Evidence map›Paper›PMID 40183001›Full record

ArticleInfectious Disease Modelling2025

The interaction between population age structure and policy interventions on the spread of COVID-19.

Hao Yin, Zhu Liu, Daniel M Kammen

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

3 authors.

Hao YinMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
Zhu LiuMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
Daniel M KammenEnergy and Resources Group, University of California, Berkeley, CA, 94720, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has triggered an unprecedented public health crisis and a global economic shock. As countries and cities have transitioned away from strict pandemic restrictions, the most effective reopening strategies may vary significantly based on their demographic characteristics and social contact patterns. In this study, we employed an extended age-specific compartment model that incorporates population mobility to investigate the interaction between population age structure and various containment interventions in New York, Los Angeles, Daegu, and Nairobi - four cities with distinct age distributions that served as local epicenters of the epidemic from January 2020 to March 2021. Our results demonstrated that individual social distancing or quarantine strategies alone cannot effectively curb the spread of infection over a one-year period. However, a combined strategy, including school closure, 50 % working from home, 50 % reduction in other mobility, 10 % quarantine rate, and city lockdown interventions, can effectively suppress the infection. Furthermore, our findings revealed that social-distancing policies exhibit strong age-specific effects, and age-targeted interventions can yield significant spillover benefits. Specifically, reducing contact rates among the population under 20 can prevent 14 %, 18 %, 56 %, and 99 % of infections across all age groups in New York, Los Angeles, Daegu, and Nairobi, respectively, surpassing the effectiveness of policies exclusively targeting adults over 60 years old. In particular, to protect the elderly, it is essential to reduce contacts between the younger population and people of all age groups, especially those over 60 years old. While an older population structure may escalate fatality risk, it might also decrease infection risk. Moreover, a higher basic reproduction number amplifies the impact of an older population structure on the fatality risk of the elderly. The considerable variations in susceptibility, severity, and mobility across age groups underscore the need for targeted interventions to effectively control the spread of COVID-19 and mitigate risks in future pandemics.

Identifiers

PMID40183001
PMCPMC11964527

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.