Estimating the Direct Impact of Influenza Vaccination and Vaccine-Averted Illnesses Using an Extended Static Compartmental Model

Flu season is a familiar rhythm of public health, a time when viruses circulate, people fall ill, and health officials work to stop the spread. For decades, scientists have relied on models—simplified mathematical representations of reality—to understand how well vaccines work. These models help answer a critical question: how many people are actually saved from getting sick, going to the hospital, or dying because they received a shot? The challenge has always been that the real world is messy. Viruses change, people's bodies forget how to fight them off over time, and in some parts of the world, the flu does not follow a neat winter schedule but circulates year-round. Older tools used to calculate these benefits were often designed for a single winter season in places with predictable weather, assuming that once a person was vaccinated or infected, they remained protected for the entire year. This approach worked well for simple scenarios but struggled to capture the full picture when vaccination campaigns stretched across multiple years or when immunity faded before the season ended.

A team of researchers from the U.S. Centers for Disease Control and Prevention and international health organizations has now updated this modeling tool to reflect a more complex reality. They extended a static model, which tracks how many people move between being healthy, sick, or protected, to handle multiple flu seasons at once. Crucially, they added the ability to account for waning immunity, meaning the protection a vaccine gives does not last forever but gradually fades, just as a memory might fade over time. They also allowed the model to distinguish between how well a vaccine prevents a mild cough versus a severe illness requiring hospitalization, and they added the capacity to count deaths averted. By building these features into a user-friendly spreadsheet tool, the researchers aimed to give public health officials in any country the power to run their own simulations using local data, without needing to be experts in complex mathematics.

To test this new framework, the researchers created two synthetic scenarios, essentially digital laboratories where they could control every variable. In the first scenario, they simulated a region with a typical Northern Hemisphere flu season, where activity peaks in the winter and vaccination programs run annually. They assumed an existing program where about one-third of the older adult population received a vaccine each year. The model showed that over four seasons, this program prevented roughly 15 percent of all flu-related illnesses. For the most severe outcomes, such as hospitalizations and deaths, the prevention rate was even higher, ranging from about 14 to 18 percent. The tool also calculated how many people needed to be vaccinated to prevent a single case of illness, a figure that varied by season but generally fell between 62 and 110 people. This demonstrated that the model could accurately quantify the value of a standard, seasonal vaccination campaign.

The second scenario explored a more challenging environment: a place where flu circulates year-round and no vaccination program currently exists. Here, the researchers simulated a hypothetical program where 20 percent of the population was vaccinated evenly throughout the year. In this setting, the model estimated that the program would prevent about 5 percent of flu illnesses over four years. While this percentage is lower than in the seasonal example, it reflects the reality of constant viral circulation and lower coverage. The researchers used this scenario to test different strategies, such as shifting the timing of the vaccine rollout. They found that starting a campaign one month earlier in a seasonal setting could prevent an additional 6 percent of illnesses, while aligning a year-round campaign with specific seasonal peaks could boost prevention by more than 20 percent. These simulations highlighted that the timing of a vaccine rollout matters significantly, and the new tool allows officials to find the optimal schedule for their specific region.

To ensure their new model was trustworthy, the researchers compared its outputs against real-world data from the United States during the 2017–2018 and 2018–2019 flu seasons. They ran the model using actual hospitalization rates, vaccination coverage, and vaccine effectiveness data from those years. The results were broadly consistent with previously published estimates, with the new model's predictions falling within the expected ranges of uncertainty. In some cases, the model that accounted for fading immunity predicted slightly higher numbers of prevented cases than older models, likely because it captured the high protection levels immediately after vaccination before the effect began to wear off. This validation gave the researchers confidence that the tool could be used to generate reliable estimates for countries that do not have access to specialized modeling teams.

The ultimate goal of this work is not just to produce numbers, but to empower decision-makers. By translating complex epidemiological concepts into a transparent spreadsheet, the researchers have provided a way for national health programs to visualize the direct impact of their efforts. The tool allows officials to ask "what if" questions: What if we increase coverage? What if we change the timing? What if the vaccine works better against severe disease than mild disease? The model suggests that even in settings with year-round flu, targeted vaccination strategies can make a measurable difference in reducing illness and saving lives. While the tool focuses on the direct protection of vaccinated individuals and does not account for the indirect benefits of herd immunity, it offers a clear, evidence-based foundation for planning and evaluating flu vaccination programs. In a world where health resources are often limited, having a reliable way to measure the return on investment for vaccination is a vital step toward protecting communities.