Utilizing Dynamic Time Warping for Pandemic Surveillance: Understanding the Relationship between Google Trends Network Metrics and COVID-19 Incidences

Abstract: The premise of network statistics derived from Google Trends data to foresee COVID-19 disease progression is gaining momentum in infodemiology. This approach was applied in Metro Manila, National Capital Region, Philippines. Through dynamic time warping (DTW), the temporal alignment was quantified between network metrics and COVID-19 case trajectories, and systematically explored 320 parameter configurations including two network metrics (network density and clustering coefficient), two data preprocessing methods (Rescaling Daily Data and MSV), multiple thresholds, two correlation window sizes, and Sakoe-Chiba band constraints. Results from the Kruskal-Wallis tests revealed that five of the six parameters significantly influenced alignment quality, with the disease comparison type (active cases vs. confirmed cases) demonstrating the strongest effect. The optimal configuration, which is using the network density statistic with a Rescaling Daily Data transformation, a threshold of 0.8, a 15-day window, and a 50-day radius constraint, achieved a DTW score of 36.30. This indicated substantial temporal alignment with the COVID-19 confirmed cases data. The discoveries demonstrate that network metrics rooted from online search behavior can serve as complementary indicators for epidemic surveillance in urban locations like Metro Manila. This strategy leverages the Philippines' extensive online usage during the pandemic to provide potentially valuable early signals of disease spread, and offers a supplementary tool for public health monitoring in resource-limited situations.