{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T08:28:34Z","timestamp":1782980914352,"version":"3.54.5"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Infect Dis Poverty"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n \n Anopheles<\/jats:italic>\n mosquitoes pose notable public health concerns as competent vectors of malaria and other diseases. Although malaria is no longer endemic in the United States, recent locally acquired cases in states including Maryland highlight the need to better understand\n Anopheles<\/jats:italic>\n dynamics in the region. This study aimed to identify geographic hotspots of\n Anopheles<\/jats:italic>\n presence in Maryland and evaluate how land cover, microclimatic conditions, and socioeconomic vulnerability shape their spatial and temporal distribution.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Monthly\n Anopheles<\/jats:italic>\n occurrence data (1999\u20132024) from Global Biodiversity Information Facility (GBIF) were aggregated at county and Census Block Group (CBG) scales. Counties were ranked by mean annual presence to identify hotspots. Associations with land cover, microclimatic, and socioeconomic conditions were assessed using Spearman\u2019s rank correlation (\u03c1;\n P<\/jats:italic>\n \u2009<\u20090.05). At the CBG scale, significantly correlated variables (|\u03c1|\u00a0\u2265\u20090.25) were used to fit an Extreme Gradient Boosting model to quantify the relative importance of environmental and socioeconomic predictors. Spatial dependence was addressed through blocked cross-validation, and model interpretability was evaluated with SHapley Additive exPlanations (SHAP) values.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Prince George\u2019s and Anne Arundel Counties emerged as primary hotspots of presence with highest observer effort during the analysis period. Seasonal analysis revealed an annual cycle, with peak presence from May to September, coinciding with warmer conditions favorable to vector proliferation. SHAP analysis at the CBG-scale identified habitat availability as the most influential predictor (33.1% of total model impact for low impervious surface percentage) with woody wetland emerging as the most preferred habitat; followed by humid conditions (24.6%), and low elevation (18.2%). Notably, cooler and more humid microclimates within the warm season provide optimal habitat, reflecting fine-scale environmental controls on\n Anopheles<\/jats:italic>\n distribution. Therefore, CBG-level analysis within Prince George\u2019s County revealed a negative correlation between Area Deprivation Index and\n Anopheles<\/jats:italic>\n presence (\u03c1\u2009=\u2009\u20130.35), indicating fine-scale ecological drivers\u2014such as woody wetland habitat, low impervious surface, and humid cooler microclimates\u2014more prevalent in affluent suburban residential neighborhoods.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n This study demonstrates that fine-scale habitat characteristics and warm-season microclimates structure\n Anopheles<\/jats:italic>\n mosquito presence in Maryland. These insights support more spatially targeted vector control and improved public health surveillance strategies.\n <\/jats:p>\n <\/jats:sec>\n \n Graphical Abstract<\/jats:title>\n <\/jats:sec>","DOI":"10.1186\/s40249-025-01407-4","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:01:45Z","timestamp":1768867305000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Microclimates, land cover, and socioeconomic vulnerability shape Anopheles hotspots in Maryland, USA"],"prefix":"10.1186","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6010-2330","authenticated-orcid":false,"given":"Chibuike Chiedozie","family":"Ibebuchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somtochukwu Stella","family":"Onwah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itohan-Osa","family":"Abu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"issue":"8","key":"1407_CR1","doi-asserted-by":"publisher","first-page":"589","DOI":"10.7326\/M16-1397","volume":"165","author":"RL LaRocque","year":"2016","unstructured":"LaRocque RL, Ryan ET. 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