The premise of network statistics derived from Google Trends data to foresee
COVID-19 disease progression is gaining momentum in infodemiology. Ce
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, et
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.

Cet article explore les excursions dans le temps et leurs implications.

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