Oral Presentation 9th Australasian Vaccines & Immunotherapeutics Development Meeting 2022

Bayesian networks as risk-benefit analysis tools in a fast-moving healthcare landscape (#27)

Jane E Sinclair 1 , Helen J Mayfield 2 , Samuel J Brown 1 , Tina Moghaddam 3 , Andrew Baird 4 , Aapeli Vuorinen 5 , Anoop K Enjeti 6 7 8 , Rajesh Puranik 9 10 , Sudhir Wahi 11 , Kerrie Mengersen 12 , Michael Waller 2 , Hassan Valley 13 , Carys Batcup 14 , Carissa Bonner 15 , John CB Litt 16 17 , Kirsty R Short 1 , Colleen L Lau 2
  1. School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia
  2. School of Public Health, The University of Queensland, Herston, QLD, Australia
  3. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD, Australia
  4. St Kilda Medical Group, St Kilda, VIC, Australia
  5. Data Science Institute, Columbia University, New York, USA
  6. School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia
  7. Calvary Mater Newcastle Hospital, Waratah, NSW, Australia
  8. NSW Health Pathology, John Hunter Hospital, New Lambton Heights, NSW, Australia
  9. Department of Cardiology, Royal Prince Alfred Hospital, Sydney, NSW, Australia
  10. Sydney Medical School Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
  11. Cardiac Society of Australia and New Zealand, Sydney, NSW, Australia
  12. School of Mathematical Sciences, Queensland University of Technology, Gardens Point, QLD, Australia
  13. School of Health and Social Development, Deakin University, Burwood, VIC, Australia
  14. Sydney Health Literacy Lab, University of Sydney, Camperdown, NSW, Australia
  15. School of Public Health, University of Sydney, Camperdown, NSW, Australia
  16. College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
  17. Scientific Advisory Committee, Immunisation Coalition, Melbourne, VIC, Australia

COVID-19 AstraZeneca and Pfizer vaccines have been associated with thrombosis and thrombocytopenia syndrome and myocarditis, respectively. While attempts have been made to contextualise adverse events following immunisation against the risks from COVID-19, this is complicated by constant changes in the pandemic landscape and accompanying updates in evidence informing the discussion. These changes include the emergence of new viral variants, associated reductions in vaccine effectiveness, waning vaccine effectiveness over time, case fatality rates, and local changes in public health measures regarding lockdowns and borders that influence COVID-19 community transmission levels.

To address the challenge of weighing up risks versus benefits of COVID-19 vaccines we developed a Bayesian network (BN) informed by Australian and international data, that calculates probabilities of outcomes under different scenarios of vaccine type and coverage, sex, age, community transmission intensity, variant, and vaccine effectiveness. This BN has been used to program the COVID-19 Risk Calculator (CoRiCal) (https://corical.immunisationcoalition.org.au), a user-friendly online tool that enables scenario analysis based on user inputs of age, sex, vaccination status, and transmission scenario. The tool can be used by health managers and individuals alone or in conjunction with clinicians for shared decision making on vaccination.

The model is easily updated to include emerging best evidence, data pertinent to different countries or vaccines, and outcomes such as long COVID. Moving beyond the pandemic, the model can function as proof-of-concept for the use of BNs as risk-benefit analysis tools in other healthcare contexts to prevent, prepare for and manage vaccine-preventable diseases into the future.