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DC Field | Value | Language |
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dc.contributor.author | Tang, Zhenyao | - |
dc.date.accessioned | 2020-09-28T15:53:18Z | - |
dc.date.available | 2020-09-28T15:53:18Z | - |
dc.date.issued | 2020-08-17 | - |
dc.identifier.uri | https://zone.biblio.laurentian.ca/handle/10219/3569 | - |
dc.description.abstract | COVID-19 is a highly contagiously atypical pneumonia attributed to a novel coronavirus. The global economy and people's lives have been tremendously affected by the COVID-19 pandemic since its outbreak in Wuhan, Hubei province, China. In this thesis, a non-linear model based on gamma distribution was built to verify the accuracy of the forecasting of the total confirmed cases of COVID-19 two weeks ahead. The daily growth in cases of COVID-19 for different countries was monitored and compared with the forecasted values. The verification of the performance of the non-linear Gamma distribution model has been verified by the non-linear regression. The data for the 19 countries with the most total confirmed COVID-19 cases as of June 22 was used. The data was sourced from the interactive web-based dashboard developed by the Center for System Science and Engineering (CSSE) at Johns Hopkins University. A web page has been developed to provide predictions generated by our models for individuals and public organizations to forecast the trends of COVID19. | en_US |
dc.language.iso | en | en_US |
dc.subject | Forecasting | en_US |
dc.subject | forecasting verification | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | World Health Organization | en_US |
dc.subject | WHO | en_US |
dc.subject | gamma distribution | en_US |
dc.subject | non-linear regression | en_US |
dc.subject | dashboard prototype | en_US |
dc.subject | jQuery | en_US |
dc.title | Forecasting COVID-19 with gamma model | en_US |
dc.type | Thesis | en_US |
dc.description.degree | Master of Science (M.Sc.) in Computational Sciences | en_US |
dc.publisher.grantor | Laurentian University of Sudbury | en_US |
Appears in Collections: | Computational Sciences - Master's theses Master's Theses |
Files in This Item:
File | Description | Size | Format | |
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Thesis_ZhenyaoTang_0375307.pdf | 2.47 MB | Adobe PDF | View/Open |
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