Hybrid Modeling of Ebola Propagation

Tanade, Cyrus; Pate, Nathanael; Paljug, Elianna; Hoffman, Ryan; Wang, May D.

doi:10.1109/bibe.2019.00044

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…This is necessary for mainstreaming the concept proposed in the study. An interesting SIR model, based on EBOV, combining agentbased and compartmental models, has been presented [5]. The authors suggested that the hybrid model can switch from one paradigm to another on a stochastic threshold.…”

Section: The Ebola Virus (Ebov) and Ebola Virus Diseases (Evd)mentioning

confidence: 99%

“…SEIR-based models designed for EVD have been proposed in the literature to monitor both direct and indirect propagation of the disease in the affected population [45] and [5]. This study adopts and adapts two relevant models from the existing SEIR models by identifying and adding new compartments perceived as omitted.…”

Section: Sir Model Of Eosamentioning

confidence: 99%

“…This transmission is aided by contact with any form of body fluid of an infected person. A healthy person comes in contact with infected objects since the Ebola virus can survive on dry surfaces such as doorknobs and countertops for several hours [4], [5]. The hemorrhagic disease, known to be notoriously fatal, has been reported to have mortality rates ranging from 25% to 90%, with an average of 50% mainly due to fluid loss rather than blood loss [6], [7].…”

Section: Introductionmentioning

confidence: 99%

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Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

et al. 2022

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Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola optimization search algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), exposed (E), Infected (I), Hospitalized (H), recovered (R), Vaccinated (V), Quarantine (Q), and Death or death (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%.

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Section: The Ebola Virus (Ebov) and Ebola Virus Diseases (Evd)mentioning

confidence: 99%

Section: Sir Model Of Eosamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

et al. 2022

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“…The mathematical model of the procedure above follows: update of Susceptible (S), Infected (I), Hospitalized (H), Exposed (E), Vaccinated (V), Recovered (R), Funeral (F), Quarantine (Q), and Dead (D) is governed by a system of ordinary differential equations derived based on those in 29 , 30 . Differential calculus is a branch of calculus which in turn is a branch in mathematics.…”

Section: Overview Of Eosa and Review Of Related Studiesmentioning

confidence: 99%

A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images

Oyelade

Ezugwu

2021

Sci Rep

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The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and evaluation strategy. The approach supports the automation of deep learning (DL) based networks such as convolutional neural networks (CNN). Automating the process of CNN architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets. However, the engineering process of NAS is often limited by the potential solutions in search space and the search strategy. This problem often narrows the possibility of obtaining best performing networks for challenging tasks such as the classification of breast cancer in digital histopathological samples. This study proposes a NAS model with a novel search space initialization algorithm and a new search strategy. We designed a block-based stochastic categorical-to-binary (BSCB) algorithm for generating potential CNN solutions into the search space. Also, we applied and investigated the performance of a new bioinspired optimization algorithm, namely the Ebola optimization search algorithm (EOSA), for the search strategy. The evaluation strategy was achieved through computation of loss function, architectural latency and accuracy. The results obtained using images from the BACH and BreakHis databases showed that our approach obtained best performing architectures with the top-5 of the architectures yielding a significant detection rate. The top-1 CNN architecture demonstrated a state-of-the-art performance of base on classification accuracy. The NAS strategy applied in this study and the resulting candidate architecture provides researchers with the most appropriate or suitable network configuration for using digital histopathology.

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“…Over the last 20 years, many different viruses have spread through different countries via airport networks. One of the well-known cases was the 2014 Ebola outbreak, which started in West Africa and caused more than 11,000 deaths worldwide ( Bailey et al, 2018 ; Tanade et al, 2019 ). Another notable case was the H1N1 virus, which was initiated in 2009 in Mexico and proceeded to spread in more than 70 countries, causing nearly 650,000 deaths between 2009 and 2010 ( Marziano et al, 2017 ; Ng et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review.

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Hybrid Modeling of Ebola Propagation

Cited by 5 publications

References 14 publications

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images

COVID-19 spread algorithm in the international airport network-DetArpds

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