Full metadata
Title
Predicting COVID-19 Using Self-Reported Survey Data
Description
Infectious diseases spread at a rapid rate, due to the increasing mobility of the human population. It is important to have a variety of containment and assessment strategies to prevent and limit their spread. In the on-going COVID-19 pandemic, telehealth services including daily health surveys are used to study the prevalence and severity of the disease. Daily health surveys can also help to study the progression and fluctuation of symptoms as recalling, tracking, and explaining symptoms to doctors can often be challenging for patients. Data aggregates collected from the daily health surveys can be used to identify the surge of a disease in a community. This thesis enhances a well-known boosting algorithm, XGBoost, to predict COVID-19 from the anonymized self-reported survey responses provided by Carnegie Mellon University (CMU) - Delphi research group in collaboration with Facebook. Despite the tremendous COVID-19 surge in the United States, this survey dataset is highly imbalanced with 84% negative COVID-19 cases and 16% positive cases. It is tedious to learn from an imbalanced dataset, especially when the dataset could also be noisy, as seen commonly in self-reported surveys. This thesis addresses these challenges by enhancing XGBoost with a tunable loss function, ?-loss, that interpolates between the exponential loss (? = 1/2), the log-loss (? = 1), and the 0-1 loss (? = ∞). Results show that tuning XGBoost with ?-loss can enhance performance over the standard XGBoost with log-loss (? = 1).
Date Created
2021
Contributors
- Vikash Babu, Gokulan (Author)
- Sankar, Lalitha (Thesis advisor)
- Berisha, Visar (Committee member)
- Zhao, Ming (Committee member)
- Trieu, Ni (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
38 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.161579
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2021
Field of study: Computer Science
System Created
- 2021-11-16 02:15:57
System Modified
- 2021-11-30 12:51:28
- 2 years 11 months ago
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