Network Density Analysis of Health Seeking Behavior in Metro Manila: A Retrospective Analysis on COVID-19 Google Trends Data
Michael T. Lopez, Cheska Elise Hung, Maria Regina Justina E. Estuar
TL;DR
This work addresses how COVID-19-related health-seeking behavior evolves in Metro Manila by analyzing Google Trends using a network-density framework. It constructs time-varying networks of 15 keywords across five categories using distance-correlation adjacency across rolling windows of $15$ and $30$ days and thresholds $\theta \in \{0.4,0.5,0.6,0.8\}$. The study finds an inverse relation between $\theta$ and network metrics, exceptionally high connectivity in the early pandemic, and a shift from policy- to symptom-focused keyword relationships over time, with the $30$-day window yielding more stable patterns. These results inform strategic health communication by identifying robust keyword coalitions and their evolution, aiding targeted information dissemination during health crises.
Abstract
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).