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New modeling led by interdisciplinary scientists at the University of British Columbia (UBC) could help public health officials better factor in the long-term impacts of infectious disease when determining optimal vaccine coverage rates.

"Our models indicate even small post-infection costs can tip the scales and make the case for vaccine-driven elimination," said Dr. Chadi Saad-Roy, an assistant professor in UBC's Department of Mathematics and Department of Microbiology and Immunology and senior author of the new study, published in the Proceedings of the National Academy of Sciences.

The COVID-19 pandemic highlighted how the effects of an infection can linger, with more than 400 million people globally experiencing persistent symptoms ("long COVID"). While evidence of post-viral impacts is mounting, models that evaluate infectious disease management strategies typically focus only on the acute phase.

To address this gap in the current study, the researchers developed a system of mathematical equations to examine interactions between interventions and post-infection effects.

"From the perspective of society, there's some cost to producing vaccines, and there's a big benefit to reducing infections and post-infection effects. So, the question is: Where does this optimum happen?" said Saad-Roy. For a benign virus, it may not be worth it to vaccinate at all.

For other infections, it may be best to vaccinate enough people to eliminate the disease. Or the ideal point may fall somewhere in between.

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