Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses

Botmart, Thongchai; Sabir, Zulqurnain; Javeed, Shumaila; Núñez, Rafaél Artidoro Sandoval; Weera, Wajaree; Ali, Mohamed R.; Sadat, R.

doi:10.1016/j.imu.2022.101028

Cited by 8 publications

(2 citation statements)

References 52 publications

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“…Recently, ANN-based solvers have been exploited for the numerical treatment of the COVID system with its variants [30][31][32][33][34][35][36][37][38][39], HIV infection system [40][41][42], Dange fever [2,[43][44][45][46], hepatitis virus system [47], influenza virus [48][49][50], and HBV virus [7]. The majority of these ANN-based modeling uses log-sigmoid, tan-sigmoid, and radial basis functions as an activation function, however, there is a need to explore other activation functions like the Mexican hat wavelet which has theoretically good approximation capabilities.…”

Section: Introductionmentioning

confidence: 99%

Swarm-optimized numerical investigation of Dengue Fever Model

Riaz,

Ullah,

Alasiry

et al. 2024

Preprint

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The current study aims to present a swarm-optimized technique for the numerical treatment of dengue fever non-linear model. The model is composed of the coupled nonlinear system comprising the susceptible, infected, and recovered compartments. The system is transformed into an unsupervised single layer feed-forward artificial neural network with a Mexican hat wavelet activation function in the hidden layer. The unknowns of the neural network is optimized with particle swarm optimization as an efficient global search aided by the effective local search technique based on sequential quadratic programming. The presented results are compared with state of art Runge-Kutta method and other modern reported techniques on various performance indicators like absolute error, mean average deviation, global absolute error, global mean average deviation, convergence, and computational complexity. Comprehensive Monte Carlo simulations and their statistical analysis are presented to ensure accuracy, consistency in convergence, and computational complexity in terms of execution time. It is observed that the proposed scheme is accurate, reliable, convergent, and computationally viable in treating the nonlinear coupled system under consideration.

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Section: Introductionmentioning

confidence: 99%

Swarm-optimized numerical investigation of Dengue Fever Model

Riaz,

Ullah,

Alasiry

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Orthodox, predictive models also generate time-series that can be compared to field epidemiology time-series [162,163]. Machine Learning (ML) [143,[164][165][166], ANN [167][168][169] and GA [168,169] have found orthodox, predictive model parameters that adequately replicate COVID-19 field time-series. Deep learning is a neurologically inspired ML technique [146,154,156,164] that has also been used in orthodox, predictive model parameter estimation [170,171].…”

Section: Introductionmentioning

confidence: 99%

Reproduction number projection for the COVID-19 pandemic

Benjamin

2023

Adv Cont Discr Mod

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The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic compartment model for epidemics and an alternative to the Susceptible-Infected-Removed (SIR) model. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and social behaviour common within that population.The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number $\mathcal{R}_{0}$ R 0 for the United Kingdom (UK) could have been projected onto the individual states of the United States of America (USA) prior to being detected in the USA.The Imperial College London (ICL) group’s estimate of $\mathcal{R}_{0}$ R 0 for the UK is projected onto each USA state. The difference between these projections and the ICL’s estimates for USA states is either not statistically significant on the paired Student t-test or not epidemiologically significant.The SARS-CoV2 Delta variant’s $\mathcal{R}_{0}$ R 0 is also projected from the UK to the USA to prove that projection can be applied to a Variant of Concern (VOC). Projection provides both a localised baseline for evaluating the implementation of an intervention policy and a mechanism for anticipating the impact of a VOC before local manifestation.

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