A multi-objective approach to identify parameters of compartmental epidemiological models—Application to Ebola Virus Disease epidemics

Ferrández, Miriam R.; Ivorra, Benjamín; Redondo, Juana López; Ramos, Ángel; Ortigosa, Pilar M.

doi:10.1016/j.cnsns.2023.107165

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Analysis Of Control Measures1

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2023

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Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Thus,

. Based on the work done in [18] , we have

where

represents the decrease of the duration of

due to the application of control measures

and

is the maximum number of days that

can be decreased due to the control measures. The Table 3 presents the description details of duration in days.…”

Section: Analysis Of Control Measuresmentioning

confidence: 99%

Analysis of COVID-19 mathematical model for predicting the impact of control measures in Rwanda

Mpinganzima

Ntaganda

Banzi

et al. 2023

Informatics in Medicine Unlocked

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“…Thus,

. Based on the work done in [18] , we have

where

represents the decrease of the duration of

due to the application of control measures

and

is the maximum number of days that

can be decreased due to the control measures. The Table 3 presents the description details of duration in days.…”

Section: Analysis Of Control Measuresmentioning

confidence: 99%

Analysis of COVID-19 mathematical model for predicting the impact of control measures in Rwanda

Mpinganzima

Ntaganda

Banzi

et al. 2023

Informatics in Medicine Unlocked

View full text Add to dashboard Cite

“…Identifying malware spreading through IoT networks is crucial for developing effective cyberattack mitigation strategies. Existing methodologies involve estimating parameters in epidemiological models; however, estimating these parameters is challenging due to the inherent difficulties in understanding and modeling malware characteristics [19]. Accurately identifying malware parameters, such as propagation and recovery rates, is crucial for anticipating behavior and implementing countermeasures.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Monte Carlo-Based and PINN Parameter Estimation Methods for Malware Identification in IoT Networks

Severt,

Casado-Vara,

Martín del Rey

2023

Technologies

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Malware propagation is a growing concern due to its potential impact on the security and integrity of connected devices in Internet of Things (IoT) network environments. This study investigates parameter estimation for Susceptible-Infectious-Recovered (SIR) and Susceptible–Infectious–Recovered–Susceptible (SIRS) models modeling malware propagation in an IoT network. Synthetic data of malware propagation in the IoT network is generated and a comprehensive comparison is made between two approaches: algorithms based on Monte Carlo methods and Physics-Informed Neural Networks (PINNs). The results show that, based on the infection curve measured in the IoT network, both methods are able to provide accurate estimates of the parameters of the malware propagation model. Furthermore, the results show that the choice of the appropriate method depends on the dynamics of the spreading malware and computational constraints. This work highlights the importance of considering both classical and AI-based approaches and provides a basis for future research on parameter estimation in epidemiological models applied to malware propagation in IoT networks.

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A multi-objective approach to identify parameters of compartmental epidemiological models—Application to Ebola Virus Disease epidemics

Cited by 2 publications

References 24 publications

Analysis of COVID-19 mathematical model for predicting the impact of control measures in Rwanda

Analysis of COVID-19 mathematical model for predicting the impact of control measures in Rwanda

A Comparison of Monte Carlo-Based and PINN Parameter Estimation Methods for Malware Identification in IoT Networks

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