Evidence map›Paper›PMID 42613442›Full record

ArticleNature medicine2026

County, district and community-level measles transmission in the United States in 2013-2025.

Siyu Chen, Ana I Bento

Abstract read
PubMed Publisher
In one paragraph

Article in Nature medicine, 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

2 authors.

Siyu ChenDepartment of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.ORCID http://orcid.org/0000-0001-9418-2527
Ana I BentoDepartment of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA. arb24@cornell.edu.ORCID http://orcid.org/0000-0001-7728-007X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The United States declared measles eliminated in 2000. In 2025-2026, more than 3,500 cases have placed that status under formal review. Vaccine hesitancy, accelerated by COVID-19 disruption and misinformation, has eroded the immunological buffers sustaining elimination. Current surveillance compounds this threat: county-level vaccination aggregates structurally obscure the fine-scale susceptibility clustering where outbreaks originate. We assembled a nationwide multiscale vaccination database spanning 45 US states and Washington, DC (2013-2025), encompassing over 50,000 schools, 13,000 districts and 3,000 counties, and developed a transmission model to quantify epidemic risk across different spatial scales. School-level effective reproduction numbers crossed the epidemic threshold in 2022-2023, a transition invisible to aggregated surveillance. Average susceptibility doubled from approximately 5% to 10% following the pandemic. We also show that county boundary misalignment creates cross-boundary corridors that push well-vaccinated counties above threshold through spillover from less-vaccinated populations. State trajectories diverge markedly, shaped by exemption policies and vaccination infrastructure. The spatial architecture of US measles vulnerability has fundamentally shifted: transmission potential is supercritical in schools but invisible at the scale monitored by surveillance. Preventing measles re-endemicity requires surveillance and interventions operating at school and district levels, where imported cases can be intercepted before igniting sustained transmission.

Indexed as

MeaslesDisease OutbreaksHumansMeasles VaccineSchoolsUnited StatesVaccinationMeasles Vaccine

Identifiers

What OpenQuestion holds

Textmetadata
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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.