Evidence map›Paper›PMID 42756487›Full record

ArticleFrontiers in epidemiology2026

Insights on United States cumulative measles cases during the 2026 outbreak using ARIMA and generalized linear models.

Antonio Gondim, Chidozie W Chukwu, Folashade B Agusto, Joshua Ologbonyo

Abstract read
In one paragraph

Article in Frontiers in epidemiology, 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

4 authors.

Antonio GondimMathematics, University of Miami, Coral Gables, FL, United States.
Chidozie W ChukwuMathematical Sciences, Georgia Southern University, Statesboro, GA, United States.
Folashade B AgustoEcology and Evolutionary Biology, University of Kansas, Lawrence, KS, United States.
Joshua OlogbonyoMathematics and Statistics, Mercer University, Macon, GA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To understand the nature of the current measles outbreaks taking place across the United States in 2026, we analyzed historical surveillance data dating back to 1997 using four distinct forecasting models. By withholding data from the first four months of 2026 for out-of-sample validation, we evaluated how well traditional trend tracking and temporal dependence could anticipate this year's trajectory. While models incorporating short-term patterns performed best overall, every single model underestimated the cumulative cases recorded by May 1, 2026. This systematic underprediction reveals that the ongoing resurgence cannot be explained merely as a continuation of past trends or 2025 outbreak effects. Instead, 2026 represents a distinct, more severe outbreak regime, as the projected annual burden was already reached by May. These findings indicate that public health tracking must adapt to this heightened risk environment, and warn that the final 2026 case burden will substantially exceed that of previous years.

Indexed as

ARIMAXmeaslesnegative binomial regressionoutbreak predictionpoisson regressionpublic healthtime series analysis

Identifiers

PMID42756487
PMCPMC13582639

What OpenQuestion holds

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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.