The Math Behind RSV Protection: What Expanding Infant Coverage Actually Achieves

The Science

What They Found

Why This Changes Things

What's Next

Every autumn, hospitals in Italy and across Europe brace for a predictable surge. Children arrive struggling to breathe, their airways inflamed by a virus so common it escapes notice until it lands a newborn in the ICU. Respiratory syncytial virus—RSV—sends roughly 200,000 children under five to hospitals across Europe each year. It kills around 50,000 children globally in the same age group. And for decades, public health had little to offer beyond supportive care and crossed fingers.

That began changing in 2023, when nirsevimab—a long-acting monoclonal antibody that provides five months of passive protection against RSV—became available in Europe. A single intramuscular injection given to infants before or during RSV season could now shield the most vulnerable from a virus that had long resisted prevention efforts. Italy, in particular, adopted the drug as part of its national immunization strategy, though implementation has varied sharply by region.

But a drug is only as good as its uptake. And that's exactly what a new modelling study published on arXiv sets out to examine: What happens—in real epidemiological terms—when more infants receive nirsevimab? And what happens when they don't?

The researchers, a team from the University of Naples Federico II, the University of Pavia, and affiliated pediatric units across Italy, built a mathematical model of RSV transmission tailored to Italy's population structure and healthcare context. Their stage-structured, age-stratified model divides Italians into three groups: infants under one year, children and adults between one and 64, and seniors 65 and older. They then simulated what would happen under different scenarios of nirsevimab coverage over a five-year horizon.

The results are both encouraging and instructive. Expanding coverage reduces RSV infections—not just among the infants who receive the drug, but across age groups, a phenomenon epidemiologists call herd immunity. However, the model also reveals a hard ceiling: infant prophylaxis alone, even at high coverage levels, cannot push the virus into extinction. The reproduction number stays above one. RSV keeps circulating.

"These findings suggest that broader and more consistent infant nirsevimab coverage may reduce RSV burden and support the evaluation of alternative implementation strategies in the Italian context," the authors write. That measured conclusion conceals a more urgent message: the tool exists. The question now is whether health systems will use it well.

The Science

The study's central instrument is a compartmental model—a workhorse of mathematical epidemiology since the early 20th century. In its simplest form, a compartmental model divides a population into discrete categories: Susceptible, Infected, Recovered. Individuals move between these categories according to mathematical rules that encode how diseases spread, how long people remain infectious, and how immunity develops and fades.

The researchers didn't invent this approach. Variations of the SIR framework have been used to model RSV for two decades. What distinguishes this study is the level of detail applied to Italy's specific context. The model is what mathematicians call stage-structured and age-stratified (SSAS): it distinguishes not just disease states but age groups, each with its own patterns of susceptibility, contact, and clinical outcomes. Infants don't interact with the world the same way teenagers do; neither do they experience RSV the same way 70-year-olds do. The model captures these differences explicitly.

The population is divided into three age classes. The first encompasses infants under one year—roughly 400,000 newborns annually in Italy. This group receives special attention because the severe disease burden of RSV concentrates here, with mortality risk highest in the first six months of life. The second group covers children and adults from age one to 64. The third comprises seniors aged 65 and older, a group where RSV increasingly causes hospitalizations and deaths, particularly in those with underlying chronic conditions.

Within the infant class, the model introduces additional compartments to track nirsevimab protection. Not all newborns receive the antibody at birth; some miss the initial window and may be reached later through catch-up campaigns. Some who receive it retain partial protection; others lose it as the monoclonal antibodies wane. The model tracks these pathways explicitly, allowing the researchers to distinguish between infants who are fully protected, partially protected, or completely susceptible at any given moment.

The compartmental structure reflects several biological realities. RSV infection confers only short-term immunity; recovered individuals return to susceptibility relatively quickly, which is why people can be reinfected multiple times throughout their lives. The model encodes this waning immunity with rate parameters derived from epidemiological literature. It also accounts for disease-induced mortality, which is concentrated in the youngest and oldest age groups, and for demographic turnover—births replacing deaths over time.

Transmission between age groups is governed by a contact matrix. This matrix encodes how often people in different age brackets interact: infants with other infants, infants with adults, adults with seniors, and so on. The researchers assume, based on empirical evidence from industrialized countries, that seniors have relatively few contacts with infants and younger adults. This is a defensible simplification: seniors are not in schools or workplaces, and family structures in Italy, as in other high-income nations, tend to be two-generational rather than three-generational. The primary transmission pathways run through families, schools, and workplaces—settings where younger, more socially active age groups mix intensively.

The model uses mass-action incidence, a standard assumption in respiratory disease modelling. Under this framework, the force of infection—the rate at which susceptible individuals become infected—scales with the density of infectious individuals in the population. This assumption works well for droplet-borne pathogens like RSV, where contact rates tend to increase with population density and social interaction frequency.

Once the model structure is established, the researchers derive two critical quantities: the basic reproduction number, denoted ℛ₀, and the control reproduction number, denoted ℛ_C. ℛ₀ represents the average number of secondary infections produced by a single infectious individual introduced into a completely susceptible population. If ℛ₀ exceeds one, the pathogen can spread; if it falls below one, it cannot. ℛ_C is the same quantity adjusted for the presence of control measures—in this case, nirsevimab prophylaxis. The difference between ℛ₀ and ℛ_C tells you how much the intervention reduces transmission.

The model is parameterized using data from multiple sources. Italian national statistics inform demographic parameters: birth rates, death rates by age group, and population structure. Epidemiological parameters—recovery rates, immune waning rates, transmission efficiencies—come from the scientific literature on RSV. The nirsevimab-specific parameters, including the effectiveness factor (which captures partial protection in some recipients) and the rate at which protection wanes, reflect clinical trial data and post-introduction surveillance.

The baseline scenario reflects the Italian context as of 2024: approximately 47.5% of newborns receive nirsevimab at birth, with additional catch-up campaigns reaching some infants who miss the initial window. This coverage level is not uniform across the country—regional variation is substantial—but the model treats it as an approximate national average. The researchers then run counterfactual simulations: What if coverage increased by 10%? What if it grew incrementally each year? What if it fell by 5%?

The simulation horizon is five years—long enough to observe how effects compound over successive RSV seasons but short enough that demographic change remains modest. The model accounts for seasonal forcing, the annual rhythm of RSV epidemics that peaks in winter and troughs in summer, through time-varying transmission rates.

This is the model that underpins everything that follows: a mathematical representation of how RSV moves through Italy, who it touches, and how a preventive antibody can interrupt that path.

What They Found

The baseline scenario establishes the current state: with 47.5% birth coverage and existing catch-up campaigns, RSV continues to circulate at levels that produce significant annual incidence across all age groups. Infants account for a disproportionate share of infections relative to their population size—a reflection of their immunological naivety and high susceptibility. But children and adults between one and 64 years, who make up the bulk of the population, accumulate the largest absolute number of infections simply by virtue of their numbers. Seniors, though fewer in number, bear a heavier clinical burden: higher hospitalization rates, more severe outcomes, greater mortality.

The first major finding concerns the indirect benefits of infant prophylaxis. When the researchers simulate increasing annual catch-up coverage by 10% relative to baseline, the effects ripple outward. RSV incidence falls not only among infants—the direct recipients of the intervention—but also among older children and adults, and to a lesser extent among seniors. This is herd immunity in action: by reducing the number of infected infants, nirsevimab reduces the number of sources from which other age groups become infected. The effect is largest for the age group most exposed to infant transmission, but it is not zero for any group.

Age Distribution of Cumulative RSV Infections

Label	Value
Infants	28 %
Children & Adults (1-64)	58 %
Seniors (65+)	14 %

Source: Autoriello et al., 2026. Model simulation results for Italy 2024 baseline scenario.

The cumulative incidence charts show this clearly. Under the upward scenario, each year's bar—broken down by infant (blue), children-and-adults (orange), and seniors (yellow)—is lower than it would have been under baseline conditions. The reduction is modest in absolute terms for any single year, but over five years the cumulative toll of avoided infections becomes substantial.

The second finding concerns the timing of prophylaxis. Nirsevimab is typically administered at birth or at the start of RSV season. But infants are born year-round, not just in autumn. Those born in spring and summer may miss the seasonal window; if they are not reached by catch-up campaigns, they enter their first RSV season partially or fully unprotected. The model examines what happens when prophylaxis is extended to cover these out-of-season births.

Extending protection to infants born outside the epidemic season further reduces cumulative incidence. This result follows logically: every additional infant who enters the susceptible pool is a potential transmitter. Reaching them earlier—before exposure risk peaks—removes them from that pool and breaks transmission chains that would otherwise extend to older family members and caregivers.

Impact of Nirsevimab Coverage Scenarios on RSV Incidence

Label	Value
Baseline (47.5% birth coverage)	100 relative incidence
+10% Catch-up Coverage	85 relative incidence
+5%/year Incremental Increase	92 relative incidence
-5% Coverage Reduction	118 relative incidence

Source: Autoriello et al., 2026. Indexed to baseline (100). Model simulation for Italy 2024.

The protection coverage chart illustrates how temporal targeting shapes the population-level benefit. When prophylaxis concentrates on a narrow seasonal window, coverage spikes during autumn and collapses in summer. When it extends year-round, protection builds more gradually but maintains higher average levels throughout the inter-epidemic period. The model shows that the latter strategy generates more cumulative protection, even if peak coverage never reaches the heights of a seasonal-only campaign.

The third finding is more sobering. Despite these benefits, the model indicates that infant-targeted prophylaxis alone—regardless of coverage level within the range explored—does not reduce the control reproduction number below the epidemic threshold. ℛ_C remains above one. RSV continues to circulate endemically, with seasonal epidemics recurring year after year. Nirsevimab reduces the burden substantially; it does not eliminate transmission.

This finding has several implications. First, it suggests that nirsevimab, while valuable, cannot serve as a standalone solution to RSV control. Additional interventions—maternal vaccination, pediatric vaccines (currently under development), adult vaccination programs, or non-pharmaceutical measures during peak seasons—may be necessary to push ℛ_C below one and achieve elimination. Second, it highlights the difference between reducing disease burden and interrupting transmission. Even if RSV cannot be eliminated, preventing millions of infections across a five-year horizon translates into substantial reductions in hospitalizations, healthcare costs, and human suffering.

The researchers examine three main scenarios beyond baseline: the upward scenario (10% increase in annual catch-up coverage), an annual increase scenario (5% increase in year one, then 5% incremental increases each subsequent year), and a downward scenario (5% decrease in both birth and catch-up coverage). Each scenario produces a distinct trajectory of cumulative incidence.

Under the annual increase scenario, the model predicts compounding benefits over time. Each year's incremental improvement in coverage adds to the benefits of previous years, as larger fractions of the infant population enter each RSV season protected. The downward scenario shows the mirror image: reduced coverage in any given year amplifies risk in subsequent years, as more infants enter the susceptible pool and sustain transmission.

Figure 4 from the paper shows annual cumulative RSV infections over five years under the upward scenario. The stacked bars—blue for infants, orange for children-and-adults, yellow for seniors—decline progressively across years, reflecting the accumulated effect of higher coverage. The reduction is visible in every age group, though the absolute numbers remain largest in the working-age population.

Figure 5 shows the annual increase scenario, where coverage improves incrementally. The trend is similar, though the pace of decline is gentler, reflecting the smaller year-over-year increments. The cumulative benefit over five years is substantial but distributed gradually.

Figure 6 illustrates the downward scenario, where reduced coverage leads to higher incidence in every year. The bars grow larger across the horizon—not because RSV is becoming more transmissible, but because each year of lower coverage leaves more infants susceptible, and those infants go on to infect others.

These results collectively paint a picture of nirsevimab as a powerful but incomplete tool. Coverage matters—a lot. Inaction has consequences that compound over time. But even the most aggressive infant prophylaxis strategy explored in this model does not achieve elimination.

Why This Changes Things

Before nirsevimab, the primary preventive tool for RSV was palivizumab, another monoclonal antibody introduced in the late 1990s. Palivizumab required monthly injections throughout RSV season, cost far more per dose, and was limited to high-risk infants—premature babies, those with congenital heart disease, chronic lung disease. It was a niche intervention: effective for the fraction of infants who qualified, irrelevant for the broader newborn population.

Nirsevimab changed this calculus fundamentally. A single dose provides five months of protection—essentially an entire RSV season. It is recommended for all infants under eight months entering their first season, or born during the epidemic period. In theory, universal infant prophylaxis became possible. Italy's decision to adopt nirsevimab into its national immunization schedule reflected this shift: from targeted prevention for high-risk groups to broad protection for an entire birth cohort.

But adoption has been uneven. The 47.5% baseline coverage figure in this study is not a policy failure per se—it represents the reality of an ongoing rollout in a country where regional health systems operate with significant autonomy. Some regions achieved high coverage quickly; others lagged. Parental acceptance, healthcare workforce capacity, supply chain logistics, and public communication all influence uptake. The COVID-19 pandemic complicated everything: respiratory virus surveillance systems were disrupted, healthcare seeking behavior changed, and the post-pandemic era saw unusual shifts in RSV circulation patterns that made planning difficult.

The model developed in this study provides a framework for thinking through these real-world complications. It translates coverage assumptions into quantitative expectations. If a region wants to know how much a 10% improvement in catch-up coverage would reduce infant hospitalizations over five years, the model offers an answer. If policymakers are debating whether to invest in year-round versus seasonal prophylaxis campaigns, the model can compare the outcomes.

This kind of translational work—bringing mathematical sophistication to bear on practical policy questions—is exactly what the field of epidemiological modelling does best. Surveillance data alone cannot answer "what if" questions. Clinical trials tell you whether a drug works under controlled conditions. But modelling lets you project forward, explore counterfactuals, and weigh tradeoffs that would be impossible to study experimentally.

The finding that infant prophylaxis generates indirect benefits in older age groups is particularly significant for healthcare planning. RSV is not just an infant disease. In adults over 60, it causes hospitalizations, pneumonia, and deaths that fly under the radar because the virus is less visible than influenza or COVID-19 in this population. Italy estimates around 26,000 RSV-associated hospitalizations annually among adults aged 60 and above, with roughly 1,800 deaths. If improving infant coverage reduces transmission to seniors—even modestly—the healthcare system benefits in ways that are not always captured by pediatric surveillance metrics.

This reframing matters for how we think about the value of infant prophylaxis. A program that costs money and requires healthcare infrastructure to deliver is easier to justify if its benefits extend beyond the direct recipients. The model's quantification of these indirect benefits gives policymakers a fuller accounting of what they're buying when they fund nirsevimab campaigns.

At the same time, the model's finding that infant prophylaxis alone cannot suppress RSV transmission below the epidemic threshold should disabuse anyone of the notion that universal nirsevimab coverage equals RSV elimination. The virus has multiple transmission pathways and multiple susceptible populations. Infants are high-risk, but they are not the only source of transmission, nor are they the only population that can sustain the epidemic. Adults and seniors who are reinfected year after year (because natural immunity is short-lived) provide a persistent reservoir. Until a vaccine provides durable protection across age groups, or until maternal immunization is more widely adopted, RSV will continue to circulate.

This does not diminish the value of infant prophylaxis. It sets realistic expectations. The goal is burden reduction, not elimination—at least not through this intervention alone.

The comparison with COVID-19 is instructive but imperfect. SARS-CoV-2 spread among adults and caused severe disease across the age spectrum, which meant that vaccinating adults was essential to protecting everyone. RSV is different: its severe manifestations concentrate in infants and seniors, while transmission is heavily driven by contact in schools, households, and childcare settings. Targeting infants for prophylaxis makes sense because they are both the most vulnerable and a major node in the transmission network. But it is not sufficient because transmission persists in older populations.

The model also highlights the importance of catch-up campaigns. Administering nirsevimab at birth is straightforward—every infant passes through a hospital or clinic. But reaching infants who miss that window requires outreach, reminders, and access. The parameter ψ in the model represents the rate at which eligible infants receive prophylaxis after birth. Increasing ψ—that is, improving catch-up coverage—produces measurable reductions in incidence. This finding aligns with what public health has learned from other immunization programs: reaching the last mile matters as much as launching the campaign.

From a health economics perspective, the model suggests that the marginal benefit of improving coverage is positive but diminishes at high levels. Each incremental increase in coverage prevents fewer additional infections than the previous increase of the same magnitude. This is the epidemiologic signature of herd immunity: as coverage grows, the pool of susceptible individuals shrinks, and each newly protected infant has fewer opportunities to be infected or to infect others. At some point, the cost of reaching the remaining unprotected infants exceeds the health benefits they would receive. Finding that equilibrium is a task for health economists as much as epidemiologists—but the model's output provides the epidemiological foundation for that calculation.

What's Next

The authors are careful to acknowledge the limitations of their analysis. The model is built for Italy, and its findings may not translate directly to countries with different population structures, healthcare systems, or RSV epidemiology. Low- and middle-income countries bear the heaviest burden of RSV mortality—nearly half of the world's RSV deaths in children under five occur in sub-Saharan Africa and South Asia—but data from these settings are sparse, and models parameterized on Italian data may not perform well there.

Several parameters in the model are uncertain. The transmission rates between age groups, encoded in the contact matrix, are derived from studies in high-income settings and may not fully reflect Italian social structures. The effectiveness of nirsevimab in real-world conditions—outside the controlled environment of clinical trials—remains an active area of research. Waning immunity parameters are based on limited data and carry substantial uncertainty.

The model does not incorporate maternal vaccination, which is emerging as an alternative or complementary strategy to infant prophylaxis. Vaccinating pregnant women induces antibodies that transfer to the fetus, providing passive protection during the vulnerable first months of life. Several maternal RSV vaccine candidates are in development or have received regulatory approval in recent years. A model that includes both maternal vaccination and infant prophylaxis would provide a more complete picture of prevention strategies.

Similarly, the model does not include RSV vaccination for older adults, which is now available in some markets. As data accumulate on vaccine effectiveness, safety, and uptake, incorporating adult vaccination into the model would allow analysts to explore combination strategies: infant prophylaxis plus senior vaccination, or maternal vaccination plus infant prophylaxis, or all three together.

The assumption that seniors have negligible contacts with infants and young children is a simplification that may not hold in all household configurations. Multigenerational living arrangements, increasingly common in southern European countries including Italy, create transmission pathways between age groups that the model may underweight. More granular contact data—ideally from longitudinal studies that track actual interactions across age groups—would improve the model's accuracy.

Seasonal forcing is incorporated as a time-varying transmission rate, but the specific shape and amplitude of the seasonal signal is uncertain. RSV seasons can vary in timing and intensity from year to year, driven by factors including population immunity, climatic conditions, and co-circulation with other respiratory viruses. A model that incorporates stochastic (random) variation in the seasonal forcing might better capture the real-world variability of RSV epidemics.

Despite these limitations, the study represents a significant step forward in understanding how nirsevimab can be deployed most effectively. The model's scenario-based framework offers a template for other countries seeking to optimize their RSV prevention strategies. The finding that catch-up coverage matters—that reaching infants born outside the epidemic season generates meaningful additional benefits—provides a specific, actionable recommendation for health systems.

The broader lesson is about the value of comprehensive prevention. No single intervention is likely to eliminate RSV. But each layer of prevention—infant prophylaxis, maternal vaccination, adult vaccination, eventually pediatric vaccines—reduces transmission and shifts the epidemic curve downward. The cumulative effect of multiple interventions, deployed together, could eventually push the virus below the epidemic threshold.

That day is not yet here. For now, the best available tool is nirsevimab, and the model developed in this study shows that using it more widely, more consistently, and with better catch-up coverage saves lives across age groups. The tool is ready. The question is whether health systems will rise to meet it.

RSV has been with humanity forever—a ubiquitous childhood infection that most people survive without notice and forget by adulthood. But for the infants whose airways swell shut, for the seniors whose lungs fill with fluid, and for the families who watch them struggle to breathe, RSV is anything but unremarkable. Mathematical models cannot treat patients. But they can tell us how to build systems that prevent patients from needing treatment in the first place. This study is a contribution to that project: rigorous, grounded in real data, and ultimately hopeful about what careful public health planning can achieve.