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This study demonstrates how Gaussian process emulation can address this challenge by training surrogate GP models on outputs from an abstract individual-based dengue-inspired simulation. The approach predicts outbreak probability, maximum incidence, and epidemic duration across an eight-dimensional parameter space, using Colombia dengue data.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/gaussian-process-emulation-for-exploring-complex-infectious-disease-models/445109/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/gaussian-process-emulation-for-exploring-complex-infectious-disease-models/445109.png","ImageObject",300,407,{"name":92,"@type":93},"mieayamfan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-02","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does Gaussian process emulation address in infectious disease modeling?","Question",{"text":112,"@type":113},"It reduces computational burden when exploring complex individual-based epidemiological models with many free parameters.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which epidemiological metrics are approximated by the Gaussian process surrogate models?",{"text":117,"@type":113},"Outbreak probability, maximum incidence, and epidemic duration derived from the individual-based model outcomes.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the calibrated GP model validated in this study?",{"text":121,"@type":113},"It is calibrated using a dataset of more than 1,000 observed dengue epidemics over 12 years in Colombia and then evaluated for predictive power.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},445109,1790974701,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090893677,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","OPEN ACCESS  \nCitation: Langmüller AM, Chandrasekher KA, Haller BC, Champer SE, Murdock CC, Messer PW (2025) Gaussian process emulation for exploring complex infectious disease models. PLoS Comput Biol 21(12): e1013849. [https://](https://)[ ](https://)[doi.org/10.1371/journal.pcbi.1013849](doi.org/10.1371/journal.pcbi.1013849)  \n[Editor:](Editor: Jennifer A. Flegg)[ Jennifer A. Flegg](Editor: Jennifer A. Flegg), The University of Melbourne Faculty of Science, AUSTRALIA Received: June 11, 2025  \nAccepted: December 18, 2025  \nPublished: December 29, 2025  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pcbi.1013849  \nCopyright: © 2025 Langmüller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution,  \nRESEARCH ARTICLE  \nGaussian process emulation for exploring complex infectious disease models  \nAnna M. Langmüller1,2,3*, Kiran A. Chandrasekher1, Benjamin C. Haller 1, Samuel E. Champer1, Courtney C. Murdock4,5,6, Philipp W. Messer1*  \n1 Department of Computational Biology, Cornell University, Ithaca, New York, United States of America, 2 Department of Mathematics, University of Vienna, Vienna, Austria, 3 Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark, 4 Department of Entomology, Cornell University, Ithaca, New York, United States of America, 5 Cornell Institute of Host-Microbe Interactions and Disease, Cornell University, Ithaca, New York, United States of America, 6 Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America  \n* [annamaria.langmueller@aias.au.dk](annamaria.langmueller@aias.au.dk), [annamaria.langmueller@gmail.com](annamaria.langmueller@gmail.com) (AML); [messer@cornell.edu](messer@cornell.edu)[ ](messer@cornell.edu)(PWM)  \nAbstract  \nEpidemiological models that aim for a high degree of biological realism by simulating every individual in a population are unavoidably complex, with many free parameters, which makes systematic explorations of their dynamics computationally challenging. In this study, we demonstrate how Gaussian Process emulation can overcome this challenge. To simulate disease dynamics, we developed an abstract individual-based model that is loosely inspired by dengue, incorporating some key features shaping dengue epidemics such as social structure, human movement, and seasonality. We focused on three epidemiological metrics derived from the individual-based model outcomes—outbreak probability, maximum incidence, and epidemic duration—and trained three Gaussian Process surrogate models to approximate these metrics. The GP surrogate models enabled the rapid prediction of these epidemiological metricsat any point in the eight-dimensional parameter space of the original model. Our analysis revealed that average infectivity and average human mobility are key drivers of these epidemiological metrics, while the seasonal timing of the first infection can influence the course of the epidemic outbreak. We used a dataset comprising more than 1,000 dengue epidemics observed over 12 years in Colombia to calibrate our Gaussian Process model and evaluated its predictive power. The calibrated Gaussian Process model identified a subset of municipalities with consistently higher average infectivity estimates; the notable overlap between these municipalities and previously reported dengue disease clusters suggests that statistical emulation can facilitate empirical data analysis. Overall, this work underscores the potential of Gaussian Process emulation to enable the use of more complex individual-based models in epidemiology, allowing a h","cbCaic7N2h1HYZxD","https://ap.wps.com/l/cbCaic7N2h1HYZxD","pdf",3608337,23,"English","# Abstract\n## Epidemiological modeling complexity\n## GP surrogate model training and metrics","[{\"question\":\"What problem does Gaussian process emulation address in infectious disease modeling?\",\"answer\":\"It reduces computational burden when exploring complex individual-based epidemiological models with many free parameters.\"},{\"question\":\"Which epidemiological metrics are approximated by the Gaussian process surrogate models?\",\"answer\":\"Outbreak probability, maximum incidence, and epidemic duration derived from the individual-based model outcomes.\"},{\"question\":\"How is the calibrated GP model validated in this study?\",\"answer\":\"It is calibrated using a dataset of more than 1,000 observed dengue epidemics over 12 years in Colombia and then evaluated for predictive power.\"}]","Gaussian process emulation for exploring complex infectious disease models | PDF",1790710315,58]