From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19

Pékpé, Komi Midzodzi; Zitouni, Djamel; Gasso, Gilles; Dhifli, Wajdi; Guinhouya, Benjamin C.

doi:10.1007/s10489-021-02379-2

Cited by 6 publications

(2 citation statements)

References 35 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the novelty of the virus, its epidemiological parameters are unknown, so the SEIR model is fitted to historical COVID-19 data, and the resulting estimated parameters are used to predict future cases. Bayesian optimization [6] , metaheuristics (e.g., particle swarm optimization, stochastic fractal search) [7] – [10] , [104] , [108] – [114] , neural networks [11] , [115] , [116] , and nonlinear curve-fitting based optimization methods [12] , [13] , [117] – [119] are some of the most popular approaches used to fit the model to the data and estimate the epidemiological parameters of the model, such as the reproduction number. In addition to forecasting COVID-19 cases, some studies considered additional aspects, such as the effect of different non-pharmaceutical intervention policies (social distancing and lockdown) and re-opening plans [101] , [114] , [120] – [127] .…”

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

View full text Add to dashboard Cite

This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

show abstract

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In general, the compartment models are usually idealized. While some works are data-driven [ 6 ], most of others can guide some epidemic prevention and control problems in the real world only at the qualitative level but without real data [ 7 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

Injurious information propagation and its global stability considering activity and normalized recovering rate

Wang

Pan

2021

PLoS ONE

View full text Add to dashboard Cite

This paper establishes a compartment model describing the propagation of injurious information among a well-mixed population. We define the information’s injuriousness as the people practicing the information being injured and leaving the system. Some informed people practice the information and are active, while others do not practice and are inactive. With the recovery resources fixed, the two groups of informed people’s recovering rates are normalized considering the information features. The stability of the nonlinear system is thoroughly studied. Analyzing the reproduction number of the injurious information, we find that in general parameter space, when there are people in an informed compartment, it is not always necessary to consider their recovery resource allocation. Instead, only when their proportion reaches a critical point should it be allocated. Unless the people in an informed compartment form a certain proportion, we can take a laissez-faire attitude towards them. In a more realistic parameter space, once inactive informed people exist, they should be allocated recovery resources. On the one hand, when the recovering rate rises, the focus on both groups of informed people is necessary for more situations. On the other hand, when the rate of active informed people leaving the system rises, ignoring active informed people benefits removing the injurious information in more cases. The model provides qualitative ways in the scenarios of removing injurious information.

show abstract

A Deep Learning Based Predictive Model for Healthcare Analytics

Tran

Leung

Madill

et al. 2022

2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)

View full text Add to dashboard Cite

From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19

Cited by 6 publications

References 35 publications

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Injurious information propagation and its global stability considering activity and normalized recovering rate

A Deep Learning Based Predictive Model for Healthcare Analytics

Contact Info

Product

Resources

About