Physics

Control engineering

Pathology

Nonlinear system

Disease

Coronavirus disease 2019 (COVID-19)

Infectious disease (medical specialty)

Outbreak

Engineering

Artificial intelligence

Virology

2019-20 coronavirus outbreak

Computer science

Quantum mechanics

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Artificial neural network

Medicine

Levenberg–Marquardt algorithm

The European Physical Journal Plus

The aim of this work is to design an intelligent computing paradigm through Levenberg–Marquardt artificial neural networks (LMANNs) for solving the mathematical model of Corona virus disease 19 (COVID-19) propagation via human to human interaction. The model is represented with systems of nonlinear ordinary differential equations represented with susceptible, exposed, symptomatic and infectious, super spreaders, infection but asymptomatic, hospitalized, recovery and fatality classes, and reference dataset of the COVID-19 model is generated by exploiting the strength of explicit Runge–Kutta numerical method for metropolitans of China and Pakistan including Wuhan, Karachi, Lahore, Rawalpindi and Faisalabad. The created dataset is arbitrary used for training, validation and testing processes for each cyclic update in Levenberg–Marquardt backpropagation for numerical treatment of the dynamics of COVID-19 model. The effectiveness and reliable performance of the design LMANNs are endorsed on the basis of assessments of achieved accuracy in terms of mean squared error based merit functions, error histograms and regression studies.

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control