Satyendra Nath Chakrabartty
Purpose: Effectiveness of COVID-19 vaccine is increasingly important for better immunization, sustain uptake, in the background of availability of a number of vaccines and uneven vaccination programmes with different objectives and strategies. The paper explores different measures of vaccine efficiency (VE) at population level for comparing and classifying groups of individuals in terms of risk and to find relationships between exposures and spread of disease.
Methods: To review model-free measures and outcome measures of mathematical/ statistical models towards meaningful comparisons and assessment of efficiency of SARS-CoV-2 vaccine and to suggest better measures.
Results: Mathematical models with unrealistic assumptions and limitations may underestimate the impact of mass vaccination. Interrupted time-series comparing trends in pre-vaccination and post-vaccine periods have wide applicability. Two suggested measures of VE are (i) difference of slopes of pre- and post-vaccination periods and (ii) Relative Risk (RR), reflecting association between the exposure and the outcome.
Conclusions: VE computed from difference of slopes of two linear trends or RR facilitates statistical testing of equality of the vaccine efficiency for two different groups with available statistical tests. Measures based on ratios and proportions are simple to compute, interpret and facilitate meaningful comparisons including statistical testing of hypothesis. Such measures may be computed for sub-populations defined in terms of the factors of COVID 19 like age, gender, co-morbidities, genetic & biological factors, adaptive immunity, prior exposure to SARS-CoV-2 via infection or via vaccination, etc. Model-free measures for evaluation and models considering administration of vaccine mechanism may be considered independently. Future studies suggested.
English 
Spanish 
Chinese 
Russian 
French 
Japanese 
Portuguese 
Hindi