2025/03/27 by Lopez, Michael T., Hung, Cheska Elise, Maria Regina Justina E. Estuar +1
Mathematics · Medicine · Physics and Astronomy · #COVID-19 epidemiological studies #Cluster analysis #Complex Network Analysis Techniques #Computers and Society (cs.CY) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Government (linguistics) #I.6.3 #Information Retrieval (cs.IR) #J.3 #Key (lock) #Network analysis #Pandemic #Social network analysis
paper · pdf · doi:10.48550/arxiv.2503.21162
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This study examined the temporal aspect of COVID-19-related health-seeking behavior in Metro Manila, National Capital Region, Philippines through a network density analysis of Google Trends data. A total of 15 keywords across five categories (English symptoms, Filipino symptoms, face wearing, quarantine, and new normal) were examined using both 15-day and 30-day rolling windows from March 2020 to March 2021. The methodology involved constructing network graphs using distance correlation coefficients at varying thresholds (0.4, 0.5, 0.6, and 0.8) and analyzing the time-series data of network density and clustering coefficients. Results revealed three key findings: (1) an inverse relationship between the threshold values and network metrics, indicating that higher thresholds provide more meaningful keyword relationships; (2) exceptionally high network connectivity during the initial pandemic months followed by gradual decline; and (3) distinct patterns in keyword relationships, transitioning from policy-focused searches to more symptom-specific queries as the pandemic temporally progressed. The 30-day window analysis showed more stable, but less search activities compared to the 15-day windows, suggesting stronger correlations in immediate search behaviors. These insights are helpful for health communication because it emphasizes the need of a strategic and conscientious information dissemination from the government or the private sector based on the networked search behavior (e.g. prioritizing to inform select symptoms rather than an overview of what the coronavirus is).