Optimal control of a fractional order model for granular SEIR epidemic with uncertainty

Dong, Nguyen Phuong; Long, Hoàng Việt; Khastan, A.

doi:10.1016/j.cnsns.2020.105312

Cited by 55 publications

(25 citation statements)

References 37 publications

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“…Many researchers, in the past, have formulated various epidemiological systems (SIS, SI, SIR, SEIR, SIERS, SEIVR, etc.) with distinct treatment procedures to keep the disease in control [ 4 , 12 , 26 , 39 , 45 , 46 ]. The famous SIR compartmental system was formulated by Kermack and McKendrick [ 23 ] in 1927.…”

Section: Introductionmentioning

confidence: 99%

Dynamic analysis of the role of innate immunity in SEIS epidemic model

Jain

Kumar

2021

Eur. Phys. J. Plus

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Section: Introductionmentioning

confidence: 99%

Dynamic analysis of the role of innate immunity in SEIS epidemic model

Jain

Kumar

2021

Eur. Phys. J. Plus

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“…For further research, we propose to extend these results to study the existence of both (i)-differentiable and (ii)-differentiable solutions of the mixed continuous-discrete fuzzy model corresponding to each case of a. The current work opens up many potential results in studying control problems or numerical algorithms for the fuzzy differential-difference equations, that are inspired by pioneer works [25][26][27][28][29].…”

Section: Discussionmentioning

confidence: 93%

A New Continuous-Discrete Fuzzy Model and Its Application in Finance

2020

Self Cite

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In this paper, we propose a fuzzy differential-difference equation for modeling of mixed continuous-discrete phenomena. In the special case, we present the general solution of linear fuzzy differential-difference equations. The dynamical process in the intervals is presented by the corresponding fuzzy differential equation and with impulsive jumps in some points. We illustrate the applicability of the model to study the time value of money.

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“…Although quick to develop with fewer datasets and foci [12], it has already been determined that standard models fitted to confirmed cases of COVID-19 are anticipated to have a high level of uncertainty, largely due to the deviation on reported dates for confirmed cases [13]. Moreover, the popularized Susceptible-Exposed-Infectious-Recovered (SEIR) model, as well as the simpler, SIR model, can both provide useful insights into the effectiveness of transmission prevention strategies, but can still be expected to derive results with considerable uncertainty [14].…”

Section: Machine Learning To Address Covid-19mentioning

confidence: 99%

Artificial intelligence to predict the risk of mortality from Covid-19: Insights from a Canadian Application

Snider

Phillips

Maclean

et al. 2020

Preprint

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The Severe Acute Respiratory Syndrome COVID-19 virus (SARS-CoV-2) has had enormous impacts, indicating need for non-pharmaceutical interventions (NPIs) using Artificial Intelligence (AI) modeling. Investigation of AI models and statistical models provides important insights within the province of Ontario as a case study application using patients' physiological conditions, symptoms, and demographic information from datasets from Public Health Ontario (PHO) and the Public Health Agency of Canada (PHAC). The findings using XGBoost provide an accuracy of 0.9056 for PHO, and 0.935 for the PHAC datasets. Age is demonstrated to be the most important variable with the next two variables being Hospitalization and Occupation. Further, AI models demonstrate identify the importance of improved medical practice which evolved over the six months in treating COVID-19 virus during the pandemic, and that age is absolutely now the key factor, with much lower importance of other variables that were important to mortality near the beginning of the pandemic. An XGBoost model is shown to be fairly accurate when the training dataset surpasses 1000 cases, indicating that AI has definite potential to be a useful tool in the fight against COVID-19 even when caseload numbers needed for effective utilization of AI model are not large.

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Optimal control of a fractional order model for granular SEIR epidemic with uncertainty

Cited by 55 publications

References 37 publications

Dynamic analysis of the role of innate immunity in SEIS epidemic model

Dynamic analysis of the role of innate immunity in SEIS epidemic model

A New Continuous-Discrete Fuzzy Model and Its Application in Finance

Artificial intelligence to predict the risk of mortality from Covid-19: Insights from a Canadian Application

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