Predictive Power of Online Search Data for COVID-19 Forecasting - Čelková, Barbora; Holý, Vladimír; Jindrová, Karolína; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
This thesis investigates whether online search data can enhance short-term forecasts of COVID-19 cases in the Czech Republic, utilizing weekly counts of new cases from March 2020 to April 2023 and search activity from Google Trends. The analyzed keywords comprise a set of general, symptom, and prevention-related queries, as well as their principal components. We first estimate an ARIMA model based solely on reported case numbers, which serves as a benchmark for all subsequent Google-augmented specifications. Afterwards, a set of models incorporating Google Trends is estimated, namely a regression with ARMA errors, a vector autoregression, and a vector error correction models. Using expanding-window forecast, we compare their predictive performance to the benchmark ARIMA model with standard forecast accuracy metrics in combination with Diebold–Mariano tests. The results show that while some Google Trends series can provide statistically significant gains in forecast accuracy, these improvements are heavily dependent on the chosen model and analyzed time period. Overall, the findings suggest that incorporating Google Trends into traditional statistical models could offer some predictive value for COVID-19 surveillance in the Czech Republic, but it does not deliver uniformly superior forecasts.
Vedoucí práce: Vladimír Holý
Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2026
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2026
Způsob přístupu: Internet
Keywords: ekonometrie a operační výzkum [obor dipl. práce]; diplomové práce; master's theses