A novel diffusion-based model for estimating cases, and fatalities in epidemics: the case of COVID-19

Eryarsoy, Enes and Delen, Dursun and Davazdahemami, Behrooz and Topuz, Kazim (2021) A novel diffusion-based model for estimating cases, and fatalities in epidemics: the case of COVID-19. Journal of Business Research, 124 . pp. 163-178. ISSN 0148-2963 (Print) 1873-7978 (Online)

Full text not available from this repository. (Request a copy)


While the COVID-19 pandemic is still ongoing in a majority of countries, a wealth of literature published in reputable journals attempted to model the spread of the disease. A vast majority of these studies dealt with compartmental models such as susceptible-infected-recovered (SIR) model. Although these models are rather simple, intuitive, and insightful, we argue that they do not necessarily provide a good enough fit to the reported data, which are usually reported in the form of daily fatalities and cases during pandemics. This study proposes an alternative analytics approach that relies on diffusion models to predict the number of cases and fatalities in epidemics. After evaluating several of the well-known and widely used diffusion models in business literature, including ADBUDG, Gompertz, and Bass models, we developed and used a modified/improved version of the original Bass diffusion model to address the shortcomings of the ordinary compartmental models such as SIR and demonstrated its applicability on the portrayal of the COVID-19 pandemic incident data. The proposed model differentiates itself from other similar models by fitting the data without the need for preprocessing, requiring no initial conditions and assumptions, not involving in heavy parameterization, and also properly addressing the pressing issues such as undocumented cases, length of infectious or recovery periods.
Item Type: Article
Uncontrolled Keywords: COVID-19; Diffusion models; Epidemic; Fatality; Prediction
Divisions: Sabancı Business School
Depositing User: Enes Eryarsoy
Date Deposited: 03 Sep 2022 22:26
Last Modified: 03 Sep 2022 22:26
URI: https://research.sabanciuniv.edu/id/eprint/43484

Actions (login required)

View Item
View Item