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To ensure proper allocation of Covid Vaccines, we need a scientific formula!

In April 2021, as India’s daily case count reached 300,000, a political dispute erupted between the Union government and some states over a purported vaccine shortage. This is the latest failure in India’s covid experiment. Several state health ministers have accused the federal government of restricting supply to their jurisdictions. The problem is particularly pressing due to a lack of clarity at a time when India requires vaccine allocations based on a research model that is both reliable and successful. It is unclear what the current vaccine distribution formula is across states. It is critical to employ straightforward statistical equations that account for covid severity. To that end, two doctoral fellows at the international institute for population sciences, Mumbai, have developed an integrated formula that recognizes six predictors of disease severity and assigns equal weightage to each.

What factors determine the vaccination process?

The need for and distribution of vaccines across states is determined by the scale of their populations, their susceptibility to the pandemic, and their sensitivity to the pandemic. A collection of six metrics that included three dimensions: demography, the magnitude of the new wave, and infection susceptibility, can be used. The three are assigned an equal weight in order to generate an overall ranking, which is nothing more than a proposed share of vaccines to be delivered to a state or Union territory (UTs).

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How did they sect these dimensions?

The demographic dimension is represented by the proportion of India’s population aged 45 and above across states and union territories. The percentage distribution of overall reported cases and deaths attributed to covid in the two weeks leading up to April 22 covers the magnitude, while the risk is measured using three indicators: the proportion of the population aged 45 and above with multimorbid conditions, the proportion of the rural population not served by community health centres (CHCs), and the percentage of the population not served by CHCs.

According to a wide range of literature, a higher share of urban dwellers and older people with multimorbidity conditions, combined with a lack of access to quality healthcare, increases the vulnerability to Sars-CoV-2 infections and deaths. The first five indicators were derived from Population Projections for India, the Longitudinal Ageing in India Study, and the National Family Health Survey reports.

The rural population of the CHCs is estimated by taking the state-wise number of CHCs from the 2019-20 Rural Health Statistics Report and the overall rural population from the Population Projections for India and the States report based on the criteria recommended by the National Health Mission (NHM). A CHC is supposed to serve 120,000 people in the plains and 80,000 people in hilly/tribal areas, according to NHM guidelines. The team used a 100,000-person threshold for states with a large mix of plains and hilly/tribal regions. A rural population is classified as a rural population not covered by CHCs if it exceeds the product of the number of CHCs and the standard population served by a CHC. Maharashtra, Uttar Pradesh, Karnataka, Bihar, and West Bengal are the top five states that seem to be in dire need of vaccines, according to this suggested formula. Given their high risk, these five states should collectively receive 50% of India’s overall vaccines; however, the most recent estimates for the first vaccine dosage show that they have only received around 37% of the total. This indicates a disparity in delivery, and the increasing number of fatalities each day leaves no room for error. Moving on to individual states, Delhi and Maharashtra receive only 56% and 57% of the proposed share, indicating a massive gap in vaccination coverage. Similarly, the formula shows that Tamil Nadu, Uttar Pradesh, Punjab, and Bihar have a 30% coverage gap. Karnataka, Chhattisgarh, Jharkhand, Andhra Pradesh, Telangana, and West Bengal, on the other hand, are providing optimal shares to ensure adequate vaccine coverage. Vaccination rates were higher in Madhya Pradesh, Meghalaya, Nagaland, Haryana, Kerala, Uttarakhand, Jammu and Kashmir, and Gujarat. Larger states like Odisha and Rajasthan, and smaller states like Himachal Pradesh, Mizoram, Arunachal Pradesh, Tripura, and Sikkim, on the other hand, are getting much more doses, up to 200 per cent of their proposed share.

Similar criteria, assessing the distribution and severity of the pandemic in different cities and administering vaccinations accordingly, can also be calculated within states. It is noteworthy that in this type the vaccination depending on the magnitude of the pandemic is allocated in a specific state for two weeks prior to 22 April. Per week, these values must be revised and vaccine distribution modified. In addition, the demographic factor can be modified to obtain the respective values given the allowance for the younger age group.

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Conclusion

Finally, without any waste, vaccines have to be used to their maximum capacity Positive components of the first step of vaccinations were observed in Italy, but their citizens had hesitancy and reservations about vaccine over time. Given the current surge in covid cases in India, it is therefore important to have as much population as possible covered by a well-equipped system. In view of the current wave of virus in India, an equipped system is, therefore, necessary to reach as much of the population as possible at doses. The government needs to develop a sharper vaccination policy to enable more people, particularly the most vulnerable, to get jabs and result in minimal waste.

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