Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study.

Publication date: Dec 16, 2024

The number of confirmed COVID-19 cases is a crucial indicator of policies and lifestyles. Previous studies have attempted to forecast cases using machine learning techniques that use a previous number of case counts and search engine queries predetermined by experts. However, they have limitations in reflecting temporal variations in queries associated with pandemic dynamics. This study aims to propose a novel framework to extract keywords highly associated with COVID-19, considering their temporal occurrence. We aim to extract relevant keywords based on pandemic variations using query expansion. Additionally, we examine time-delayed web-based search behavior related to public interest in COVID-19 and adjust for better prediction performance. To capture temporal semantics regarding COVID-19, word embedding models were trained on a news corpus, and the top 100 words related to “Corona” were extracted over 4-month windows. Time-lagged cross-correlation was applied to select optimal time lags correlated to confirmed cases from the expanded queries. Subsequently, ElasticNet regression models were trained after reducing the feature dimensions using principal component analysis of the time-lagged features to predict future daily case counts. Our approach successfully extracted relevant keywords depending on the pandemic phase, encompassing keywords directly related to COVID-19, such as its symptoms, and its societal impact. Specifically, during the first outbreak, keywords directly linked to COVID-19 and past infectious disease outbreaks similar to those of COVID-19 exhibited a high positive correlation. In the second phase of the pandemic, as community infections emerged, keywords related to the government’s pandemic control policies were frequently observed with a high positive correlation. In the third phase of the pandemic, during the delta variant outbreak, keywords such as “economic crisis” and “anxiety” appeared, reflecting public fatigue. Consequently, prediction models trained by the extracted queries over 4-month windows outperformed previous methods for most predictions 1-14 days ahead. Notably, our approach showed significantly higher Pearson correlation coefficients than models based solely on the number of past cases for predictions 9-11 days ahead (P=. 02, P

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Concepts Keywords
Elasticnet case prediction
Korea confirmed case prediction
Month COVID-19
Pandemic COVID-19
Principal Forecasting
Humans
infodemiology
infodemiology study
lifestyle
Machine Learning
machine learning
machine learning techniques
model
novel framework
Pandemics
policy
prediction model
public health
query expansion
Republic of Korea
SARS-CoV-2
Search Engine
search engine
search engine queries
South Korea
temporal
temporal semantics
temporal variation
Time Factors
utilization
web-based search
word embedding

Semantics

Type Source Name
disease MESH COVID-19
disease MESH lifestyles
drug DRUGBANK Tropicamide
disease MESH infectious disease
pathway REACTOME Infectious disease
disease MESH infections
disease MESH anxiety

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