{"doi":"10.1093/jtm/taae013","title":"From GeoSentinel data to epidemiological insights: a multidisciplinary effort towards artificial intelligence-supported detection of infectious disease outbreaks","abstract":"Sentinel surveillance of international travellers has enabled GeoSentinel, a global surveillance and research network collaboration between the International Society of Travel Medicine (ISTM) and the US Centers for Disease Control and Prevention (CDC), to help identify multiple unrecognized outbreaks of public health importance (e.g. dengue in Angola 2013, Zika in Costa Rica 2016 and yellow fever in Brazil 20181) mostly using manual analysis techniques. Since its inception in 1995, the number of participating international GeoSentinel clinical sites has increased to 71 across 29 countries located on six continents. Standardized data (e.g. demographic, clinical and travel information) of ill travellers seen during and after travel are collected and entered in the GeoSentinel database by expert clinicians at travel and tropical medicine clinical sites. The database holds records from over 400 000 international travellers, however, the evolving system of data collection (e.g. addition or removal of variables) and the dynamic changes in both the number and geographic coverage of reporting sites have led to increased complexity in detecting sentinel cases or outbreaks. Although the application of standard statistical methodologies has led to successful detection of travel-associated illness trends and clusters,2 data from the GeoSentinel Network present further opportunities to enhance our understanding of travel-related diseases through development of more sophisticated outbreak detection methodologies. Important progress in developing early-warning systems for disease surveillance has been made by incorporating artificial intelligence (AI) algorithms that can extract insights from complex datasets for signals of infectious disease events with high accuracy.3 Between 1900 and 1935, modelling techniques were developed in which populations were assigned to compartments (e.g. Susceptible, Infected, Recovered) to describe the characteristics of the spread of infectious diseases. Such models, now considered foundational to mathematical epidemiology, were not developed by statisticians but by public health physicians.4 In a similar spirit, we argue that the development of modern outbreak detection methodologies is accelerated by multidisciplinary collaboration between data scientists and epidemiologists.5 While AI has increasingly replaced human tasks in other industries, given the necessary global collaboration in combating disease outbreaks, an outbreak detection methodology should complement rather than replace human decision-making.6 In this perspective, we identify challenges associated with applying novel data science methods to GeoSentinel surveillance data for outbreak detection. Subsequently, we demonstrate how effective multidisciplinary collaboration can overcome these challenges. Finally, we highlight the advantages of analysing the GeoSentinel data using such methods. Multiple statistical methods have been developed for early detection of infectious disease outbreaks such as control charts, scan statistics and regression-based techniques.7 As these methods have become more sophisticated with the integration of AI,3 the following inherent challenges persist in automated outbreak detection: determining and modelling background behaviour (e.g. endemic transmission rates), evaluating model performance, handling outbreak signals, the nature of outbreaks and their identification and evaluating overall system performance.8 In addressing the challenge of determining and modelling background behaviour, baseline prevalence data are essential. Since GeoSentinel data are limited to travellers seeking healthcare at GeoSentinel member sites, calculating prevalence, incidence and risk is challenging. However, given a diagnosis, timeframe and geographical range, approximate baseline limits for non-outbreak-like frequency patterns can still be established.9 Outbreak detection models signal epidemiologists when observed case numbers exceed t","journal":"Journal of Travel Medicine","year":2024,"id":444051,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9261,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1259203,"name":"Ivo V. Stoepker","orcid":"0000-0001-9579-7259","position":1,"is_corresponding":false},{"id":1259204,"name":"Gerard Flaherty","orcid":"0000-0002-5987-1658","position":2,"is_corresponding":false},{"id":261679,"name":"Kristina M Angelo","orcid":"0000-0001-7665-7875","position":3,"is_corresponding":false},{"id":1213245,"name":"R. Post","orcid":"0000-0001-6110-7467","position":4,"is_corresponding":false},{"id":1259205,"name":"Charles M. Miller","orcid":"0000-0001-6413-7389","position":5,"is_corresponding":false},{"id":932207,"name":"Michael Libman","orcid":"0000-0003-3416-6482","position":6,"is_corresponding":false},{"id":261694,"name":"Davidson H. Hamer","orcid":"0000-0002-4700-1495","position":7,"is_corresponding":false},{"id":413326,"name":"Edwin R. van den Heuvel","orcid":"0000-0001-9157-7224","position":8,"is_corresponding":false},{"id":621365,"name":"Ralph Huits","orcid":"0000-0001-8803-9468","position":9,"is_corresponding":false},{"id":1259633,"name":"Stan Heidema","orcid":null,"position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-19T02:01:33.526738Z","pmid":"38236181","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}