Evidence map›Paper›PMID 42768350›Full record

ArticleBMC proceedings2026

Harnessing digital twins for public health surveillance and pandemic preparedness.

Hossein Hassani, Barbara Tornimbene, Francisco J Doblas-Reyes, Julia Fitzner, Steve MacFeely

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Hossein HassaniInternational Institute for Applied Systems Analysis (IIASA), Vienna, Austria.
Barbara TornimbeneWorld Health Organization Hub for Pandemic and Epidemic Intelligence, Berlin, Germany. tornimbeneb@who.int.
Francisco J Doblas-ReyesBarcelona Supercomputing Center (BSC), Barcelona, Spain.
Julia FitznerWorld Health Organization Hub for Pandemic and Epidemic Intelligence, Berlin, Germany.
Steve MacFeelyData and Analytics Division, World Health Organization, Geneva, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 10th session of the WHO Pandemic & Epidemic Intelligence Innovation Forum focused on the transformative potential of digital twins in enhancing public health systems and pandemic preparedness. Digital twins, virtual replicas of physical entities or systems, can integrate real-time data to simulate and predict outcomes, providing valuable insights for public health interventions. This session explored their applications in healthcare, urban planning, and climate adaptation, while addressing challenges such as data governance, ethical considerations, and system interoperability. Discussions also emphasised the need for overcoming limitations and leveraging multisectoral collaboration to maximise the utility of digital twins in advancing global health resilience.

Indexed as

Data governanceDigital twinsEthicsHealth careInnovationPrivacy

Identifiers

PMID42768350
PMCPMC13591669

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.