Machine Learning Forecast of Growth in COVID-19 Confirmed Infection Cases with Non-Pharmaceutical Interventions and Cultural Dimensions: Algorithm Development and Validation

Yeung, A. Y.; Roewer-Despres, F.; Rosella, L.; Rudzicz, F.

Background: National governments have implemented non-pharmaceutical interventions to control and mitigate against the COVID-19 pandemic. A deep understanding of these interventions is required. Objective: We investigate the prediction of future daily national Confirmed Infection Growths - the percentage change in total cumulative cases across 14 days - using metrics representative of non-pharmaceutical interventions and cultural dimensions of each country. Methods: We combine the OxCGRT dataset, Hofstede's cultural dimensions, and COVID-19 daily reported infection case numbers to train and evaluate five non-time series machine learning models in predicting Confirmed Infection Growth. We use three validation methods - in-distribution, out-of-distribution, and country-based cross-validation - for evaluation, each applicable to a different use case of the models. Results: Our results demonstrate high R^2 values between the labels and predictions for the in-distribution, out-of-distribution, and country-based cross-validation methods (0.959, 0.513, and 0.574 respectively) using random forest and AdaBoost regression. While these models may be used to predict the Confirmed Infection Growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case. Conclusions: This work provides new considerations in using machine learning techniques with non-pharmaceutical interventions and cultural dimensions data for predicting the national growth of confirmed infections of COVID-19.