Ageing Populations May Curb Outbreak Risks, but Behavioural Shifts Complicate the Picture

The world’s population is ageing at a pace without historical precedent, and the consequences extend far beyond pension systems and healthcare budgets. Because the ways people mix with one another, how susceptible they are to infection, and how infectious they become once infected all vary with age, the demographic composition of a population fundamentally shapes whether a single imported case of disease sparks a sustained epidemic or fizzles out. A new modelling study published in PLOS Computational Biology examines this question in detail, using the Republic of Korea, projected to have the world’s oldest population by 2050, as a case study of how long-term demographic transitions may alter the probability that a pathogen introduction escalates into a major outbreak.

The research team, led by Abbie Evans and William S. Hart of the University of Warwick together with collaborators including Eunok Jung of Konkuk University and Robin N. Thompson of the University of Oxford, developed an age-structured mathematical model designed to capture two intertwined processes. The first is the demographic transition itself: the shifting distribution of ages within a population as fertility declines and longevity increases. The second is behavioural adaptation, the ways in which individuals and institutions respond to demographic change, most notably through extended workforce participation as societies age and older people remain economically active for longer. By coupling these processes within a single framework, the model moves beyond static snapshots of contact patterns and instead tracks how outbreak risk evolves across decades of structural change.

The central quantity the model estimates is the probability of a major outbreak, defined as the likelihood that a single pathogen introduction into a population leads to sustained transmission rather than rapid extinction. This probability is a cornerstone of outbreak risk assessment, yet it is often estimated using contact data from a single point in time. The new study argues that such snapshots can be misleading when the underlying population is changing. Contact patterns, which describe how often individuals of different ages interact, are strongly age-dependent: children mix intensively at school, working-age adults encounter large numbers of colleagues and commuters, and older adults typically have fewer daily contacts. Susceptibility and infectiousness, meanwhile, can also vary with age depending on the pathogen in question.

To ground the model in realistic data, the authors drew on demographic projections for the Republic of Korea, a country whose fertility rate has fallen among the lowest in the world and whose age structure is transforming faster than almost any other. Korea’s trajectory makes it a natural laboratory for studying the epidemiological consequences of population ageing, since the changes it will experience over the coming decades are ones that many other nations, including China, Italy and Japan, are likely to follow. The researchers combined these projections with age-specific contact matrices, which quantify the rates at which individuals in each age band interact with those in every other band, and with pathogen-specific assumptions about how susceptibility and infectiousness scale with age.

The headline finding is that population ageing may, in many scenarios, reduce the probability that an imported infection triggers a major outbreak. The mechanism is intuitive: as the share of older individuals in the population grows and the share of children and young adults shrinks, the average number of contacts per person declines, since older individuals tend to have fewer social interactions than younger age groups. Fewer contacts mean fewer opportunities for transmission, which lowers the effective reproduction number and makes sustained chains of spread less likely. For pathogens whose transmissibility does not increase sharply with age, an older population therefore acts as a partial buffer against epidemic take-off.

However, the study shows that this protective effect is far from universal, and the authors identify two main factors that can attenuate or even reverse it. The first is the pathogen’s own age profile. For infections whose susceptibility or infectiousness increases with age, the demographic advantage of an older population shrinks, because the growing fraction of older individuals is precisely the group most likely to acquire and transmit infection. In such cases, ageing does not simply reduce the number of transmission opportunities; it shifts those opportunities toward the individuals for whom each contact carries the greatest epidemiological weight. The model demonstrates that the direction and magnitude of the age gradient in susceptibility and infectiousness can therefore determine whether demographic change raises or lowers outbreak risk.

The second attenuating factor is behavioural. As societies age, labour force participation among older adults is expected to rise, whether through later retirement ages, pension reforms or the need to sustain shrinking workforces. When older individuals remain in employment, their daily contact rates increase, eroding the contact advantage that retirement confers. The study specifically models extended workforce participation as a behavioural response to demographic change and finds that it increases the number of contacts older individuals have, thereby offsetting the reduction in outbreak probability that ageing alone would produce. This coupling between demography and behaviour is the study’s key conceptual contribution: the two cannot be assessed in isolation, because demographic transitions induce behavioural responses that feed back into transmission dynamics.

The implications for public health planning are significant. Outbreak risk assessments are typically built on contact surveys and demographic data from the recent past, and models calibrated to current conditions may project inaccurate risks for future decades. If a country’s age structure is changing rapidly, the probability that a given pathogen, say a novel respiratory virus or a re-emerging vaccine-preventable infection, establishes sustained transmission may shift substantially over a planning horizon of twenty to thirty years. The study suggests that governments preparing pandemic preparedness strategies should incorporate demographic projections and anticipated behavioural changes, such as rising retirement ages, into their risk models rather than assuming a stationary population with fixed mixing patterns.

The work also highlights the value of computational approaches that integrate multiple drivers of epidemic risk. Age-structured models of this kind allow researchers to disentangle the separate contributions of contact rates, susceptibility, infectiousness and population composition, and to ask counterfactual questions: how would outbreak risk change if retirement ages rose by five years, or if a pathogen’s age gradient in severity were steeper? By varying these components systematically, the authors show that the net effect of demographic change on outbreak vulnerability is the product of competing forces, some of which reduce risk and others of which increase it. This decomposition provides a template for applying the same analysis to other countries and other pathogens.

More broadly, the study is a reminder that infectious disease risk is embedded in social structure. The probability that a pathogen introduction becomes an epidemic is not a fixed biological property of the pathogen alone; it emerges from the interaction between the pathogen’s characteristics and the age distribution, contact behaviour and economic organisation of the host population. As demographic transitions reshape societies worldwide, the authors argue that accounting for demographic and socio-behavioural context will be essential for accurately assessing future outbreak risks and for designing interventions, from vaccination targeting to workplace policies, that remain effective as populations age.

Subject of Research: Age-structured modelling of how demographic change and behavioural responses alter the probability of major infectious disease outbreaks

Article Title: Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks

Article References: Evans, A., Hart, W. S., Jung, E., Nah, K., Bonic-Babic, K., Jung, S.-M., & Thompson, R. N. (2026). Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks. PLOS Computational Biology, 22(10), e1014319. https://doi.org/10.1371/journal.pcbi.1014319

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014319

Keywords: population ageing, infectious disease outbreaks, mathematical modelling, age-structured model, contact patterns, South Korea, workforce participation, outbreak probability, epidemiology, PLOS Computational Biology, demographic transition, behavioural adaptation