Application of Scientific Machine Learning Methods in Epidemical Modeling: Undetected COVID-19 Population Ratio Prediction

Simple item page
dc.contributor.advisorWare, Tony
dc.contributor.authorLiu, Xinyang
dc.contributor.committeememberLong, Quan
dc.contributor.committeememberRios, Cristian
dc.date2023-11
dc.date.accessioned2023-09-07T19:33:29Z
dc.date.available2023-09-07T19:33:29Z
dc.date.issued2023-08-31
dc.description.abstractCombining deep learning techniques with mathematical models can compensate for the drawbacks of each method. Deep learning methods can help increase accuracy, while mathematical models can save computational costs by constraining the neural network (NN) structure. The combined method is efficient for capturing the dynamics of real-life data with a small sample size. In addition, the trained model can be sparsely regressed to a concise parametric model using data-driven methods for learning the mechanics, and used for prediction. In this thesis we focus on the application to epidemic modelling. Some theoretical background is introduced in Chapter 1. In Chapter 2, the methodology for developing scientific learning model on epidemic population data is explained. Discrete time-shifting is considered because of the latent detection of infected population. The undetected rate is modelled in two ways: using a constant detection rate, and by involving the detection rate as an output of the NN. The trained models are regressed to the dynamic systems via SINDy for learning the terms, and for prediction. In Chapter 3, we apply the model to the first wave of COVID-19 in Calgary where the data only contains 12 points of weekly detected infected population. In Chapter 4, the model is extended to the first wave of COVID-19 in Canada, where there is training data containing 18 weeks of the detected infected population and the deceased population. In Chapter 5, some conclusions are drawn and the future potential of this method is discussed.
dc.identifier.citationLiu, X. (2023). Application of scientific machine learning methods in epidemical modeling: undetected COVID-19 population ratio prediction (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/116978
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/41822
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEpidemical Modeling
dc.subjectUniversal Differential Equations
dc.subjectDeep Learning
dc.subjectData-driven Methods
dc.subject.classificationEducation--Mathematics
dc.titleApplication of Scientific Machine Learning Methods in Epidemical Modeling: Undetected COVID-19 Population Ratio Prediction
dc.typemaster thesis
thesis.degree.disciplineMathematics & Statistics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2023_liu_xinyang.pdf
Size:
3.34 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Theses and Dissertations