{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T15:51:49Z","timestamp":1770479509659,"version":"3.49.0"},"reference-count":41,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T00:00:00Z","timestamp":1766880000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The resurgence of tick\u2010borne diseases necessitates predictive frameworks that integrate both high accuracy and ecological relevance. This study develops a comprehensive machine learning pipeline to forecast the occurrence of\n                    <jats:italic>Ixodes ricinus<\/jats:italic>\n                    , a principal tick vector in Europe, leveraging high\u2010dimensional climatic, environmental, and land\u2010use datasets. We assembled and cleaned regional occurrence datasets from the United Kingdom and wider European repositories, to create a harmonized database comprising over 27,000 verified occurance record. To represent local tick presence and reduce spatial bias, we transformed the point data into 20 km\u2010wide hexagonal grid cell duplicates. The framework that integrates hexagonal spatial binning, binary transformation, and spatially aware absence selection maintains a balanced 1:2 ratio to minimize sampling bias and spatial autocorrelation. Spatial interpretation was strengthened by adopting DBSCAN with geodesic (haversine) distance, which identifies density\u2010based clusters and noise points and avoids the Euclidean\u2010distance constraints inherent to K\u2010Means. Each observation was paired with dynamic environmental and land\u2010use variables, including monthly rainfall, NDVI, temperature, and annual land cover. Models were trained and evaluated using stratified fivefold cross\u2010validation and optimized through RandomizedSearchCV, ensuring efficient exploration of hyperparameter spaces. Comparative evaluation across Random Forest, CatBoost, Gradient Boosting, AdaBoost, and Support Vector Machine classifiers demonstrated high predictive accuracy, with Random Forest achieving an ROC\u2013AUC of 0.941% and F1\u2010score of 0.882%. Incorporating spatial constraints and temporally aggregated features improved ecological realism and generalisation, addressing prior limitations in temporal dynamics and sampling bias. Feature importance analysis revealed NDVI, rainfall, and temperature as dominant predictors, aligning with ecological expectations. The study centres on tick occurrence, establishing a scalable and robust framework poised to support early warning systems and enable data\u2010driven surveillance of tick populations across Europe.\n                  <\/jats:p>","DOI":"10.1002\/cpe.70496","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T04:28:31Z","timestamp":1766982511000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Modelling of Tick Distribution: A Machine Learning Approach to\n                    <i>Ixodes ricinus<\/i>\n                    Abundance"],"prefix":"10.1002","volume":"38","author":[{"given":"Kruttika","family":"Jamalpuram","sequence":"first","affiliation":[{"name":"Intelligent Technologies Research Group School of Architecture, Computing and Engineering, CDT, UEL  London UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4008-8049","authenticated-orcid":false,"given":"Mhd Saeed","family":"Sharif","sequence":"additional","affiliation":[{"name":"Intelligent Technologies Research Group School of Architecture, Computing and Engineering, CDT, UEL  London UK"}]},{"given":"Afrin","family":"Nanmi","sequence":"additional","affiliation":[{"name":"Intelligent Technologies Research Group School of Architecture, Computing and Engineering, CDT, UEL  London UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9822-7884","authenticated-orcid":false,"given":"Samantha","family":"Lansdell","sequence":"additional","affiliation":[{"name":"Infection and Immunity Research Group School of Health, Sport and Bioscience, UEL  London UK"}]},{"given":"Ahmed Ibrahim","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering College of Applied Studies  Riyadh Saudi Arabia"}]},{"given":"Nasser","family":"Alalwan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering College of Applied Studies  Riyadh Saudi Arabia"}]},{"given":"Sally","family":"Cutler","sequence":"additional","affiliation":[{"name":"Infection and Immunity Research Group School of Health, Sport and Bioscience, UEL  London UK"}]}],"member":"311","published-online":{"date-parts":[[2025,12,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ttbdis.2022.102114"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foreco.2011.10.028"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmm.2006.02.008"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10493-014-9849-0"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-ento-052720-094533"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13071-023-05959-y"},{"issue":"8","key":"e_1_2_9_8_1","article-title":"Modelling Tick Bite Rissssk by Combining Random Forests and Count Data Regression Models","volume":"14","author":"Garcia\u2010Marti I.","year":"2019","journal-title":"PLoS One"},{"key":"e_1_2_9_9_1","volume-title":"SN\u00c4FF\u2010Annual Meeting","author":"Kj\u00e6r L. J.","year":"2017"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ttbdis.2024.102373"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/07853890.2024.2405074"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13071-019-3583-8"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(12)61151-9"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1017\/S003118202400132X"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1574-6976.2011.00312.x"},{"issue":"9","key":"e_1_2_9_16_1","article-title":"Perception of Ticks and Tick\u2010Borne Diseases Worldwide","volume":"12","author":"Fuente J.","year":"2023","journal-title":"Pathogens"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10493-017-0197-8"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/zph.12203"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/ecog.02881"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/2041-210X.13107"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2041-210X.2011.00172.x"},{"key":"e_1_2_9_22_1","first-page":"161","article-title":"Modeling of Species Distributions With Maxent: New Extensions and a Comprehensive Evaluation","volume":"32","author":"Phillips S. J.","year":"2009","journal-title":"Ecography"},{"key":"e_1_2_9_23_1","first-page":"1799","article-title":"Presence\u2010Only Species Distribution Modelling in a Rapidly Changing World: A Review of Methods and Applications","volume":"45","author":"Valavi R.","year":"2022","journal-title":"Ecography"},{"issue":"1","key":"e_1_2_9_24_1","first-page":"33","article-title":"Hyperparameter Tuning in Machine Learning: A Comprehensive Review","volume":"36","author":"Ilemobayo J. A.","year":"2024","journal-title":"Journal of Engineering Research and Reports"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.52710\/rjcse.88"},{"issue":"5","key":"e_1_2_9_26_1","first-page":"2903","article-title":"Evaluation of Feature Scaling for Improving the Performance of Supervised Learning Methods","volume":"12","author":"Assegie T. A.","year":"2023","journal-title":"Bulletin of Electrical Engineering and Informatics"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102769"},{"issue":"7","key":"e_1_2_9_28_1","article-title":"Predicting and Mapping Human Risk of Exposure to Ixodes ricinus Nymphs Using Climatic and Environmental Data, Denmark, Norway and Sweden, 2016","volume":"24","author":"Kj\u00e6r L. J.","year":"2019","journal-title":"Eurosurveillance"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pt.2017.12.006"},{"issue":"5","key":"e_1_2_9_30_1","article-title":"Eleven Quick Tips for Data Cleaning and Feature Engineering","volume":"18","author":"Chicco D.","year":"2022","journal-title":"PLoS Computational Biology"},{"key":"e_1_2_9_31_1","volume-title":"Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools","author":"Smith M. J. D.","year":"2007"},{"key":"e_1_2_9_32_1","volume-title":"Distributed Artificial Intelligent Model Training ABND Evcaluation","author":"Monahan C.","year":"2021"},{"key":"e_1_2_9_33_1","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"Van D.","year":"2012"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(01)00065-2"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_9_36_1","volume-title":"CatBoost: Unbiased Boosting With Categorical Features","author":"Ostroumova L.","year":"2017"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v22i2.1566"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"e_1_2_9_40_1","volume-title":"Presented at the AAAI Conference on Artificial Intelligence","author":"McCallum A.","year":"1998"},{"key":"e_1_2_9_41_1","volume-title":"Neural Networks and Learning Machines","author":"Haykin S. S.","year":"2010"},{"issue":"4","key":"e_1_2_9_42_1","article-title":"Effects of Micro\u2010Scale Environmental Factors on the Quantity of Questing Black\u2010Legged Ticks in Suburban New York","volume":"13","author":"Di C.","year":"2023","journal-title":"Applied Sciences"}],"updated-by":[{"DOI":"10.1002\/cpe.70600","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000}}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.70496","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T04:48:07Z","timestamp":1768193287000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.70496"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,28]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1002\/cpe.70496"],"URL":"https:\/\/doi.org\/10.1002\/cpe.70496","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"value":"1532-0626","type":"print"},{"value":"1532-0634","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,28]]},"assertion":[{"value":"2025-07-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70496"}}