Evidence map›Paper›PMID 42821564›Full record

ArticlePLoS computational biology2026

Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks.

Abbie Evans, William S Hart, Eunok Jung, Kyeongah Nah, Karla Bonic-Babic, Sung-Mok Jung, Robin N Thompson

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Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Abbie EvansMathematical Institute, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0001-9591-665X
William S HartMathematical Institute, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-2504-6860
Eunok JungDepartment of Mathematics, Konkuk University, Seoul, Republic of Korea.
Kyeongah NahBusan Centre for Medical Mathematics, National Institute for Mathematical Sciences, Busan, Republic of Korea.ORCID https://orcid.org/0000-0002-7708-1019
Karla Bonic-BabicMathematical Institute, University of Oxford, Oxford, United Kingdom.
Sung-Mok JungSaw Swee Hock School of Public Health, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0002-0787-4515
Robin N ThompsonMathematical Institute, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0001-8545-5212

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Demographic shifts are reshaping population age structures worldwide, with implications for infectious disease dynamics. Since contact patterns, susceptibility and infectiousness often vary by age, the risk that pathogen introductions initiate a substantial outbreak depends on the population's age distribution and associated behavioural characteristics. We develop an age-structured mathematical model to estimate the risk that a single pathogen introduction leads to sustained transmission (the probability of a major outbreak) under long-term demographic transitions, incorporating changes in age-specific contact patterns and behavioural adaptation (specifically, extended workforce participation in an ageing society). Using the Republic of Korea (projected to become the world's oldest population by 2050) as a case study, we show that population ageing may reduce the probability of a major outbreak due to older individuals' lower contact rates. However, this effect is attenuated for pathogens with increasing susceptibility or infectiousness with age, and by factors such as later retirement that increase the number of contacts that older individuals have. These findings demonstrate that, while outbreak risks are affected by demographic changes, they are further modified by associated behavioural responses, highlighting the importance of accounting for demographic and socio-behavioural context when assessing future infectious disease outbreak risks.

Indexed as

Communicable DiseasesDisease OutbreaksPopulation DynamicsDemographyDisease SusceptibilityHumansModels, BiologicalRepublic of Korea

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.