower than the national average of 1.49.
Andhra Pradesh, the numbers show, has done remarkably well in controlling the number of deaths. With over eight lakh cases, the state has managed to keep the fatality rate to 0.81.
States such as Kerala, Odisha, Bihar, Telangana and Assam, with over 2 lakh confirmed cases, have also been able to keep a check on Covid deaths. They all have a fatality rate of less than 0.60.
An accurate analysis of coronavirus’ severity is a complex process.
A lot also depends on the authenticity of the data recording. If the ground reports are flawed, due to intentional or intentional errors, then an analysis of these flawed reports would yield flawed results. Moreover, Covid-19 is a young, mysterious and a fast-spreading pandemic, which researchers and scientists are trying to unravel every day.

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