A Theoretical Linguistic Fuzzy Rule-Based Compartmental Modeling for the COVID-19 Pandemic

Recently 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.

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

International Journal of Modern Physics C ◽

10.1142/s0129183122500917 ◽

2022 ◽

Author(s):

Zakaria Shams Siam ◽

Rubyat Tasnuva Hasan ◽

Hossain Ahamed ◽

Samiya Kabir Youme ◽

Soumik Sarker Anik ◽

...

Keyword(s):

Fuzzy Logic ◽

Compartmental Model ◽

Fuzzy Rule ◽

Transmission Dynamics ◽

Intervention Measures ◽

Proposed Model ◽

Total Death ◽

Linguistic Intervention ◽

Fuzzy Linguistic ◽

Death Cases

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 ([Formula: see text]) 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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Is Nigeria really on top of COVID-19? Message from effective reproduction number

10.1101/2020.05.16.20104471 ◽

2020 ◽

Author(s):

Adeshina Israel Adekunle ◽

Oyelola Adegboye ◽

Ezra Gayawan ◽

Emma McBryde

Keyword(s):

Public Health ◽

Compartmental Model ◽

Reproduction Number ◽

Transmission Rate ◽

Credible Interval ◽

Control Measures ◽

Maximum Value ◽

Intervention Measures ◽

Death Cases ◽

Epidemiological Parameters

Following the importation of Covid-19 into Nigeria on the 27 February 2020 and then the outbreak, the question is: how do we anticipate the progression of the ongoing epidemics following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of Covid-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R_0). This also enables us to estimate the daily transmission rate (β) that determines the effect of social distancing. We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on Covid-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 1.78. Most importantly, the R(t) is strictly greater than one from April 13 till 7 May 2020. The R_0 is estimated to be 2.42 with credible interval: (2.37, 2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below one. Also, the estimated fractional reported symptomatic cases are between 10 to 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

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A Fuzzy Rule-Based Compartmental Modeling for SARS-CoV-2

10.21203/rs.3.rs-68790/v1 ◽

2020 ◽

Author(s):

Zakaria Shams Siam ◽

Rubyat Tasnuva Hasan ◽

Hossain Ahamed ◽

Samiya Kabir Youme ◽

Soumik Sarker Anik ◽

...

Keyword(s):

Compartmental Model ◽

Fuzzy Rule ◽

Compartmental Modeling ◽

Rule Based ◽

Intervention Measures ◽

The Us ◽

If Current ◽

Current Scenario ◽

Prediction Scenario ◽

Asymptomatic Individuals

Abstract Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is the most burning issue all over the world right now. In this study, we have proposed a new fuzzy rule-based Susceptible-Exposed-Infected-Recovered-Death (SEIRD) compartmental model to delineate the intervention and transmission heterogeneity in China, New Zealand, United States and Bangladesh for SARS-CoV-2 viral infection. We have introduced a new dynamic fuzzy transmission possibility variable in the compartmental model. Through our model, we have presented the correspondence of the intervention measures in relaxing the transmission possibility. We estimated that the peak in the US might arrive during the last half of August and for Bangladesh, it might occur during the first half of August, 2020 if current intervention measures are not violated. We have modeled a prediction scenario for Bangladesh if current intervention measures are violated due to Eid-ul-Azha. We further investigated what might happen if Bangladesh government reopens everything from September, 2020. We suggested various effective epidemic control policies for the authority of Bangladesh to fight against the virus. We concluded analyzing the current scenario of Bangladesh suggesting that extensive tests must be carried out collecting more samples of the asymptomatic individuals along with the symptomatic cases and also proper isolation and quarantine measures should be maintained strictly to contain the epidemic sooner.

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From granulation-degranulation mechanisms to fuzzy rule-based models: Augmentation of granular-based models with a double fuzzy clustering

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210336 ◽

2021 ◽

pp. 1-10

Author(s):

Kaijie Xu ◽

Hanyu E ◽

Yinghui Quan ◽

Ye Cui ◽

Weike Nie

Keyword(s):

Design Process ◽

Fuzzy Clustering ◽

Mathematical Proof ◽

Experimental Studies ◽

Fuzzy Model ◽

Fuzzy Rule ◽

Clustering Method ◽

Rule Based ◽

Information Granules ◽

Fuzzy C Means

In this study, we develop a novel clustering with double fuzzy factors to enhance the performance of the granulation-degranulation mechanism, with which a fuzzy rule-based model is designed and demonstrated to be an enhanced one. The essence of the developed scheme is to optimize the construction of the information granules so as to eventually improve the performance of the fuzzy rule-based models. In the design process, a prototype matrix is defined to express the Fuzzy C-Means based granulation-degranulation mechanism in a clear manner. We assume that the dataset degranulated from the formed information granules is equal to the original numerical dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme.

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Effect of Lockdown and Isolation to Suppress the COVID-19 in Bangladesh: An Epidemic Compartments Model

10.20944/preprints202004.0193.v2 ◽

2020 ◽

Cited By ~ 3

Author(s):

Md. Shahidul Islam ◽

Jannatun Irana Ira ◽

K. M. Ariful Kabir ◽

Md. Kamrujjaman

Keyword(s):

Sensitivity Analysis ◽

Basic Reproduction Number ◽

Reproduction Number ◽

Equilibrium Points ◽

Economic Stability ◽

The Social ◽

The World ◽

Over Time ◽

The Basic Reproduction Number

In the promptness of the COVID-19 outbreak, it would be very important to observe and estimate the pattern of diseases to reduce the contagious infection. To study this effect, we developed a COVID-19 analytical epidemic framework that combines with isolation and lockdown effect by incorporating five various groups of individuals. Then we analyze the model by evaluating the equilibrium points and analyzing their stability as well as determining the basic reproduction number. The extensive numerical simulations show the dynamics of a different group of the population over time. Thus, our findings based on the sensitivity analysis and the reproduction number highlight the role of outbreak of the virus that can be useful to avoid a massive collapse in Bangladesh and rest of the world. The outcome of this study concludes that outbreak will be in control which ensures the social and economic stability.

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Is Nigeria really on top of COVID-19? Message from effective reproduction number

Epidemiology and Infection ◽

10.1017/s0950268820001740 ◽

2020 ◽

Vol 148 ◽

Cited By ~ 3

Author(s):

A. I. Adekunle ◽

O. A. Adegboye ◽

E. Gayawan ◽

E. S. McBryde

Keyword(s):

Public Health ◽

Compartmental Model ◽

Reproduction Number ◽

Transmission Rate ◽

Credible Interval ◽

Control Measures ◽

Maximum Value ◽

Intervention Measures ◽

Death Cases ◽

Epidemiological Parameters

Abstract Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) – this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

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COVID-19 Epidemic Compartments Model and Bangladesh

10.20944/preprints202004.0193.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Md. Shahidul Islam ◽

Jannatun Irana Ira ◽

K. M. Ariful Kabir ◽

Md. Kamrujjaman

Keyword(s):

Sensitivity Analysis ◽

Basic Reproduction Number ◽

Reproduction Number ◽

Equilibrium Points ◽

Economic Stability ◽

The Social ◽

The World ◽

Over Time ◽

The Basic Reproduction Number

In the promptness of the COVID-19 outbreak, it would be very important to observe and estimate the pattern of diseases to reduce the contagious infection. To study this effect, we developed a COVID-19 epidemic model that incorporates five various groups of individuals. Then we analyze the model by evaluating the equilibrium points and analyzing their stability as well as determining the basic reproduction number. Also, numerical simulations show the dynamics of a different group of the population over time. Thus, our findings based on the sensitivity analysis and the reproduction number highlight the role of outbreak of the virus that can be useful to avoid a massive collapse in Bangladesh and rest of the world. The outcome of this study concludes that outbreak will be in control which ensure the social and economic stability.

Download Full-text