Integrating Google Mobility Indices for Forecasting Infectious Diseases Incidence: A Multi‐Country Study on COVID‐19 With LightGBM
Abstract
Reliable forecasts of infectious disease trajectories are indispensable for timely public health action and allocation of medical resources. However, most time‐series forecasting frameworks still rely solely on historical case counts and thus struggle to capture sudden shifts in population behavior. Therefore, to quantify the value of external behavioral signals during the COVID‐19 pandemic, this research assembled a 124‐week (from May 31, 2020, to October 9, 2022) panel that fuses Google Community‐Mobility indices with standard surveillance indicators such as new cases, deaths, tests, and vaccinations plus information about population density and the Oxford policy‐stringency score for 20 countries spanning six continents. We proceed to assess two forecasting methodological families for predicting new cases using an 8‐week hold‐out window. The target‐variable‐only family comprised models using a 4‐week rolling average, autoregressive integrated moving average (ARIMA), Prophet, and long short‐term memory (LSTM) approaches. In contrast, the data‐integration family employs distinct light gradient boosting machine (LightGBM) variants: LightGBM‐Direct, which learns a single multi‐output mapping for all periods in the horizon, and LightGBM‐Recursive, which updates a one‐step model and rolls its predictions forward. Performance is evaluated using root mean square error (RMSE) and two optimized weight indices (OWIs), which benchmark improvements over the rolling‐average baseline and…
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