Real-Time Forecasting of COVID-19 Cases Using Human Mobility in Ontario, Canada

ABSTRACT

Purpose To minimize the impact of the COVID-19 pandemic, local public health authorities are often required to make prompt and informed decisions on anticipated case-loads, resource allocation for surveillance and testing, and public health intervention appropriateness. The objective of this research was to develop a near-term forecasting model to predict COVID-19 cases using real-time human mobility information in Ontario, Canada to assist public health authorities with outbreak response. Methods & Materials We utilized a deep neural network model to generate a short-term forecast of new COVID-19 cases by two weeks from May to August 2021. Variable selection was informed by a recent literature review and our ongoing research associating COVID-19 cases with human mobility, demographic and socio-economic factors. A real-time human mobility statistics consisting of a weekly summary of short and long-distance movement, demographic characteristics, weather, vaccination coverage, geo-location, and reported COVID-19 cases two weeks prior were included as predictors. We considered weeks as temporal and health regions as geographic units to account for population-level variabilities. We used a holdout method for model validation of over 300 iterations. Average mean squared error (MSE) and 95% confidence interval (CI) along with overlaying forecasted COVID-19 cases over the reported were used to evaluate the overall model fit. The model predictions were summarized as means and 95% CIs. Results Our best forecasting model had a mean MSE of 0.53 (95% CI 0.49 – 0.56). Since May 2021, the overall trend of the reported COVID-19 cases in Ontario closely followed the forecasted cases, about 89% of the reported cases were within 1.5 times the interquartile range (IQR) and all were within the entire range of the distribution of the predictions. Forecasting accuracy also varied by health region characteristics, such a population size and density, remoteness, and reported COVID-19 case volume during the most recent weeks. Conclusion A near-term prediction of new COVID-19 cases with real-time population-level data could help public health authorities anticipate, plan and monitor disease burden in a population. Such predictions also allow the assessment of population-level health interventions to minimize new COVID-19 cases on a real-time basis and inform prompt decision making.