Evidence map›Paper›PMID 42609501›Full record

ReviewFrontiers in public health2026

Geographic expansion, not viral intensification, leads to human dengue outbreaks in Mexico: a 40-year integrated remote sensing and machine learning analysis.

Huixuan Li, Christopher Lee, Sean Sweeney, Homayoun Valafar, Melissa S Nolan

Abstract readReview
In one paragraph

Review in Frontiers in public health, 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.

Huixuan Li *Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States.
Christopher Lee *Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States.
Sean SweeneyDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States.
Homayoun ValafarDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States.
Melissa S NolanDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States.

Funding

Next generation mosquito control through technology-driven trap development and artificial intelligence guided detection of mosquito breeding habitatsR01AI165560 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI GUNTER, SARAH MURPHY, NOLAN, MELISSA SUZANNE · 2021 to 2025
$3.9M
ACL HHS U01CK000662NIAID NIH HHS R01 AI165560
6 · The paper itself

Abstract

Background: Dengue fever poses a pervasive, yet escalating public health burden in Mexico and abroad. Methods: We conducted a 41-year spatiotemporal analysis of dengue fever across Mexico (1985-2025), integrating monthly case surveillance with climate, land cover, vegetation, and novel disaster severity covariates derived from the Emergency Events Database. Four supervised regression models were trained on 1990-2021 data, with models evaluated on a 2022-2023 temporal holdout and against observed 2024-2025 surveillance totals. Five supplementary hazard analyses examined temporal correlation, disaster type breakdown, spatial co-occurrence, pre/post event trajectories, and sensitivity to scoring weight assumptions. Results: Mann-Kendall trend analysis identified statistically significant increasing dengue incidence in 15 of 32 states (9 inland), evidencing geographic expansion over four decades. Z-score analysis confirmed 2024 as a profound anomaly across both endemic and emerging states. Hazard features were significantly associated with national monthly dengue counts at lags 0-2 months across the full 1985-2025 series. A + 613% case increase following the June 2024 tropical storm. Sensitivity analyses confirmed the project's developed 'Severity Score' performed comparably to four theoretically motivated differential weighting schemes. Conclusion: This study provides the first structured integration of disaster severity covariates in a dengue forecasting framework for Mexico. Geographic expansion into inland states, the 41-year trend analysis, and the hazard adjustment results collectively support a differentiated public health response that targets both endemic coastal states and newly affected inland regions. The analytical framework is directly transferable to emerging dengue risk contexts in the United States and Central America, geographic neighbors also experiencing increased dengue virus transmission.

Indexed as

DengueDisease OutbreaksMachine LearningRemote Sensing TechnologyHumansIncidenceMexicoSpatio-Temporal Analysisdengue feverEM-DAThazard covariatesmachine learningMexicospatiotemporal analysis

Identifiers

PMID42609501
PMCPMC13478283

What OpenQuestion holds

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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.