Modest Post-Infection Costs Can Tip Vaccine Coverage Toward Disease Elimination, UBC Model Shows

A new modelling study led by interdisciplinary researchers at the University of British Columbia suggests that public health officials may be systematically underestimating the value of vaccination by ignoring what happens after an infection resolves. The research, published this week in the Proceedings of the National Academy of Sciences, demonstrates mathematically that even small costs associated with post-infection complications can shift the optimal level of vaccine coverage dramatically upward, in some cases tipping the balance all the way to disease elimination. The findings arrive at a moment when the long shadow of COVID-19, with more than 400 million people worldwide experiencing persistent symptoms, has made the question of post-viral morbidity impossible for policymakers to ignore.

Senior author Dr. Chadi Saad-Roy, an assistant professor in UBC’s Department of Mathematics and Department of Microbiology and Immunology, said the models indicate that even modest post-infection costs can tip the scales and make the case for vaccine-driven elimination. The central insight of the work is that most existing frameworks for evaluating infectious disease management strategies focus almost exclusively on the acute phase of infection, quantifying hospitalizations, deaths, and transmission during the active illness while treating recovery as the end of the story. When a substantial fraction of recovered individuals go on to experience lingering sequelae, that accounting error can fundamentally distort the cost-benefit calculus that determines how aggressively a population should be vaccinated.

To address this gap, the team developed a system of mathematical equations designed to examine the interactions between interventions and post-infection effects. From the perspective of society, Dr. Saad-Roy explained, there is some cost to producing vaccines, and there is a large benefit to reducing infections and their downstream consequences, so the essential question is where the optimum lies. For a genuinely benign virus, it may not be worth vaccinating at all. For other infections, it may be best to vaccinate enough people to eliminate the disease entirely. In many realistic cases, the ideal point falls somewhere between those extremes, and the new modelling framework is intended to help answer precisely those questions in a principled way.

The researchers began with an epidemiological model of the classic susceptible-infectious-recovered type, which tracks the fraction of a population in each disease state over time. This compartmental structure allowed them to evaluate non-pharmaceutical interventions that reduce transmission, such as masking, social distancing, and air filtration, as complements to vaccination rather than substitutes for it. On long timescales, the team found that these non-pharmaceutical measures substantially decreased the portion of people who became infected and then recovered, and who were therefore vulnerable to post-infection effects. That result suggests that transmission-reducing measures can meaningfully reduce the long-term disease burden even when their most visible impact is on acute case counts.

The next step was to couple the epidemiological model to an economic one, allowing the researchers to assess how the costs associated with post-recovery syndromes alter the optimal level of vaccine coverage. To build a tool capable of pinpointing that sweet spot, they wrote equations that account for the societal cost of both acute and long-term infection, including expenses such as hospitalization and the loss of labor force productivity, alongside the cost of vaccination in terms of development, production, distribution, and recruitment. This dual accounting makes explicit what has often been implicit in immunization policy debates: the true price of an infection extends well beyond the acute episode, and the true cost of a vaccine campaign depends on how easily coverage can be expanded.

A key innovation of the study lies in how it treats vaccine costs. The team examined three different scenarios describing how the marginal cost of vaccination changes as the number of doses administered increases. In the accelerating scenario, there is a first wave of easy-to-reach vaccine adopters, and it becomes progressively harder to vaccinate additional people because of hesitancy or logistical barriers. In the decelerating scenario, initial resistance to vaccination gives way to peer influence that makes each additional individual easier to reach. The third scenario alternates between acceleration and deceleration, capturing settings where uptake dynamics shift back and forth as campaigns mature.

Across all three cost scenarios, the researchers found that the ideal amount of vaccine coverage was highly sensitive to post-acute effects. Even small post-infection costs were sufficient to shift the optimal coverage to higher rates, and in some cases to push the optimum all the way to elimination levels. This sensitivity means that diseases once considered marginal candidates for elimination campaigns, on the basis of acute severity alone, may become clear candidates once the cumulative burden of post-infection morbidity is factored in. Conversely, the result underscores how fragile optimal policy can be when the long-term consequences of infection are dismissed or left unmeasured.

Dr. Saad-Roy emphasized that the phenomenon needs to be tackled rigorously rather than dismissing individuals’ experiences as anecdotal. To more systematically monitor and document post-infection effects, he suggested that researchers conduct large, long-term cohort studies that survey participants on a broad constellation of symptoms. Such studies would supply the empirical cost estimates that models like these require, replacing scattered clinical impressions with population-level data on incidence, duration, and severity of post-acute conditions. Because these interventions reduce acute infection, he noted, they in turn mitigate post-acute burden and ultimately lead to a healthier world.

The practical implications extend to how vaccination campaigns are designed and communicated. It is critical, the authors argue, to understand which vaccine cost scenario, accelerating or decelerating, is actually at play in a specific setting in order to determine the optimal coverage level. That determination will require socioeconomic studies exploring vaccine decision-making dynamics in a given time and place, which may themselves be shaped by outreach efforts. Prakhar Jaiswal, a graduate student in the UBC Department of Mathematics and first author of the study, observed that educating people about the benefits of vaccination and reducing hesitancy can lower the cost of vaccination and tip the balance in favor of vaccinating to eliminate the disease. In other words, public engagement is not merely a soft complement to immunization programs but a direct lever on the economics that determine whether elimination is the rational target.

The interdisciplinary team behind the study includes co-authors Daniel Coombs of UBC Mathematics, Caroline Wagner of McGill Bioengineering, and Troy Day of Queen’s Mathematics and Statistics. The research was supported by the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research. As health systems worldwide continue to grapple with the legacy of long COVID and prepare for future viral threats, the study offers a quantitative argument that the era of evaluating vaccines by acute outcomes alone is drawing to a close, and that the arithmetic of elimination looks far more favorable once the full lifetime cost of infection is on the table.

Subject of Research: Mathematical modelling of how post-infection morbidity costs influence optimal vaccine coverage levels

Article Title: Even modest post-infection complications can make expanded vaccine coverage cost effective: New modelling

Article References: Even modest post-infection complications can make expanded vaccine coverage cost effective: New modelling. (n.d.). Original publication

Image Credits: AI Generated

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Keywords: vaccine coverage, post-infection complications, long COVID, mathematical modelling, cost-effectiveness, epidemiology, non-pharmaceutical interventions, vaccine hesitancy, disease elimination, public health, University of British Columbia, PNAS