COVID-19: Forecasting mortality given mobility trend data and non-pharmaceutical interventions

TL;DR

This paper introduces a hybrid epidemiological model that quantifies the impact of mobility restrictions and other NPIs on COVID-19 spread, achieving high accuracy in short-term mortality forecasts across US states and worldwide.

Contribution

A novel hybrid model that separates the effects of mobility restrictions from other NPIs and demonstrates accurate short-term COVID-19 mortality forecasting.

Findings

Mobility restrictions contributed to approximately 47% of transmission suppression.

The model achieved a mean absolute percentage error of around 5-7% in mortality forecasts.

High R-squared values (>0.98) indicate excellent fit to observed data.

Abstract

We develop a novel hybrid epidemiological model and a specific methodology for its calibration to distinguish and assess the impact of mobility restrictions (given by Apple's mobility trends data) from other complementary non-pharmaceutical interventions (NPIs) used to control the spread of COVID-19. Using the calibrated model, we estimate that mobility restrictions contribute to 47 % (US States) and 47 % (worldwide) of the overall suppression of the disease transmission rate using data up to 13/08/2020. The forecast capacity of our model was evaluated doing four-weeks ahead predictions. Using data up to 30/06/20 for calibration, the mean absolute percentage error (MAPE) of the prediction of cumulative deceased individuals was 5.0 % for the United States (51 states) and 6.7 % worldwide (49 countries). This MAPE was reduced to 3.5% for the US and 3.8% worldwide using data up to 13/08/2020. We find that the MAPE was higher for the total confirmed cases at 11.5% worldwide and 10.2% for the US States using data up to 13/08/2020. Our calibrated model achieves an average R-Squared value for cumulative confirmed and deceased cases of 0.992 using data up to 30/06/20 and 0.98 using data up to 13/08/20.