A design of predictive computational network for transmission model of Lassa fever in Nigeria

Muhammad Shoaib; Rafia Tabassum; Muhammad Asif Zahoor Raja; Kottakkaran Sooppy Nisar; Mohammed S. Alqahtani; Mohamed Abbas

doi:10.1016/j.rinp.2022.105713

A design of predictive computational network for transmission model of Lassa fever in Nigeria

Muhammad Shoaib
, Rafia Tabassum
, Muhammad Asif Zahoor Raja
, Kottakkaran Sooppy Nisar
, Mohammed S. Alqahtani
, Mohamed Abbas

Mathematics

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

Abstract

This paper presents an innovative artificial neural networks (ANNs) based hybrid algorithm of genetics optimization and sequential quadratic programming (AGOSQP) to construct the mathematical model for the dynamics of Lassa fever (DLF) in Nigeria. The model designated by the transmission of disease between two populations: human population i.e. susceptible S_h, exposed E_h, infectious I_h and recovered R_h humans and rodent population i.e. susceptible S_r and infectious I_r rodents. The log sigmoid function as an objective function based on mean squared error is constructed to optimize AGOSQP where genetic algorithm work as global searching optimization and SQP serve as the local searching optimization. To assess the correctness, robustness and convergence stability, the comparison between state of art Adam method and proposed AGOSQP is established. The Theil's inequality coefficient (TIC), root mean square error (RMSE) and mean absolute deviation (MAD) are also computed to authenticate the efficiency of proposed AGOSQP to solve the model for the dynamics of Lassa fever.

Original language	English
Article number	105713
Journal	Results in Physics
Volume	39
DOIs	https://doi.org/10.1016/j.rinp.2022.105713
State	Published - Aug 2022

Keywords

Adam method
Genetic algorithm
Lassa fever
Neural Networks
Sequential quadratic programming

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.rinp.2022.105713

Cite this

@article{32a916aa79b940ce81a2d1de5c29476d,

title = "A design of predictive computational network for transmission model of Lassa fever in Nigeria",

abstract = "This paper presents an innovative artificial neural networks (ANNs) based hybrid algorithm of genetics optimization and sequential quadratic programming (AGOSQP) to construct the mathematical model for the dynamics of Lassa fever (DLF) in Nigeria. The model designated by the transmission of disease between two populations: human population i.e. susceptible Sh, exposed Eh, infectious Ih and recovered Rh humans and rodent population i.e. susceptible Sr and infectious Ir rodents. The log sigmoid function as an objective function based on mean squared error is constructed to optimize AGOSQP where genetic algorithm work as global searching optimization and SQP serve as the local searching optimization. To assess the correctness, robustness and convergence stability, the comparison between state of art Adam method and proposed AGOSQP is established. The Theil's inequality coefficient (TIC), root mean square error (RMSE) and mean absolute deviation (MAD) are also computed to authenticate the efficiency of proposed AGOSQP to solve the model for the dynamics of Lassa fever.",

keywords = "Adam method, Genetic algorithm, Lassa fever, Neural Networks, Sequential quadratic programming",

author = "Muhammad Shoaib and Rafia Tabassum and Raja, \{Muhammad Asif Zahoor\} and Nisar, \{Kottakkaran Sooppy\} and Alqahtani, \{Mohammed S.\} and Mohamed Abbas",

note = "Publisher Copyright: {\textcopyright} 2022",

year = "2022",

month = aug,

doi = "10.1016/j.rinp.2022.105713",

language = "English",

volume = "39",

journal = "Results in Physics",

issn = "2211-3797",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - A design of predictive computational network for transmission model of Lassa fever in Nigeria

AU - Shoaib, Muhammad

AU - Tabassum, Rafia

AU - Raja, Muhammad Asif Zahoor

AU - Nisar, Kottakkaran Sooppy

AU - Alqahtani, Mohammed S.

AU - Abbas, Mohamed

PY - 2022/8

Y1 - 2022/8

N2 - This paper presents an innovative artificial neural networks (ANNs) based hybrid algorithm of genetics optimization and sequential quadratic programming (AGOSQP) to construct the mathematical model for the dynamics of Lassa fever (DLF) in Nigeria. The model designated by the transmission of disease between two populations: human population i.e. susceptible Sh, exposed Eh, infectious Ih and recovered Rh humans and rodent population i.e. susceptible Sr and infectious Ir rodents. The log sigmoid function as an objective function based on mean squared error is constructed to optimize AGOSQP where genetic algorithm work as global searching optimization and SQP serve as the local searching optimization. To assess the correctness, robustness and convergence stability, the comparison between state of art Adam method and proposed AGOSQP is established. The Theil's inequality coefficient (TIC), root mean square error (RMSE) and mean absolute deviation (MAD) are also computed to authenticate the efficiency of proposed AGOSQP to solve the model for the dynamics of Lassa fever.

AB - This paper presents an innovative artificial neural networks (ANNs) based hybrid algorithm of genetics optimization and sequential quadratic programming (AGOSQP) to construct the mathematical model for the dynamics of Lassa fever (DLF) in Nigeria. The model designated by the transmission of disease between two populations: human population i.e. susceptible Sh, exposed Eh, infectious Ih and recovered Rh humans and rodent population i.e. susceptible Sr and infectious Ir rodents. The log sigmoid function as an objective function based on mean squared error is constructed to optimize AGOSQP where genetic algorithm work as global searching optimization and SQP serve as the local searching optimization. To assess the correctness, robustness and convergence stability, the comparison between state of art Adam method and proposed AGOSQP is established. The Theil's inequality coefficient (TIC), root mean square error (RMSE) and mean absolute deviation (MAD) are also computed to authenticate the efficiency of proposed AGOSQP to solve the model for the dynamics of Lassa fever.

KW - Adam method

KW - Genetic algorithm

KW - Lassa fever

KW - Neural Networks

KW - Sequential quadratic programming

UR - https://www.scopus.com/pages/publications/85132362736

U2 - 10.1016/j.rinp.2022.105713

DO - 10.1016/j.rinp.2022.105713

M3 - Article

AN - SCOPUS:85132362736

SN - 2211-3797

VL - 39

JO - Results in Physics

JF - Results in Physics

M1 - 105713

ER -

A design of predictive computational network for transmission model of Lassa fever in Nigeria

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this