Research PaperNo Access

Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

Zakaria Shams Siam

Department of Electrical & Computer Engineering, North South University, Bashundhara RA, Dhaka 1229, Bangladesh

E-mail Address: zakaria.siam@northsouth.edu

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Rubyat Tasnuva Hasan

Department of Electrical & Computer Engineering, North South University, Bashundhara RA, Dhaka 1229, Bangladesh

E-mail Address: rubyat.tasnuva@northsouth.edu

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Hossain Ahamed

Department of Electrical & Computer Engineering, North South University, Bashundhara RA, Dhaka 1229, Bangladesh

E-mail Address: hossain.ahmed04@northsouth.edu

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Samiya Kabir Youme

Department of Electrical & Computer Engineering, North South University, Bashundhara RA, Dhaka 1229, Bangladesh

E-mail Address: samiya.kabir@northsouth.edu

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Soumik Sarker Anik

Department of Electrical & Computer Engineering, North South University, Bashundhara RA, Dhaka 1229, Bangladesh

E-mail Address: soumik.anik@northsouth.edu

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Sumaia Islam Alita

Department of Electrical & Computer Engineering, North South University, Bashundhara RA, Dhaka 1229, Bangladesh

E-mail Address: islam.sumaia@northsouth.edu

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, and

Rashedur M. Rahman

Department of Electrical & Computer Engineering, North South University, Bashundhara RA, Dhaka 1229, Bangladesh

E-mail Address: rashedur.rahman@northsouth.edu

Corresponding author.

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https://doi.org/10.1142/S0129183122500917Cited by:0 (Source: Crossref)

Abstract

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ( $S E I R D$ $S E I R D$ ) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

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