2020/09/25 by Víctor Hugo Grisales Díaz, Diaz, Victor Hugo Grisales, Oscar Andrés Prado‐Rubio +3
Mathematics · Medicine · #COVID-19 and healthcare impacts #COVID-19 epidemiological studies #Classical Analysis and ODEs (math.CA) #FOS: Biological sciences #FOS: Mathematics #Optimization and Control (math.OC) #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2009.12171
openalex publication_date 2020/09/25 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28
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.