<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>24(1)</volume><submitter>Kelly PH</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>Tick-borne encephalitis (TBE) is the most serious tick-borne viral disease in Europe. Identifying TBE risk areas can be difficult due to hyper focal circulation of the TBE virus (TBEV) between mammals and ticks. To better define TBE hazard risks and elucidate regional-specific environmental factors that drive TBEV circulation, we developed two machine-learning (ML) algorithms to predict the habitat suitability (maximum entropy), and occurrence of TBEV (extreme gradient boosting) within distinct European regions (Central Europe, Nordics, and Baltics) using local variables of climate, habitat, topography, and animal hosts and reservoirs.&lt;h4>Methods&lt;/h4>Geocoordinates that reported the detection of TBEV in ticks or rodents and anti-TBEV antibodies in rodent reservoirs in 20</pubmed_abstract><journal>International journal of health geographics</journal><pagination>3</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11908066</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models.</pubmed_title><pmcid>PMC11908066</pmcid><pubmed_authors>Kwark R</pubmed_authors><pubmed_authors>Madhava H</pubmed_authors><pubmed_authors>Davis J</pubmed_authors><pubmed_authors>Moisi JC</pubmed_authors><pubmed_authors>Stark JH</pubmed_authors><pubmed_authors>Dobler G</pubmed_authors><pubmed_authors>Kelly PH</pubmed_authors><pubmed_authors>Marick HM</pubmed_authors></additional><is_claimable>false</is_claimable><name>Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models.</name><description>&lt;h4>Background&lt;/h4>Tick-borne encephalitis (TBE) is the most serious tick-borne viral disease in Europe. Identifying TBE risk areas can be difficult due to hyper focal circulation of the TBE virus (TBEV) between mammals and ticks. To better define TBE hazard risks and elucidate regional-specific environmental factors that drive TBEV circulation, we developed two machine-learning (ML) algorithms to predict the habitat suitability (maximum entropy), and occurrence of TBEV (extreme gradient boosting) within distinct European regions (Central Europe, Nordics, and Baltics) using local variables of climate, habitat, topography, and animal hosts and reservoirs.&lt;h4>Methods&lt;/h4>Geocoordinates that reported the detection of TBEV in ticks or rodents and anti-TBEV antibodies in rodent reservoirs in 20</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Mar</publication><modification>2026-05-29T16:27:34.415Z</modification><creation>2025-04-03T23:23:49.727Z</creation></dates><accession>S-EPMC11908066</accession><cross_references><pubmed>40087786</pubmed><doi>10.1186/s12942-025-00388-9</doi></cross_references></HashMap>