ABSTRACT
The COVID-19 pandemic has affected the whole world quite seriously. The number of new infectious cases and death cases are rapidly increasing over time. In this study, a theoretical linguistic fuzzy rule-based susceptible-exposed-infectious-isolated-recovered (SEIIsR) compartmental model has been proposed to predict the dynamics of the transmission of COVID-19 over time considering population immunity and infectiousness heterogeneity based on viral load in the model. The model's equilibrium points have been calculated, and stability analysis of the model's equilibrium points has been conducted. Consequently, the fuzzy basic reproduction number, R0f, of the fuzzy model has been formulated. Finally, the temporal dynamics of different compartmental populations with immunity and infectiousness heterogeneity using the fuzzy Mamdani model are delineated, and some disease control policies have been suggested to get over the infection in no time. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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 (SEIRD) 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. © 2022 World Scientific Publishing Company.
ABSTRACT
Bangladesh has been combating the COVID-19 pandemic with limited financial resources and poor health infrastructure since March, 2020. Although the government has imposed several restricted measures to curb the progression of the outbreak, these arrays of measures are not sustainable in the long run. In this paper, we use a data-driven forecasting model considering susceptible, exposed, infected, recovered and deaths status through time to assess the impact of lift of flexible lockdown on the COVID-19 dynamics in Bangladesh. Our analysis demonstrates that the country might experience second infection peak in six to seven months after the withdrawal of current lockdown. Moreover, a prolonged restrictions until January, 2021 will shift the infection peak towards August, 2021 and reduce approximately 20% COVID-19 cases in Bangladesh. © 2021 World Scientific Publishing Company.