论文信息 - Parametrization of bass diffusion model on COVID-19 first wave data

Parametrization of bass diffusion model on COVID-19 first wave data

The paper addresses the development of the system for the parameterization of the Bass Diffusion Model on Covid-19 Data. Continuous model developed by the principle of system dynamics is presented. The parameters of the model are tuned to fit the data with genetic algorithm in Powersim simulation tool. The validation is performed by standard simulation validation techniques. The input data used in the experiments were gathered by publically available databases. In particular, the data collected in Austria, France, Italy, South Korea, Slovenia and Swiss were considered in this study. The results have shown that the simple model with is capable of predicting the growth with S-shaped curve with high accuracy.

[1] Jure Leskovec,et al. Mobility network models of COVID-19 explain inequities and inform reopening , 2020, Nature.

[2] Avinandan Mukherjee,et al. The Bass Model: a parsimonious and accurate approach to forecasting mortality caused by COVID-19 , 2020, International Journal of Pharmaceutical and Healthcare Marketing.

[3] Budimir Lutovac,et al. A Headset Like Wearable Device to Track COVID-19 Symptoms , 2020, 2020 9th Mediterranean Conference on Embedded Computing (MECO).

[4] David Adam,et al. A guide to R — the pandemic’s misunderstood metric , 2020, Nature.

[5] Ta-Chou Ng,et al. Epidemiological Benchmarks of the COVID-19 Outbreak Control in China after Wuhan’s Lockdown: A Modelling Study with An Empirical Approach , 2020 .

[6] Wendi Wang,et al. Mathematical models of innovation diffusion with stage structure , 2006 .

[7] Koenraad Debackere,et al. Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium , 2020, Scientometrics.