Back

Optimal Control For A Crossover Cholera Mathematical Model Using Fractal (Variable-Fractional) {Psi}-Caputo Derivative With Nonstandard Kernel

AL-Mekhlafi, S. M.; Bonyah, E.

2025-04-01 health informatics
10.1101/2025.03.31.25324982 medRxiv
Show abstract

This paper introduces an optimal control strategy for choleras crossover mathematical model. The proposed model integrates {Psi}-Caputo fractal variable-order derivatives, fractal fractional-order derivatives, and integer-order derivatives across three distinct time intervals, utilizing a simple non-standard kernel function {Psi}(t). A comprehensive stability analysis of the models steady states is conducted. The models results are compared with real-world data from the cholera outbreak in Yemen. Following this, an optimal control problem is formulated within the crossover framework. To numerically solve the resulting optimality system, a discretized non-standard -finite difference method is developed. Numerical simulations and comparative studies are presented to demonstrate the methods applicability and the efficiency of the approximation approach. The key finding of this study highlights that the crossover-controlled system proves to be the most effective approach for mitigating and controlling the spread of cholera.

Published in Scientific African · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Mathematical Biosciences
○ 49 papers in training set
Top 0.1%
11.8%
Similar papers in this journal
2
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.1%
10.9%
Similar papers in this journal
3
PLOS ONE
● 5266 papers in training set
Top 19%
8.8%
Similar papers in this journal
4
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.1%
7.8%
Similar papers in this journal
5
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.1%
6.7%
Similar papers in this journal
6
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.1%
6.7%
50% of probability mass above
7
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
● 15 papers in training set
Top 0.1%
5.4%
8
Infectious Disease Modelling
○ 54 papers in training set
Top 0.3%
4.8%
9
Scientific Reports
○ 3612 papers in training set
Top 20%
4.8%
10
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 0.3%
4.3%
11
Mathematics
○ 11 papers in training set
Top 0.1%
4.0%
12
Royal Society Open Science
● 214 papers in training set
Top 3%
2.1%
13
Journal of Theoretical Biology
○ 162 papers in training set
Top 1%
2.1%
14
Applied Sciences
○ 25 papers in training set
Top 0.3%
1.7%
15
Journal of Mathematical Biology
○ 40 papers in training set
Top 0.3%
1.4%
16
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.5%
1.1%
17
Bioinformatics
◐ 1204 papers in training set
Top 9%
0.8%
18
Computers in Biology and Medicine
○ 128 papers in training set
Top 5%
0.8%
19
PLOS Computational Biology
● 1863 papers in training set
Top 20%
0.8%
20
Frontiers in Ecology and Evolution
○ 69 papers in training set
Top 3%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.