INDIA’S PRAGMATIC VACCINATION STRATEGY AGAINST COVID-19: A MATHEMATICAL MODELLING BASED ANALYSIS

Objectives

To investigate the impact of targeted vaccination strategies on morbidity and mortality due to COVID-19, as well as on the incidence of SARS-CoV-2, in India.

Design

Mathematical modelling.

Settings

Indian epidemic of COVID-19 and vulnerable population.

Data sources

Country specific and age-segregated pattern of social contact, case fatality rate and demographic data obtained from peer-reviewed literature and public domain.

Model

An age-structured dynamical model describing SARS-CoV-2 transmission in India incorporating uncertainty in natural history parameters was constructed.

Interventions

Comparison of different vaccine strategies by targeting priority groups such as key workers including health care professionals, individuals with comorbidities (24 – 60 year), and all above 60.

Main outcome measures

Incidence reduction and averted deaths in different scenarios, assuming that the current restrictions are fully lifted as vaccination is implemented.

Results

The priority groups together account for about 18% of India’s population. An infection preventing vaccine with 60% efficacy covering all these groups would reduce peak symptomatic incidence by 20.6% (95% uncertainty intervals (CrI) 16.7 - 25.4), and cumulative mortality by 29.7% (95% CrI 25.8-33.8). A similar vaccine with ability to prevent symptoms (but not infection) will reduce peak incidence of symptomatic cases by 10.4% (95% CrI 8.4 – 13.0), and cumulative mortality by 32.9% (95% CrI 28.6 - 37.3). In the event of insufficient vaccine supply to cover all priority groups, model projections suggest that after keyworkers, vaccine strategy should prioritise all who are > 60, and subsequently individuals with comorbidities. In settings with weakest transmission, such as sparsely-populated rural areas, those with comorbidities should be prioritised after keyworkers.

Conclusions

An appropriately targeted vaccination strategy would witness substantial mitigation of impact of COVID-19 in a country like India with wide heterogenity. ‘Smart vaccination’, based on public health considerations, rather than mass vaccination, appears prudent.

Strengths and limitation of this study

• The model in this study is informed by age-dependent risk factors for SARS-CoV-2 infection among contacts, and is stratified by co-morbidities (diabetes and/or hypertension), and vaccination status.

• Data on mortality and large-scale contact tracing from within India, and the recent national sero-survey results were used, which constituted a major strength of this investigation.

• Distinguishing between ‘infection’ and ‘symptomatic disease ‘ preventing vaccines, the model was simulated under a range of scenarios for the basic reproduction number (R0).

• Should they have been available, real life country-specific data on excess risks of deaths due to comorbidities would have added strength to the presented model.

• Key priority group-specific data on social mixing and potential associated transmission was not available, and remained as a limitation.