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Efficient estimation of influenza epidemics based on seasonal ARIMA models
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- Authors
- Advisor
- 이상열
- Major
- 자연과학대학 통계학과
- Issue Date
- 2016-02
- Publisher
- 서울대학교 대학원
- Keywords
- SARIMA model ; ARGO ; Seasonal effect ; Influenza epidemics ; Disease detection ; Influenza-like illnesses activity estimation ; Big data
- Description
- 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2016. 2. 이상열.
- Abstract
- The issue of big data has received a lot of attention from researchers
working in various fields as big data sets have been considered to have great
potential in detecting or predicting future events such as epidemic outbreaks
and changes in stock prices. Reflecting the current popularity of big data,
a number of studies have been published to propose models that track influenza
epidemics by using internet-based information. The most recent version
of influenza tracking model was ARGO, which harnesses search queries
from Google and reports from the Centers for Disease Control (CDC). Although
the ARGO appears to predict the outbreaks of influenza accurately
and outperform other existing methods such as Google Flu Trends (GFT), in
this study, a classical seasonal autoregressive and integrated moving average
(SARIMA) model is demonstrated to show that it outperforms the ARGO.
Compared to the ARGO, the time series model incorporates more accurate
seasonality of the past influenza activities, and takes less input variables
into account since it utilizes only the reports provided by the CDC. The
findings in this study show that the SARIMA model is a more efficient tool
for estimating influenza epidemics.
- Language
- English
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