Arnold Ys Yeung1,2, Francois Roewer-Despres1,2, Laura Rosella3, Frank Rudzicz1,2,4. 1. Department of Computer Science, University of Toronto, 27 King's College Cir, Toronto, CA. 2. Vector Institute for Artificial Intelligence, Toronto, CA. 3. Dalla Lana School of Public Health, University of Toronto, Toronto, CA. 4. Unity Health Toronto, Toronto, CA.
Abstract
BACKGROUND: National governments have implemented non-pharmaceutical interventions to control and mitigate against the COVID-19 pandemic. OBJECTIVE: We investigate the prediction of future daily national Confirmed Infection Growths - the percentage change in total cumulative cases across 14 days for 114 countries using non-pharmaceutical intervention metrics and cultural dimension metrics, which are metrics indicative of specific national sociocultural norms. 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 R2 values between the labels and predictions for the in-distribution method (0.959), and moderate R2 values for the out-of-distribution and country-based cross-validation methods (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.
BACKGROUND: National governments have implemented non-pharmaceutical interventions to control and mitigate against the COVID-19 pandemic. OBJECTIVE: We investigate the prediction of future daily national Confirmed Infection Growths - the percentage change in total cumulative cases across 14 days for 114 countries using non-pharmaceutical intervention metrics and cultural dimension metrics, which are metrics indicative of specific national sociocultural norms. 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 R2 values between the labels and predictions for the in-distribution method (0.959), and moderate R2 values for the out-of-distribution and country-based cross-validation methods (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.