Prioritising states will need to balance many considerations, including population, share of infections in total population, share in overall infections in the country, etc
Distribution of vaccines is challenging when there is scarcity as it gives rise to speculation on political motivations. Scarcity can be gauged from the fact that given the population that has to be vaccinated, there are not enough vaccines today. The PM has assured everyone that the Centre will bear the cost, which is a definite positive. Yet, given that they cannot be imported like wheat or onions as a lot of due diligence is required, rationing is necessary. Medical science also seems to have become as fickle as economics, with the interval before the second dose extended in India while the world is narrowing the gap. Quite clearly, the need is for the authorities to restore credibility.
Rationing is the way out for now, at least until supplies are in surplus and one can walk in to a vaccinie-centre and get the shot at will, as is possible for OTC medicines. One way to go about it is to give the maximum doses to the most populated states. Here, Uttar Pradesh (UP), Bihar, Maharashtra, West Bengal (WB), Madhya Pradesh (MP), Rajasthan, Tamil Nadu, Karnataka, Gujarat and Andhra Pradesh would be the main beneficiaries. But, is this a fair way of distribution, considering a large population does not necessarily mean that the need of the vaccine is greater?
We need to hence look at the infection levels, in terms of share in total infections in the country over the last 15 months or so. This will tell us which states have been affected the most that require attention as the spread has been faster for various reasons. The picture changes sharply now. Maharashtra has accounted for 20.2% of the total caseload of 28.57 mn (at the close of the first week of June) since the beginning of the pandemic. This is really high considering that the next four, Karnataka with 9.3%, Kerala with 9.1%, Tamil Nadu with 7.6% and Andhra with 6%, have recorded single-digit proportions. UP and Delhi come next, with 5.9% and 5%, respectively. This shows that the more industrialised states (barring UP) have tended to have higher Covid-19 infection. Therefore, prioritising vaccines per the share of infections may seem a better way of doing so as such states have the propensity to see fast-rising infection within the region and require preference.
But, here too, there is a controversy over how right the figures are in terms of spread as the levels of testing are different across states. Maharashtra, Kerala, Delhi, Karnataka have been very thorough in getting people tested while others have been lenient either because of indifference or the inability to test due to limitations, especially in rural areas. The ratio of infection to population is another good way of creating a priority list. Here, the picture is very different. The smaller states and UTs surprisingly have a higher proportion of their population that have been infected. Lakshadweep and Goa have above 10% of population that was infected, while it is above 5% for Delhi, Puducherry, Ladakh and Chandigarh. Maharashtra had 4.7% and Karnataka 3.9%. Some like Bihar, UP, and MP have less than 1% level of infection and Gujarat, Rajasthan and West Bengal have between 1-2% of the population that has been infected.
Hence, there are different ways to distribute the vaccine and, ideally, it should be driven by an algorithm which includes all these facets as the idea is first to stop the spread of the virus in the immediate run so that the numbers remain under control and there is less pressure on the healthcare system. Population, share in total infections, infection to population ratio are some of the indicators that may be used. They can be further stratified under heads such as active cases (in sync with the share in countrywide infection numbers) and age/morbidity cohorts that are more vulnerable. The NITI Aayog should build a model for allocating the vaccines to the states, with specific quotas for vulnerable age-cohorts as there does seem to be a sharp relation between activity in the states and age-groups.
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