{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T05:46:01Z","timestamp":1785735961608,"version":"3.56.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Dengue fever poses a pervasive, yet escalating public health burden in Mexico and abroad.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a 41-year spatiotemporal analysis of dengue fever across Mexico (1985\u20132025), 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\u20132021 data, with models evaluated on a 2022\u20132023 temporal holdout and against observed 2024\u20132025 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Mann\u2013Kendall 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\u20132\u202fmonths across the full 1985\u20132025 series. A\u202f+\u202f613% case increase following the June 2024 tropical storm. Sensitivity analyses confirmed the project\u2019s developed \u2018Severity Score\u2019 performed comparably to four theoretically motivated differential weighting schemes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1888753","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T05:30:34Z","timestamp":1785735034000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Geographic expansion, not viral intensification, leads to human dengue outbreaks in Mexico: a 40-year integrated remote sensing and machine learning analysis"],"prefix":"10.3389","volume":"14","author":[{"given":"Huixuan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of South Carolina","place":["Columbia, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina","place":["Columbia, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Sweeney","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of South Carolina","place":["Columbia, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Homayoun","family":"Valafar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina","place":["Columbia, SC, United States"]},{"name":"Institute for Infectious Disease Translational Research, University of South Carolina","place":["Columbia, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa S.","family":"Nolan","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of South Carolina","place":["Columbia, SC, United States"]},{"name":"Artificial Intelligence Institute, University of South Carolina","place":["Columbia, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"ref1","volume-title":"Dengue and Severe Dengue. 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