Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention.
Modeling the trend of contagious diseases has particular importance for managing them and reducing the side effects on society. In this regard, researchers have proposed compartmental models for modeling the spread of diseases. However, these models suffer from a lack of adaptability to variations o...
| Publicado en: | Artificial Intelligence in Medicine Vol. 134 |
|---|---|
| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Dec2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160558802&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160558802 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Dec2022 vid: 134 pid: 1004 pub: Elsevier B.V. artinfo: ui: 160558802 160558802 NLM36462905 10.1016/j.artmed.2022.102422 NLM36462905 160558802 ppct: 1 formats: tig: atl: Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention. aug: au: Haghrah, Amir Arslan Ghaemi, Sehraneh Badamchizadeh, Mohammad Ali affil: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran sug: ab: Modeling the trend of contagious diseases has particular importance for managing them and reducing the side effects on society. In this regard, researchers have proposed compartmental models for modeling the spread of diseases. However, these models suffer from a lack of adaptability to variations of parameters over time. This paper introduces a new Fuzzy Susceptible-Infectious-Recovered-Deceased (Fuzzy-SIRD) model for covering the weaknesses of the simple compartmental models. Due to the uncertainty in forecasting diseases, the proposed Fuzzy-SIRD model represents the government intervention as an interval type 2 Mamdani fuzzy logic system. Also, since society's response to government intervention is not a static reaction, the proposed model uses a first-order linear system to model its dynamics. In addition, this paper uses the Particle Swarm Optimization (PSO) algorithm for optimally selecting system parameters. The objective function of this optimization problem is the Root Mean Square Error (RMSE) of the system output for the deceased population in a specific time interval. This paper provides many simulations for modeling and predicting the death tolls caused by COVID-19 disease in seven countries and compares the results with the simple SIRD model. Based on the reported results, the proposed Fuzzy-SIRD model can reduce the root mean square error of predictions by more than 80% in the long-term scenarios, compared with the conventional SIRD model. The average reduction of RMSE for the short-term and long-term predictions are 45.83% and 72.56%, respectively. The results also show that the principle goal of the proposed modeling, i.e., creating a semantic relation between the basic reproduction number, government intervention, and society's response to interventions, has been well achieved. As the results approve, the proposed model is a suitable and adaptable alternative for conventional compartmental models. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|