Vaccine Prioritization Depends on Transmission

At 50% vaccination coverage, prioritizing the most exposed people reduces the peak of an epidemic by up to 15 percentage points — but can leave more people infected overall, depending on how fast the virus spreads. This counterintuitive trade-off, revealed in a new modeling study, upends a long-standing assumption in public health: that targeting high-transmission individuals is always the best way to slow an outbreak.

The reason? Most vaccines don’t offer perfect, all-or-nothing protection. Instead, they’re “leaky” — they reduce your risk of infection with each exposure, but don’t eliminate it. And when people face repeated exposures, even vaccinated ones can eventually get infected. This reality reshapes the calculus of who should be vaccinated first — and shows that the best strategy depends not just on who’s most likely to spread the virus, but on how intense the outbreak already is.

A team of researchers from Peking University simulated epidemics in a real-world community of 2,279 people, using detailed survey data on who lives with whom, who works where, and how often people go out. They compared two strategies: vaccinating the most exposed individuals first (the “high-exposure strategy”), and vaccinating the oldest first (the “oldest-first strategy”). At a fixed 50% vaccine coverage, they found that the relative success of each approach flips dramatically depending on transmission intensity — measured by the basic reproduction number, $R_{0}$ , the average number of people one infected person will infect in a fully susceptible population.

Their findings, published in 2026 on arXiv (Xing et al., 2026), reveal three distinct patterns across the epidemic spectrum — patterns that could help public health officials make smarter, more adaptive decisions during future outbreaks.

The Science

The study builds on a growing body of work showing that assumptions about how vaccines work can profoundly influence policy recommendations. Most classic models assume “all-or-nothing” protection: a vaccinated person is either fully immune or not protected at all. But real-world data — from influenza to measles to COVID-19 — suggest many vaccines are “leaky,” meaning they reduce susceptibility per exposure rather than blocking infection completely.

Under leaky protection, the risk of infection accumulates over time. A delivery driver who interacts with dozens of people daily faces many small risks — and even with a leaky vaccine, those risks add up. In contrast, someone with few contacts might remain protected for much longer.

To capture this, the researchers built an individual-level exposure framework using survey data from a community in Beijing. Each of the 2,279 residents was represented as a node in a network, with connections weighted by contact duration. The model included:

Household contacts (stable, high-intensity)
Community contacts (stochastic, based on daily activities)
External exposure (from work, school, or other out-of-community activities)

Figure 2: Individual contact network and recipients selected by the two targeted vaccination strategies. (A) Largest connected component of the internal weighted contact network, containing 2,258 nodes (99.1% of residents) and 4,617 edges. Each node represents a resident, with darker blue indicating higher relative exposure strength. Dark and light grey edges represent household and community contacts, respectively. Node positions do not represent geographical locations. (B) Joint distribution of age group and relative exposure strength for all 2,279 residents. Colours distinguish recipients selected by the high-exposure strategy, the oldest-first strategy, both or neither at 50%50\% vaccination coverage. Points are randomly offset horizontally within each age group to reduce overlap. In both panels, relative exposure strength is normalized by the maximum exposure strength across all residents. Source: Yiwen Xing, Jianyi Zhang

shows the resulting contact network — a web of 2,258 connected individuals, with darker nodes indicating higher exposure. The researchers defined “exposure strength” as the sum of internal network contact weights plus external exposure, normalized to a 0–1 scale.

They then simulated SEIS-type disease dynamics (Susceptible → Exposed → Infectious → Susceptible), with partial immunity after recovery. Vaccination provided leaky protection: it reduced the probability of infection at each exposure, with efficacy waning over time. They tested a range of $R_{0}$ values (1.0 to 18.6) and baseline vaccine efficacy ( $V E_{S, 0}$ ) from 50% to 80%, running 100 Monte Carlo simulations per scenario.

Two targeted strategies were compared:

High-exposure strategy: vaccinate individuals in descending order of exposure strength
Oldest-first strategy: vaccinate in descending order of age

Both were evaluated at 50% coverage, with outcomes measured as:

Final infection proportion (share of people infected at least once)
Epidemic peak size (maximum number of infectious individuals at one time)
Expected deaths (sum of individual infection fatality ratios, or IFRs)

The random strategy served as a baseline.

What They Found

The results revealed a striking dependency on transmission intensity.

At low transmission ( $R_{0} \leq 1.2$ ), the high-exposure strategy dominated. It reduced both the epidemic peak and final infection proportion more than the oldest-first approach. Why? Because infections spread slowly and remain localized. Protecting the most connected individuals — the hubs of transmission — effectively blocks early chains of spread. At $R_{0} = 1.0$ and $V E_{S, 0} = 0.8$ , the high-exposure strategy reduced final infections by 2.92 percentage points more than the oldest-first strategy.

But at intermediate transmission ( $R_{0} = 3.0$ ), a trade-off emerged. The high-exposure strategy still suppressed the peak — by up to 15.08 percentage points compared to random vaccination — but paradoxically led to a higher final infection proportion. At $R_{0} = 3.0$ and $V E_{S, 0} = 0.8$ , it resulted in 1.60 percentage points more infections than the random strategy.

Hybrid vaccination strategies at different R₀ levels

Trajectories of final infection proportion and epidemic peak size as allocation shifts from oldest-first (α=0) to high-exposure (α=1) at fixed 50% coverage.

Hybrid vaccination strategies at different R₀ levels
Label	Value
R₀ = 1.0	0.15
R₀ = 3.0	0.32
R₀ = 18.6	0.48

illustrates this divergence. At $R_{0} = 3.0$ , the high-exposure strategy (triangle) achieves the lowest peak but the highest final infections. The oldest-first strategy (square) has a higher peak but fewer total infections. The hybrid strategy (diamond) offers a middle ground.

This happens because highly exposed individuals, even when vaccinated, face so many infection opportunities that their cumulative risk remains high under leaky protection. Meanwhile, lower-exposure individuals — many of them older — are more likely to stay protected. By vaccinating the latter, the oldest-first strategy shields people who are less likely to get re-exposed, effectively preserving vaccine efficacy.

At high transmission ( $R_{0} \geq 14.0$ ), the differences narrow. Nearly everyone gets infected regardless of strategy, so final infection proportions converge. But here, the oldest-first strategy gains a slight edge in both metrics. Why? Because at such high $R_{0}$ , even leaky protection in lower-exposure groups can delay or prevent some infections, while the sheer volume of transmission overwhelms the advantages of targeting hubs.

Three patterns of vaccination strategy performance

Performance of high-exposure vs. oldest-first strategies across R₀ and vaccine efficacy.

Three patterns of vaccination strategy performance
Label	Value
Low R₀	1.2
Intermediate R₀	3
High R₀	18.6

summarizes the three patterns. At low $R_{0}$ , green dominates: high-exposure wins on both fronts. At intermediate $R_{0}$ , blue takes over: a trade-off between peak and final infections. At high $R_{0}$ , red wins: oldest-first slightly outperforms.

The oldest-first strategy also consistently reduced expected deaths — by up to 11.11 percentage points at low $R_{0}$ — because it directly protects those at highest risk of dying. In contrast, the high-exposure strategy offered no mortality benefit, since many high-exposure individuals are younger and at lower risk.

Why This Changes Things

These findings challenge a core principle of network epidemiology: that targeting high-degree nodes is optimal for outbreak control. That logic holds under all-or-nothing protection — and indeed, when the authors reran their model under that assumption, the high-exposure strategy consistently outperformed on both infection metrics (

Figure S1: Under all-or-nothing protection, the high-exposure strategy consistently reduces epidemic peak size relative to the random strategy. Heatmaps show additional reductions in final infection proportion (A), epidemic peak size (B) and expected deaths (C) across combinations of R0R_{0} and VES,0VE_{S,0}. Positive values favour the high-exposure strategy, whereas negative values favour the random strategy. Additional reductions are expressed in percentage points (pp) using the corresponding no-vaccination mean as the normalization baseline. Results are averaged across 100 simulations of 500 days per setting at 50% vaccination coverage. All-or-nothing protection follows the waning schedule described in Section 1I. Source: Yiwen Xing, Jianyi Zhang

Figure S2: Under all-or-nothing protection, the oldest-first strategy reduces expected deaths but produces a larger epidemic peak size than the random strategy. Heatmaps show additional reductions in final infection proportion (A), epidemic peak size (B) and expected deaths (C). Positive values favour the oldest-first strategy, whereas negative values favour the random strategy. Values are in pp. Results are averaged across 100 simulations of 500 days per setting at 50% vaccination coverage. Parameter combinations, waning and normalization are the same as in Fig. S1. Source: Yiwen Xing, Jianyi Zhang

But under leaky protection — which better reflects real-world vaccines — the picture flips. The very people who drive transmission are also the most likely to eventually get infected, even when vaccinated. This erodes the long-term benefit of prioritizing them.

The implications are profound. During the early stages of an outbreak, when $R_{0}$ is low, targeting the highly exposed — healthcare workers, transit staff, teachers — makes sense. It flattens the curve, buys time, and reduces total infections.

But once transmission accelerates, that same strategy may backfire. It could lead to a lower peak but more total infections — and more deaths, if older, vulnerable people are left unprotected.

This helps explain real-world observations. During the 2009 H1N1 pandemic, some countries prioritized high-transmission groups, while others focused on high-risk individuals. The outcomes were mixed — and this study suggests why. The optimal choice may have depended on local $R_{0}$ , which varied by region and phase of the pandemic.

It also reframes debates about vaccine allocation. The tension between “saving lives” and “stopping spread” isn’t just ethical — it’s epidemiological. And the best balance shifts with the state of the epidemic.

Consider a city facing a new respiratory virus. If $R_{0}$ is 1.1, vaccinating bus drivers and grocery workers could prevent thousands of infections. But if $R_{0}$ jumps to 3.5 due to a new variant, the same strategy might protect fewer people overall — and cost more lives — than vaccinating the elderly, even if it keeps hospitals from being overwhelmed in the short term.

What's Next

The study has limitations. It models a single community with specific age and contact patterns. Real cities are more complex, with layered networks and mobility. The model assumes uniform vaccine efficacy and waning, though real vaccines vary. And it doesn’t account for behavior change — people may reduce contacts when cases rise.

Still, the core insight — that leaky protection creates a transmission-intensity-dependent trade-off — is likely generalizable. The authors tested alternative network structures (like scale-free networks) and found similar patterns.

Future work could integrate real-time $R_{0}$ estimates into dynamic allocation algorithms. Imagine a dashboard that recommends shifting from exposure-based to age-based prioritization as $R_{0}$ crosses a threshold. Or hybrid strategies that split doses between high-exposure and high-risk groups, tuned to local conditions.

One such hybrid is explored in

Figure 5: Relative advantages of vaccination strategies across transmission intensities under leaky protection. All panels use 50%50\% vaccination coverage, with outcomes averaged across 100 Monte Carlo simulations per strategy or hybrid allocation. (A) Comparison of the high-exposure and oldest-first strategies across combinations of R0R_{0} and baseline vaccine efficacy VES,0VE_{S,0}. Green and red indicate lower values of both metrics under the high-exposure and oldest-first strategies, respectively. Blue indicates a trade-off in which the high-exposure strategy achieves a lower epidemic peak size at the cost of a higher final infection proportion than the oldest-first strategy. Regions are classified by endpoint performance, and panel B examines the corresponding hybrid allocation trajectories in representative settings. (B) Hybrid allocation trajectories at VES,0=0.8VE_{S,0}=0.8 for R0=1.0R_{0}=1.0, 3.03.0 and 18.618.6. Recipients selected by both targeted strategies remain vaccinated. The fraction α\alpha of the remaining vaccination places is assigned to the high-exposure strategy, with the remainder assigned to the oldest-first strategy. Points show mean outcomes at 51 equally spaced values of α\alpha from 0 to 1 and are coloured by α\alpha. Squares, diamonds and triangles identify the oldest-first strategy (α=0\alpha=0), hybrid allocation (α=0.5\alpha=0.5) and high-exposure strategy (α=1\alpha=1), respectively. (C, D) Exposure-decile contributions to the high-exposure minus oldest-first differences in final infection proportion (C, percentage points) and epidemic peak size (D, individuals) for the same scenarios as in B. Residents are ranked by exposure strength from lowest to highest and divided into ten population deciles. In C, differences in the number of individuals infected at least once in each group are divided by the total population and expressed in percentage points. In D, infectious individuals in each group are counted at the peak in each simulation. Negative values (blue) and positive values (orange) indicate lower contributions under the high-exposure and oldest-first strategies, respectively. The dashed line separates the lower and higher exposure halves of the population. The Total column reports the overall difference between strategies. pp denotes percentage points. Source: Yiwen Xing, Jianyi Zhang

B. At intermediate $R_{0}$ , a 50-50 split (diamond) offers a compromise: a peak only slightly higher than the high-exposure strategy, but final infections closer to the oldest-first approach.

But hybrids aren’t always optimal. At low $R_{0}$ , pure high-exposure wins. At high $R_{0}$ , pure oldest-first wins. The best strategy isn’t always a blend — it’s the right one at the right time.

The biggest challenge may be operational. Public health campaigns thrive on simple messages: “Protect the vulnerable” or “Stop the spread.” Shifting strategies mid-outbreak could confuse the public and undermine trust.

Yet the alternative — sticking to one strategy regardless of context — may cost lives. As climate change and urbanization fuel more frequent outbreaks, we’ll need more nuanced tools.

This study doesn’t offer a one-size-fits-all answer. Instead, it gives us a framework: to see vaccination not as a static policy, but as a dynamic intervention, shaped by the virus, the vaccine, and the moment.

In a world of leaky protection and unpredictable transmission, the best way to save lives may be to stop asking who should get the vaccine first — and start asking when.

Vaccines Don't Work the Same for Everyone — And That Changes Who Should Get Them First

The Science

What They Found

Why This Changes Things

What's Next