A research team led by the School’s Associate Professors (Department of Finance) Abhiroop MUKHERJEE and George PANAYOTOV, together with Associate Professor (School of Banking and Finance) Rik SEN from the University of New South Wales, has demonstrated how to measure vaccine effectiveness (VE) in the absence of adequate public health data. The team offers an easy-to-implement alternative to measuring VE and keeping data-deficient countries informed when formulating and adjusting their vaccination and immunization policies.
Using a simple statistical model called the Regression Discontinuity Design (RDD), they compared the various COVID outcomes (positive cases, positive cases with high CT value, hospitalizations, deaths) of groups of individuals above and below the cut-off age.
The research findings were recently published in Science Advances.
“VE could differ between populations as individual countries’ pre-existing exposure to COVID-19 could vary. Helping individual countries to understand whether and how well vaccines are working for their populations can help ascertain vaccination policy,” said Professor Mukherjee, also the Liwei Huang Associate Professor of Business. “Having accurate VE estimates can also help health authorities decide whether vaccinated populations will be spared severe outcomes, which is critical for deciding whether to ‘live with the virus’.”
Professor Mukherjee added that almost all countries used – and are still using – age eligibility criteria for COVID vaccines, so the RDD measures are widely applicable, and will be particularly helpful in data-deficient countries where public health data infrastructure is not good enough to apply standard VE measurements.
Find out more about the research here.
