
Globally, the COVID-19 pandemic has claimed millions of lives. In this study, we develop a mathematical model to investigate the impact of human behavior on the dynamics of COVID-19 infection in South Africa. Specifically, our model examined the effects of positive versus negative human behavior. We parameterize the model using data from the COVID-19 fifth wave of Gauteng province, South Africa, from May 01, 2022, to July 23, 2022. To forecast new cases of COVID-19 infections, we compared three forecasting methods: exponential smoothing (ETS), long short-term memory (LSTM), and gated recurrent units (GRUs), using the dataset. Results from the time series analysis showed that the LSTM model has better performance and is well-suited for predicting the dynamics of COVID-19 compared to the other models. Sensitivity analysis and numerical simulations were also performed, revealing that noncompliant infected individuals contribute more to new infections than those who comply. It is envisaged that the insights from this work can better inform public health policy and enable better projections of disease spread.
Citation: CW Chukwu, S. Y. Tchoumi, Z. Chazuka, M. L. Juga, G. Obaido. Assessing the impact of human behavior towards preventative measures on COVID-19 dynamics for Gauteng, South Africa: a simulation and forecasting approach[J]. AIMS Mathematics, 2024, 9(5): 10511-10535. doi: 10.3934/math.2024514
[1] | Sabila Ali, Shahid Mubeen, Rana Safdar Ali, Gauhar Rahman, Ahmed Morsy, Kottakkaran Sooppy Nisar, Sunil Dutt Purohit, M. Zakarya . Dynamical significance of generalized fractional integral inequalities via convexity. AIMS Mathematics, 2021, 6(9): 9705-9730. doi: 10.3934/math.2021565 |
[2] | Yousaf Khurshid, Muhammad Adil Khan, Yu-Ming Chu . Conformable integral version of Hermite-Hadamard-Fejér inequalities via η-convex functions. AIMS Mathematics, 2020, 5(5): 5106-5120. doi: 10.3934/math.2020328 |
[3] | Shuang-Shuang Zhou, Saima Rashid, Muhammad Aslam Noor, Khalida Inayat Noor, Farhat Safdar, Yu-Ming Chu . New Hermite-Hadamard type inequalities for exponentially convex functions and applications. AIMS Mathematics, 2020, 5(6): 6874-6901. doi: 10.3934/math.2020441 |
[4] | Maimoona Karim, Aliya Fahmi, Shahid Qaisar, Zafar Ullah, Ather Qayyum . New developments in fractional integral inequalities via convexity with applications. AIMS Mathematics, 2023, 8(7): 15950-15968. doi: 10.3934/math.2023814 |
[5] | Hari Mohan Srivastava, Soubhagya Kumar Sahoo, Pshtiwan Othman Mohammed, Bibhakar Kodamasingh, Kamsing Nonlaopon, Khadijah M. Abualnaja . Interval valued Hadamard-Fejér and Pachpatte Type inequalities pertaining to a new fractional integral operator with exponential kernel. AIMS Mathematics, 2022, 7(8): 15041-15063. doi: 10.3934/math.2022824 |
[6] | Muhammad Bilal Khan, Muhammad Aslam Noor, Thabet Abdeljawad, Bahaaeldin Abdalla, Ali Althobaiti . Some fuzzy-interval integral inequalities for harmonically convex fuzzy-interval-valued functions. AIMS Mathematics, 2022, 7(1): 349-370. doi: 10.3934/math.2022024 |
[7] | Yanping Yang, Muhammad Shoaib Saleem, Waqas Nazeer, Ahsan Fareed Shah . New Hermite-Hadamard inequalities in fuzzy-interval fractional calculus via exponentially convex fuzzy interval-valued function. AIMS Mathematics, 2021, 6(11): 12260-12278. doi: 10.3934/math.2021710 |
[8] | Sabir Hussain, Rida Khaliq, Sobia Rafeeq, Azhar Ali, Jongsuk Ro . Some fractional integral inequalities involving extended Mittag-Leffler function with applications. AIMS Mathematics, 2024, 9(12): 35599-35625. doi: 10.3934/math.20241689 |
[9] | Xiuzhi Yang, G. Farid, Waqas Nazeer, Muhammad Yussouf, Yu-Ming Chu, Chunfa Dong . Fractional generalized Hadamard and Fejér-Hadamard inequalities for m-convex functions. AIMS Mathematics, 2020, 5(6): 6325-6340. doi: 10.3934/math.2020407 |
[10] | Muhammad Bilal Khan, Pshtiwan Othman Mohammed, Muhammad Aslam Noor, Abdullah M. Alsharif, Khalida Inayat Noor . New fuzzy-interval inequalities in fuzzy-interval fractional calculus by means of fuzzy order relation. AIMS Mathematics, 2021, 6(10): 10964-10988. doi: 10.3934/math.2021637 |
Globally, the COVID-19 pandemic has claimed millions of lives. In this study, we develop a mathematical model to investigate the impact of human behavior on the dynamics of COVID-19 infection in South Africa. Specifically, our model examined the effects of positive versus negative human behavior. We parameterize the model using data from the COVID-19 fifth wave of Gauteng province, South Africa, from May 01, 2022, to July 23, 2022. To forecast new cases of COVID-19 infections, we compared three forecasting methods: exponential smoothing (ETS), long short-term memory (LSTM), and gated recurrent units (GRUs), using the dataset. Results from the time series analysis showed that the LSTM model has better performance and is well-suited for predicting the dynamics of COVID-19 compared to the other models. Sensitivity analysis and numerical simulations were also performed, revealing that noncompliant infected individuals contribute more to new infections than those who comply. It is envisaged that the insights from this work can better inform public health policy and enable better projections of disease spread.
The following abbreviations are used in this manuscript:
H-HHermite-HadamardH-H-MHermite-Hadamard-MercerH-H-FHermite-Hadamard-Fejér |
In science, convex functions have a long and distinguished history, and they have been the focus of study for almost a century. The rapid growth of convexity theory and applications of fractional calculus has kept the interest of a number of researchers on integral inequalities. Inequalities such as the H-H type, the Ostrowski-type, the H-H-M type, the Opial type, and other types, by using convex functions have been the focus of research for many years. The H-H inequality given in [1] has piqued the curiosity of most academics among all of these integral inequalities. Dragomir et al. [2] and Kirmaci et al. [3] presented some trapezoidal type inequalities and also some applications to special means. Following these articles, several mathematicians proposed new refinements of the Hermite-Hadamard inequality for various classes of convex functions and mappings such as quasi convex function [4], convex functions [5], m-convex functions [6], s-type convex functions of Raina type [7], σ-s-convex function [8] and harmonically convex functions [9]. Recently, this inequality was also investigated via different fractional integral operators, like Riemann-Liouville [10], ψ-Riemann-Liouville [11], Proportional fractional [12,13], k-Riemann-Liouville [14], Caputo-Fabrizio [15,16], generalized Atangana-Baleanu operator [17] to name a few.
It is important to emphasise that Leibniz and L'Hospital are credited with developing the idea of fractional calculus (1695). Other mathematicians, such as Riemann, Liouville, Letnikov, Erdéli, Grünwald, and Kober, have made significant inputs to the field of fractional calculus and its numerous applications. Many physical and engineering experts are interested in fractional calculus because of its behaviour and capacity to address a wide range of practical issues. Fractional calculus is currently concerned with the study of so-called fractional order integral and derivative functions over real and complex domains, as well as its applications. In many cases, fractional analysis requires the use of arithmetic from classical analysis to produce more accurate conclusions. Numerous mathematical models can be handled by differential equations of fractional order. Fractional mathematical models have more conclusive and precise results than classical mathematical models because they are particular examples of fractional order mathematical models. In classical analysis, integer orders do not serve as an adequate representation of nature. By using mathematical modelling, it is possible to identify the endemics' unique transmission dynamics and get insight into how infection impacts a new population. To enhance the actual phenomena to a higher degree of precision and accuracy, non-integer order fractional differential equations (FDEs) are applied. Additionally, [18,19,20,21,22] use and reference their utilization of fractional calculus. Other interesting results for fractional calclus can be found in [23,24,25]. However, fractional computation enables us to consider any number of orders and formulate far more measurable objectives. In recent years, mathematicians have become more and more interested in presenting well-known inequalities using a variety of novel theories of fractional integral operators. There are several different integral inequality results for fractional integrals. For generalizing significant and well-known integral inequalities, these operators are helpful. The Hermite-Hadamard integral inequality is a particular type of integral inequality. It is frequently used in the literature and outlines the necessary and sufficient conditions for a function to be convex. The Hermite-Hadamard inequalities were generalized by Sarikaya et al. [10] using Riemann-Liouville fractional integrals. Işcan [26] expanded Sarikaya et al. [10] 's findings to include Hermite-Hadamard-Fejer-type inequalities. By utilizing the product of two convex functions, Chen [27] produced fractional Hermite-Hadamard-type integral inequalities. Ögülmüs et al. [28] incorporated the Hermite-Hadamard and Jensen-Mercer inequalities to present Hermite-Hadamard-Mercer type inequalities for Riemann-Liouville fractional integrals. Motivated by the above articles, Butt et al. (see [29]), presented new versions of Jensen and Jensen-Mercer type inequalities in the fractal sense. New fractional versions of Hermite-Hadamard-Mercer and Pachpatte-Mercer type inclusions are established for convex [30] and harmonically convex functions [31] respectively. Latif et al. [32] established Hermite-Hadamard-Fejér type inequalities for convex harmonic and a positive symmetric increasing function. New refinements of Hermite-Mercer type inequalities are presented in [33], Mercer-Ostrowski type inequalities are presented in [34]. Further, the Hermite-Hadamard inequality is also generalized for convexity and quasi convexity [35] and differentiable convex functions [36]. For further information on other fractional-order integral inequalities, see the papers [37,38,39,40,41].
Definition 1.1. (see [42]) Let G:X→R be a function and X be a convex subset of a real vector space R. Then we say that the function G is convex if and only if the following condition:
G(Φr+(1−Φ)s)≤ΦG(r)+(1−Φ)G(s), |
holds true for all r,s∈X and Φ∈[0,1].
For further discussion, we first present the classical Hermite-Hadmard (H-H) inequality, which states that (see [1]):
If the function G:X⊆R→R is convex in X for r,s∈X and r<s, then
G(r+s2)≤1s−r∫srG(x)dx≤G(r)+G(s)2. | (1.1) |
Definition 1.2. (see [43]) Let there be a function G:[r,s]→[0,∞) and it is symmetric with respect to r+s2, if
G(r+s−x)=G(x). |
In 1906, Fejér [44] preposed the following weighted variant of Hermite-Hadamard inequality famously known as Hermite-Hadamard-Fejér inequality, given as
Theorem 1.1. Let there be a convex function G:[r,s]⊆R∖{0}→R with r<s. If D:[r,s]⊆R∖{0}→R be a convex symmetric and integrable function with respect to r+s2. Then
G(r+s2)∫srD(x)dx≤1s−r∫srG(x)D(x)dx≤G(r)+G(s)2∫srD(x)dx, | (1.2) |
holds true.
Definition 1.3. (see [9]) Let G:X→R be a function and X be a subset of a real vector space R. Then we say that the function G is harmonically convex if and only if the following condition
G(rsΦr+(1−Φ)s)≤ΦG(s)+(1−Φ)G(r), |
holds true for all r,s∈X and Φ∈[0,1].
For further discussion, we first present the classical Hermite-Hadmard (H-H) inequality, which states that (see [9]):
If the function G:X⊆R→R is harmonically convex in X for r,s∈X and r<s, then
G(2rsr+s)≤rss−r∫srG(x)x2dx≤G(r)+G(s)2. | (1.3) |
Definition 1.4. (see [45]) Let there be a function G:[r,s]→[0,∞) and it is harmonically symmetric with respect to 2rsr+s, if
G(Φ)=G(11r+1s−1Φ). |
In the year 2014, Chen and Wu [46] proposed the following weighted variant of Hermite-Hadamard inequality for harmonically convex function, given as
Theorem 1.2. Let there be a convex function G:[r,s]⊆R∖{0}→R with r<s. If D:[r,s]⊆R∖{0}→R be a convex symmetric and integrable function with respect to r+s2. Then,
G(2rsr+s)∫srD(x)x2dx≤rss−r∫srG(x)D(x)x2dx≤G(r)+G(s)2∫srD(x)x2dx, | (1.4) |
holds true.
Definition 1.5. (see, for details, [10,47]; see also [48]) Let \(\mathscr{G} \in \mathcal{L} \left\lbrack \mathfrak{r}, \ \mathfrak{s} \right\rbrack\). Then, the Riemann-Liouville fractional integrals of the order α>0, are defined as follows:
Iαr+G(x)=1Γ(α)∫xr(x−m)α−1G(m)dm(x>r), |
and
Iαs−G(x)=1Γ(α)∫sx(m−x)α−1G(m)dm(x<s), |
respectively, where \(\Gamma\left(\alpha \right) = \int_{0}^{\infty}{\Phi^{\alpha - 1}e^{- \Phi}}{d\Phi}\) is the Euler gamma function.
Definition 1.6. (see, [49] for details) Let G∈L[r,s]. Then, the new left and right fractional integrals Iαr+ and Iαs− of order α>0 are defined as
Iαr+G(x):=1α∫xre−1−αα(x−m)G(m)dm(0≤r<x<s), |
and
Iαs−G(x):=1α∫sxe−1−αα(m−x)G(m)dm(0≤r<x<s), |
respectively.
It should be noted that
limα→1Iαr+G(x)=∫xrG(m)dm and limα→1Iαs−G(x)=∫sxG(m)dm. |
Sarikaya et al. [37], in their article proved some interesting mid-point type Hermite-Hadamard inequalities. Here, we present one of his main results as follows:
Theorem 1.3. (see [37]) Let G:[r,s]⟶R be a convex function with 0≤r≤s. If G∈L[r,s], then the following inequality for Riemann-Liouville fractional integral operator holds true:
G(r+s2)≤2α−1Γ(α+1)(s−r)α[Iα(r+s2)+G(s)+Iα(r+s2)−G(r)]≤G(r)+G(s)2. |
The major goal of this paper is to establish Fejér type fractional inequalities using differintegrals of the (r+s2) type for both convex and harmonically convex functions via a novel fractional integral operator. In order to derive those inequalities, first we prove two new lemmas i.e., Lemmas 2.1 and 3.1 for convex and harmonic convex functions respectively.
In this study, we used a new fractional integral operator to achieve more generalized results. This is caused by the exponential function that makes up the kernel of this fractional operator. Our results differ from prior generalizations in that they do not lead to the aforementioned fractional integral inequalities. Numerous experts have suggested utilizing different fractional integral operators to extend the Hermite-Hadamard and Fejér type inequalities, however, none of their findings exhibit an exponential property. This study generated interest in using an exponential function as the kernel to create more generalized fractional inequalities. Furthermore, the application of symmetric and harmonically symmetric functions to the main results gives the study of inequalities a new path. For other generalization regarding exponential kernel interested reader can see e.g., on distributed-order fractional derivative in [50]. There are many research gaps to be filled for integral inequalities involving fractional calculus for different types of convex functions, despite the fact that there exist many different forms of research on the growth of fractional integral inequalities. As a result, the main purpose of this research is to find new Hermite-Hadamard and Fejér type inequalities for positive symmetric functions using fractional integral operators.
Our present investigation is structured as follows. In Sections 2 and 3, we discuss two additional characteristics of the relevant fractional operator before proving some enhanced versions (mid-point types) of the Fejér and Hermite-Hadamard type inequalities for convex and harmonically convex functions respectively. Some applications are also taken into consideration in Section 4 to determine whether the predetermined results are appropriate. Section 5 explores a brief conclusion and possible areas for additional research that is related to the findings in this paper are discussed in Section 6.
In this section for simplicity, we denote ρc=1−αα(s−r). If α→1, then ρc=1−αα(s−r)→0.
Lemma 2.1. Let D:[r,s]⊆R∖{0}→R be a symmetric convex function with respect to r+s2. Then for α>0, the following equality holds true:
Ir+s2+D(s)=Ir+s2−D(r)=12[Ir+s2−D(r)+Ir+s2+D(s)]. |
Proof. Since D:[r,s]⊆R∖{0}→R is integrable and symmetric to r+s2 we have D(r+s−x)=D(x). Also, Setting Φ=r+s−x and dΦ=−dx, we have
Ir+s2+D(s)=1α∫sr+s2e−1−αα(s−Φ)D(Φ)dΦ=−1α∫rr+s2e−1−αα(s−(r+s−x))D(r+s−x)dx=1α∫r+s2re−1−αα(x−r)D(r+s−x)dx=1α∫r+s2re−1−αα(x−r)D(x)dx=Iαr+s2−D(r). |
⟹Ir+s2+D(s)=Ir+s2−D(r)=12[Ir+s2−D(r)+Ir+s2+D(s)]. |
This led us to the desired equality.
First, we prove both the first and second kind Fejér type inequalities in a different approach. Then, we also prove the Hermite-Hadamard inequality using symmetric convex functions.
Theorem 2.1. Let there be a convex function G:[r,s]⊆R∖{0}→R with r<s. If D:[r,s]⊆R∖{0}→R be a convex symmetric and integrable function with respect to r+s2. Then for α>0, the following inequality holds true:
G(r+s2)[Iαr+s2−D(r)+Iαr+s2+D(s)]≤[Iαr+s2−(GD)(r)+Iαr+s2+(GD)(s)]. |
Proof. Using the convexity of G on [r,s], we have
2G(r+s2)≤G(Φr+(1−Φ)s)+G(Φs+(1−Φ)r). | (2.1) |
Upon multiplication of both sides of the inequality (2.1) by e−1−αα(s−r)ΦD(Φs+(1−Φ)r) and then integrating the resultant over [0,12], we obtain
2G(r+s2)∫120e−1−αα(s−r)ΦD(Φs+(1−Φ)r)dΦ≤∫120e−1−αα(s−r)ΦG(Φr+(1−Φ)s)D(Φs+(1−Φ)r)dΦ+∫120e−1−αα(s−r)ΦG(Φs+(1−Φ)r)D(Φs+(1−Φ)r)dΦ. | (2.2) |
Since D is symmetric with respect to r+s2, we have D(x)=D(r+s−x).
Moreover, setting x=Φs+(1−Φ)r and dx=(s−r)dΦ in (2.2), we have
2G(r+s2)1s−r∫r+s2re−1−αα(x−r)D(x)dx=2G(r+s2)αs−r[Iαr+s2−D(r)]≤1s−r∫r+s2re−1−αα(x−r)G(r+s−x)D(x)dx+1s−r∫r+s2re−1−αα(x−r)G(x)D(x)dx=1s−r∫sr+s2e−1−αα(s−x)G(x)D(r+s−x)dx+1s−r∫r+s2re−1−αα(x−r)G(x)D(x)dx=αs−r[1α∫sr+s2e−1−αα(s−x)G(x)D(x)dx+1α∫r+s2re−1−αα(x−r)G(x)D(x)dx]. |
It follows from the above developments and Lemma 2.1 that,
αs−rG(r+s2)[Iαr+s2−D(r)+Iαr+s2+D(s)]≤αs−r[Iαr+s2−(GD)(r)+Iαr+s2+(GD)(s)]. |
This concludes the proof of the required result.
Example 2.1. Let G(m)=em, m∈[1,9] and D(m)=(5−m)2, is non-negative symmetric about m=5. Let 0<α<1, then
G(r+s2)[Iαr+s2−D(r)+Iαr+s2+D(s)]=e5[Iα5−D(1)+Iα5+D(9)]=e5[1α∫51e−1−αα(m−1)(5−m)2dm+1α∫95e−1−αα(9−m)(5−m)2dm]. |
and
Iαr+s2−(GD)(r)+Iαr+s2+(GD)(s)=Iα5−(GD)(1)+Iα5+(GD)(9)=1α∫51e−1−αα(m−1)em(5−m)2dm+1α∫95e−1−αα(9−m)em(5−m)2dm. |
The graphical representation of Theorem 2.1 is shown in the graph below (see Figure 1) for 0<α<1:
Theorem 2.2. Let there be a convex function G:[r,s]⊆R∖{0}→R with r<s. If D:[r,s]⊆R∖{0}→R be a convex symmetric and integrable function with respect to r+s2. Then for α>0, the following inequality holds true:
[Iαr+s2−(GD)(r)+Iαr+s2+(GD)(s)]≤G(r)+G(s)2[Iαr+s2−(D)(r)+Iαr+s2+(D)(s)]. |
Proof. Since G is convex function, we have
G(Φr+(1−Φ)s)+G(Φs+(1−Φ)r)≤G(r)+G(s). | (2.3) |
Multiplying both side of the above inequality (2.3) by e−1−αα(s−r)ΦD(Φs+(1−Φ)r) and upon integration of the obtained result over [0,12], one has
∫120e−1−αα(s−r)ΦG(Φr+(1−Φ)s)D(Φs+(1−Φ)r)dΦ+∫120e−1−αα(s−r)ΦG(Φs+(1−Φ)r)D(Φs+(1−Φ)r)dΦ≤[G(r)+G(s)]∫120e−1−αα(s−r)ΦD(Φs+(1−Φ)r)dΦ. |
It follows
αs−r[Iαr+s2−(GD)(r)+Iαr+s2+(GD)(s)]≤α[G(r)+G(s)]s−r[Iαr+s2−(D)(r)]. |
Furthermore, using the Lemma 2.1, we obtain
αs−r[Iαr+s2−(GD)(r)+Iαr+s2+(GD)(s)]≤G(r)+G(s)2αs−r[Iαr+s2−(D)(r)+Iαr+s2+(D)(s)]. |
This concludes the proof of the desired result.
Example 2.2. Let G(m)=em, m∈[1,9] and D(m)=(5−m)2, is non-negative symmetric about m=5. Let 0<α<1, then
e+e92[Iαr+s2−(D)(r)+Iαr+s2+(D)(s)]=e+e92[Iα5−D(1)+Iα5+D(9)]=e+e92[1α∫51e−1−αα(m−1)(5−m)2dm+1α∫95e−1−αα(9−m)(5−m)2dm]. |
And
Iαr+s2−(GD)(r)+Iαr+s2+(GD)(s)=Iα5−(GD)(1)+Iα5+(GD)(9)=1α∫51e−1−αα(m−1)em(5−m)2dm+1α∫95e−1−αα(9−m)em(5−m)2dm. |
The graphical representation of Theorem 2.2 is shown in the graph below (see Figure 2) for 0<α<1:
Theorem 2.3. Let there be a convex function G:[r,s]⊆R∖{0}→R with r<s. Then for α>0, the following fractional integral inequality holds true:
G(r+s2)≤1−α2(1−e−ρc2)[Iαr+s2−G(r)+Iαr+s2+G(s)]≤G(r)+G(s)2. | (2.4) |
Proof. By the hypothesis of convexity, we have
G(r+s2)=G(Φr+(1−Φ)s+Φs+(1−Φ)r2)≤G(Φr+(1−Φ)s)+G(Φs+(1−Φ)r)2. | (2.5) |
Upon multiplication of both sides of the inequality (2.5) by 2e−1−αα(s−r)Φ and then integrating the obtained result over [0,12], we have
2G(r+s2)∫120e−1−αα(s−r)ΦdΦ≤∫120e−1−αα(s−r)ΦG(Φr+(1−Φ)s)dΦ+∫120e−1−αα(s−r)ΦG(Φs+(1−Φ)r)dΦ. | (2.6) |
Furthermore, let m=Φs+(1−Φ)r⟹dΦ=dms−r. Then inequality (2.6) gives
2(1−e−ρc2)ρG(r+s2)≤[1s−r∫r+s2re−1−αα(s−r)m−rs−rG(r+s−m)dm+1s−r∫r+s2re−1−αα(s−r)m−rs−rG(m)dm]=αs−r[1α∫sr+s2e−1−αα(s−m)G(m)dm+1α∫r+s2re−1−αα(m−r)G(m)dm]=αs−r[Iαr+s2−G(r)+Iαr+s2+G(s)]. | (2.7) |
This concludes the proof of the first part of the inequality (2.4). To prve the next part of inequality, under the given hypothesis, we have
G(Φr+(1−Φ)s)+G(Φs+(1−Φ)r)≤G(r)+G(s). | (2.8) |
Upon multiplication of both sides of the inequality (2.8) by e−1−αα(s−r)Φ and integrating over [0,12], we have
∫120e−1−αα(s−r)ΦG(Φr+(1−Φ)s)dΦ+∫120e−1−αα(s−r)ΦG(Φs+(1−Φ)r)dΦ≤[G(r)+G(s)]∫120e−1−αα(s−r)ΦdΦ. |
Consequently, we obtain
αs−r[Iαr+s2−G(r)+Iαr+s2+G(s)]≤G(r)+G(s)22(1−e−ρc2)ρc. | (2.9) |
Consequently, it follows from the above developments (2.7) and (2.9) that
G(r+s2)≤1−α2(1−e−ρc2)[Iαr+s2−G(r)+Iαr+s2+G(s)]≤G(r)+G(s)2. |
This concludes the proof of the required result.
Remark 2.1. If one chooses α→1 i.e., ρc2→0, then
limα→11−α2(1−e−ρc2)=1s−r |
and hence Theorem 2.3 retrieves the classical Hermite-Hadamard inequality (1.1).
Example 2.3. Let G(m)=em, m∈[1,9] and 0<α<1, then
G(r)+G(s)2=e+e92, |
G(r+s2)=e5 |
and
1−α2(1−e−ρc2)[Iαr+s2−G(r)+Iαr+s2+G(s)]=1−α2(1−e−4(1−α)α)[Iα5−(G)(1)+Iα5+(G)(9)]=1−α2(1−e−4(1−α)α)[1α∫51e−1−αα(m−1)emdm+1α∫95e−1−αα(9−m)emdm]. |
The graphical representation of Theorem 2.3 is shown in the graph below (see Figure 3) for 0<α<1:
The family of Lebesgue measurable functions is represented here by \(\mathcal{L} \left\lbrack \mathfrak{r}, \mathfrak{s} \right\rbrack \). In this section, for brevity we use, ρh=1−ααs−rrs wherever needed. If α→1, then ρh=1−ααs−rrs→0.
Lemma 3.1. Let D:[r,s]⊆R∖{0}→R be a harmonically symmetric and integrable function with respect to 2rsr+s. Then for α>0, the following equality holds true:
Iαr+s2rs+D∘K(1r)=Iαr+s2rs−D∘K(1s)=12[Iαr+s2rs+D∘K(1r)+Iαr+s2rs−D∘K(1s)], |
where K(x)=1x,x∈[1s,1r].
Proof. Let D be a harmonically symmetric function with respect to 2rsr+s. Then using the harmonically symmetric property of D, given as D(1Φ)=D(11r+1s−Φ) for α>0.
Iαr+s2rs+D∘K(1r)=1α∫1rr+s2rse−1−αα(1r−Φ)D(1Φ)dΦ=1α∫1rr+s2rse−1−αα(1r−Φ)D(11r+1s−Φ)dΦ=−1α∫1sr+s2rse−1−αα(x−1s)D(1x)dx=1α∫r+s2rs1se−1−αα(x−1s)D(1x)dx=Iαr+s2rs−D∘K(1s). |
Consequently, it follows from the above developments that
Iαr+s2rs+D∘K(1r)=Iαr+s2rs−D∘K(1s)=12[Iαr+s2rs+D∘K(1r)+Iαr+s2rs−D∘K(1s)], |
where K(x)=1x,x∈[1s,1r].
Now, we use the above result to produce new Hadamard-Fejér type inequalities of both first and second kind for harmonically convex functions.
Let us begin with the Hadamard-Fejér type inequality of the first kind.
Theorem 3.1. Let there be a harmonically convex function G:[r,s]⊆R∖{0}→R with r<s. If D:[r,s]⊆R∖{0}→R be a harmonically symmetric and integrable function with respect to 2rsr+s. Then for α>0, the following inequality holds true:
G(2rsr+s)[Iαr+s2rs−D∘K(1s)+Iαr+s2rs+D∘K(1r)]≤[Iαr+s2rs−GD∘K(1s)+Iαr+s2rs+GD∘K(1r)]. |
Proof. Since G is harmonically convex function on [r,s], we have
G(2rsr+s)≤G(rsΦr+(1−Φ)s)+G(rsΦs+(1−Φ)r)2. |
Multiplying both side by 2e−1−ααs−rrsΦD(rsΦs+(1−Φ)r) and then integrating over [0,12], we find
2G(2rsr+s)∫120e−1−ααs−rrsΦD(rsΦs+(1−Φ)r)dΦ≤∫120e−1−ααs−rrsΦD(rsΦs+(1−Φ)r)G(rsΦr+(1−Φ)s)dΦ+∫120e−1−ααs−rrsΦD(rsΦs+(1−Φ)r)G(rsΦs+(1−Φ)r)dΦ. |
Since, D is harmonically symmetric with respect to 2rsr+s i.e
D(1x)=D(11r+1s−x). |
Also, setting x=Φs+(1−Φ)rrs ⟹dΦ=rss−rdx the above developments proceed as follows:
α2rss−rG(2rsr+s)1α∫r+s2rs1se−1−αα(x−1s)D(1x)dx≤αrss−r[1α∫r+s2rs1se−1−αα(x−1s)G(11r+1s−x)D(1x)dx+1α∫r+s2rs1se−1−αα(x−1s)G(1x)D(1x)dx]=αrss−r[1α∫1rr+s2rse−1−αα(1r−x)G(1x)D(11r+1s−x)dx+1α∫r+s2rs1se−1−αα(x−1s)G(1x)D(1x)dx]=αrss−r[1α∫1rr+s2rse−1−αα(1r−x)G(1x)D(1x)dx+1α∫r+s2rs1se−1−αα(x−1s)G(1x)D(1x)dx]=αrss−r[Iαr+s2rs−GD∘K(1s)+Iαr+s2rs+GD∘K(1r)]. |
From the above developments and Lemma 3.1, we have
αrss−rG(2rsr+s)[Iαr+s2rs−D∘K(1s)+Iαr+s2rs+D∘K(1r)]≤αrss−r[Iαr+s2rs−GD∘K(1s)+Iαr+s2rs+GD∘K(1r)]. |
This concludes the proof of the required result.
Example 3.1. Let G(m)=m2, m∈[1,4], D(m)=(5m−88m)2 and 0<α<1, then
G(2rsr+s)[Iαr+s2rs−D∘K(1s)+Iαr+s2rs+D∘K(1r)]=6425[1α∫5814e−1−αα(m−14)(5−8m8)2dm+1α∫158e−1−αα(1−m)(5−8m8)2dm] |
and
Iαr+s2rs−GD∘K(1s)+Iαr+s2rs+GD∘K(1r)=Iα58−GD∘K(14)+Iα58+GD∘K(1)=1α∫5814e−1−αα(m−14)(1m)2(5−8m8)2dm+1α∫158e−1−αα(1−m)(1m)2(5−8m8)2dm. |
The graphical representation of Theorem 3.1 is shown in the graph below (see Figure 4) for 0<α<1:
Now, we will establish the Fejér type inequality of the second kind.
Theorem 3.2. Let there be a harmonically convex function G:[r,s]⊆R∖{0}→R with r<s. If D:[r,s]⊆R∖{0}→R be a harmonically symmetric and integrable function with respect to 2rsr+s. Then for α>0, the following inequality holds true:
[Iαr+s2rs−GD∘K(1s)+Iαr+s2rs+GD∘K(1r)]≤G(r)+G(s)2[Iαr+s2rs−D∘K(1s)+Iαr+s2rs+D∘K(1r)]. |
Proof. Since G is harmonically convex function
G(rsΦr+(1−Φ)s)+G(rsΦs+(1−Φ)r)≤G(r)+G(s). |
Multiplying both side by e−1−ααs−rrsΦD(rsΦs+(1−Φ)r) and then integrating the resultant over [0,12], we find
∫120e−1−ααs−rrsΦG(rsΦr+(1−Φ)s)D(rsΦs+(1−Φ)r)dΦ+∫120e−1−ααs−rrsΦG(rsΦs+(1−Φ)r)D(rsΦs+(1−Φ)r)dΦ≤[G(r)+G(s)]∫120e−1−ααs−rrsΦD(rsΦs+(1−Φ)r)dΦ. | (3.1) |
Setting x=Φs+(1−Φ)rrs and D(1x)=D(11r+1s−x) in (3.1), we have
rss−r[∫r+s2rs1se−1−αα(x−1s)G(11r+1s−x)D(1x)dx∫r+s2rs1se−1−αα(x−1s)G(1x)D(1x)dx]=αrss−r[1α∫1rr+s2rse−1−αα(1r−x)G(1x)D(1x)dx+1α∫r+s2rs1se−1−αα(x−1s)G(1x)D(1x)dx]=αrss−r[Iαr+s2rs−GD∘K(1s)+Iαr+s2rs+GD∘K(1r)]. | (3.2) |
Also,
[G(r)+G(s)]∫120e−1−ααs−rrsΦD(rsΦs+(1−Φ)r)dΦ=αrss−r[G(r)+G(s)]1α∫r+s2rs1se−1−αα(x−1s)D(1x)dx=αrss−r[G(r)+G(s)][Iαr+s2rs−D∘K(1s)]. | (3.3) |
From the above developments (3.2), (3.3) and Lemma 3.1, we have
αrss−r[Iαr+s2rs−GD∘K(1s)+Iαr+s2rs+GD∘K(1r)]≤αrss−rG(r)+G(s)2[Iαr+s2rs−D∘K(1s)+Iαr+s2rs+D∘K(1r)]. |
This concludes the proof of the required result.
Example 3.2. Let G(m)=m2, m∈[1,4], D(m)=(5m−88m)2 and 0<α<1, then
172[Iαr+s2rs−D∘K(1s)+Iαr+s2rs+D∘K(1r)]=172[1α∫5814e−1−αα(m−14)(5−8m8)2dm+1α∫158e−1−αα(1−m)(5−8m8)2dm] |
and
Iαr+s2rs−GD∘K(1s)+Iαr+s2rs+GD∘K(1r)=Iα58−GD∘K(14)+Iα58+GD∘K(1)=1α∫5814e−1−αα(m−14)(5−8m8)4dm+1α∫158e−1−αα(1−m)(5−8m8)4dm. |
The graphical representation of Theorem 3.2 is shown in the graph below (see Figure 5) for 0<α<1:
Theorem 3.3. Let there be a harmonically convex function G:[r,s]⊆R∖{0}→R with r<s. Then for α>0,
G(2rsr+s)≤(1−α)2(1−eρh2)[Iαr+s2rs+G∘K(1r)+Iαr+s2rs−G∘K(1s)]≤G(r)+G(s)2, | (3.4) |
holds true.
Proof. Since G is harmonically convex function on [r,s], we have
G(2rsr+s)=G(2(rsΦr+(1−Φ)s)(rsΦs+(1−Φ)r)(rsΦr+(1−Φ)s)+(rsΦs+(1−Φ)r))≤G(rsΦr+(1−Φ)s)+G(rsΦs+(1−Φ)r)2. | (3.5) |
Multiplying both side of the inequality (3.5) by 2e−1−ααs−rrsΦ and integrating over [0,12], we obtain
2G(2rsr+s)∫1/20e−1−ααs−rrsΦdΦ≤∫1/20e−1−ααs−rrsΦG(rsΦr+(1−Φ)s)dΦ+∫1/20e−1−ααs−rrsΦG(rsΦs+(1−Φ)r)dΦ. |
Let m=Φs+(1−Φ)rrs, then dm=s−rrsdΦ
2(1−e−ρh2)ρhG(2rsr+s)≤∫r+s2rs1se−1−ααs−rrsrss−r(m−1s)(rss−r)G(11r+1s−m)dm+∫r+s2rs1se−1−ααs−rrsrss−r(m−1s)(rss−r)G(1m)dm=rss−r[∫r+s2rs1se−1−αα(m−1s)G(11r+1s−m)dm+∫r+s2rs1se−1−αα(m−1s)G(1m)dm]=rss−r[∫1rr+s2rse−1−αα(1r−m)G(1m)dm+∫r+s2rs1se−1−αα(m−1s)G(1m)dm]=αrss−r[Iαr+s2rs+G∘K(1r)+Iαr+s2rs−G∘K(1s)]. | (3.6) |
This gives us the first part of the inequality (3.4). Now, for the next part, we use the hypotheses of harmonically convex function i.e.
G(rsΦr+(1−Φ)s)+G(rsΦs+(1−Φ)r)≤G(r)+G(s). | (3.7) |
Multiplying both side of the above inequality (3.7) by e−1−ααs−rrsΦ and then integrating the resultant over [0,1], we obtain
∫120e−1−ααs−rrsΦG(rsΦr+(1−Φ)s)dΦ+∫120e−1−ααs−rrsΦG(rsΦs+(1−Φ)r)dΦ≤[G(r)+G(s)]∫120e−1−ααs−rrsΦdΦ=2(1−e−ρh2)ρhG(r)+G(s)2. |
Consequently from the first inequality (3.6), we have
∫120e−1−ααs−rrsΦG(rsΦr+(1−Φ)s)dΦ+∫120e−1−ααs−rrsΦG(rsΦs+(1−Φ)r)dΦ=αrss−r[Iαr+s2rs+G∘K(1r)+Iαr+s2rs−G∘K(1s)]≤2(1−e−ρh2)ρhG(r)+G(s)2. | (3.8) |
From the above developments (3.6) and (3.8), it follows
G(2rsr+s)≤(1−α)2(1−eρh2)[Iαr+s2rs+G∘K(1r)+Iαr+s2rs−G∘K(1s)]≤G(r)+G(s)2. |
This concludes the proof of the required result.
Remark 3.1. If one chooses α→1 i.e., ρh2→0, then
limα→11−α2(1−e−ρh2)=rss−r |
and hence Theorem 2.3 retrieves the classical Hermite-Hadamard inequality (1.3) for harmonically convex function.
Example 3.3. Let G(m)=m2, m∈[1,4], K(m)=1m and 0<α<1, then
G(2rsr+s)=6425, |
(1−α)2(1−eρh2)[Iαr+s2rs+G∘K(1r)+Iαr+s2rs−G∘K(1s)]=(1−α)2(1−e3(1−α)8α)[Iα58+G∘K(1r)+Iα58−G∘K(1s)]=(1−α)2(1−e3(1−α)8α)[1α∫5814e−1−αα(m−14)(1m)2dm+1α∫158e−1−αα(1−m)(1m)2dm] |
and
G(r)+G(s)2=172. |
The graphical representation of Theorem 3.3 is shown in the graph below (see Figure 6) for 0<α<1:
Example 4.1. Let Cn be the set of n×n complex matrices, Mn denote the algebra of n×n complex matrices, and M+n denote the strictly positive matrices in Mn. That is, for any nonzero u∈Cn, A∈M+n if ⟨Au,u⟩>0.
Sababheh [51], proved that G(κ)=∥AκXB1−κ+A1−κXBκ∥, A,B∈M+n,X∈Mn is convex for all κ∈[0,1].
Then, by using Theorem 2.3, we have
∥Ar+s2XB1−(r+s2)+A1−(r+s2)XBr+s2∥≤1−α2(1−e−ρc2)[Iαr+s2+∥AsXB1−s+A1−sXBs∥+Iαr+s2−∥ArXB1−r+A1−rXBr∥]≤∥ArXB1−r+A1−rXBr∥+∥AsXB1−s+A1−sXBs∥2. |
Example 4.2. The q-digamma(psi) function ψΦ given as (see [52]):
ψΦ(ζ)=−ln(1−Φ)+ lnΦ∞∑k=0Φk+ζ1−Φk+ζ =−ln(1−Φ)+ lnΦ∞∑k=1Φkζ1−Φkζ. |
For Φ>1 and ζ>0, Φ-digamma function ψΦ can be given as:
ψΦ(ζ)=−ln(Φ−1)+ lnΦ[ζ−12−∞∑k=0Φ−(k+ζ)1−Φ−(k+ζ)] =−ln(Φ−1)+ lnΦ[ζ−12−∞∑k=1Φ−kζ1−Φ−kζ]. |
If we set G(ζ)=ψ′Φ(ζ) in Theorem 2.3, then we have the following inequality.
ψ′Φ(r+s2)≤1−α2α(1−e−Φc2)[∫sr+s2e−1−αα(s−m)ψ′Φ(m)dm+∫r+s2re−1−αα(m−r)ψ′Φ(m)dm]≤ψ′Φ(r)+ψ′Φ(s)2. |
Modified Bessel functions
Example 4.3. Let the function Jρ:R→[1,∞) be defined [52] as
Jρ(m)=2ρΓ(ρ+1)m−ρIρ(m), m∈R. |
Here, we consider the modified Bessel function of first kind given in
Jρ(m)=∞∑n=0(m2)ρ+2nn!Γ(ρ+n+1). |
The first and second order derivative are given as
J′ρ(m)=m2(ρ+1)Jρ+1(m). |
J′′ρ(m)=14(ρ+1)[u2(ρ+1)Jρ+2(m)+2Jρ+1(m)]. |
If we use, G(m)=J′ρ(m) and the above functions in Theorem 2.3, we have
r+s2Jρ+1(r+s2)≤1−α2α(1−e−ρc2)[∫sr+s2e−1−αα(s−m)mJρ+1(m)dm+∫r+s2re−1−αα(m−r)mJρ+1(m)dm]≤rJρ+1(r)+sJρ+1(s)2. |
The use of fractional calculus for finding various integral inequalities via convex functions has skyrocketed in recent years. This paper addresses a novel sort of Fejér type integral inequalities. In order to generalize some H-H-F (Hermite-Hadamard-Fejér) type inequalities, a new fractional integral operator with exponential kernel is employed. New midpoint type inequalities for both convex and harmonically convex functions are studied. Applications related to matrices, q-digamma and modifed Bessel functions are presented as well.
We will use our theories and methods to create new inequalities for future research by combining these new weighted generalized fractional integral operators with Chebyshev, Simpson, Jensen-Mercer Markov, Bullen, Newton, and Minkowski type inequalities. Quantum calculus, fuzzy interval-valued analysis, and interval-valued analysis can all be used to establish these kinds of inequalities. The idea of Digamma functions and other special functions will be integrated with this kind of inequality as the major focus. We also aim to find other novel inequalities using finite products of functions. In future, we will employ the concept of cr-order defined by Bhunia and Samanta [53] to present different inequalities for cr-convexity and cr-harmonically convexity [54].
This research received funding support from the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, (grant number B05F650018).
The authors declare that there are no conflicts of interest regarding the publication of this paper.
[1] | Coronavirus Disease 2019 (COVID-19) Situation Report-40, World Health Organization, 2020. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200229-sitrep-40-covid-19. |
[2] | Coronavirus Disease (COVID-19): How is it Transmitted? World Health Organization, 2021. Available from: https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-covid-19-how-is-it-transmitted. |
[3] |
S. Funk, M. Salathé, V. A. A. Jansen, Modelling the influence of human behaviour on the spread of infectious diseases: a review, J. Royal Soc. Interf., 7 (2010), 1247–1256. http://dx.doi.org/10.1098/rsif.2010.0142 doi: 10.1098/rsif.2010.0142
![]() |
[4] | COVID-19 Vaccine: What You Need to Know, Johns Hopkins Medicine, 2022. Available from: https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/covid-19-vaccines-myth-versus-fact. |
[5] | Bringing Traditional Healing Under the Microscope in South Africa, Medscape, 2020. Available from: https://www.medscape.com/viewarticle/943429. |
[6] |
S. L. Canham, P. M. Mauro, C. N. Kaufmann, A. Sixsmith, Association of alcohol use and loneliness frequency among middle-aged and older adult drinkers, J. Aging Health, 28 (2016), 267–284. https://doi.org/10.1177/0898264315589579 doi: 10.1177/0898264315589579
![]() |
[7] | Advice for the Public: Coronavirus Disease (COVID-19), World Health Organization, 2022. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public. |
[8] |
Y. Li, D. Ji, W. Cai, Y. Hu, Y. Bai, J. Wu, et al., Clinical characteristics, cause analysis and infectivity of COVID-19 nucleic acid repositive patients: a literature review, J. Med. Virol., 93 (2021), 1288–1295. http://dx.doi.org/10.1002/jmv.26491 doi: 10.1002/jmv.26491
![]() |
[9] | W. McNeill, Plagues and Peoples, New York: Anchor Press, 2010. |
[10] |
S. Funk, S. Bansal, C. T. Bauch, K. T. D. Eames, W. John Edmunds, A. P. Galvani, et al., Nine challenges in incorporating the dynamics of behaviour in infectious diseases models, Epidemics, 10 (2015), 21–25. https://doi.org/10.1016/j.epidem.2014.09.005 doi: 10.1016/j.epidem.2014.09.005
![]() |
[11] |
M. Salathé, S. Bonhoeffer, The effect of opinion clustering on disease outbreaks, J. Royal Soc. Interf., 5 (2008), 1505–1508. http://dx.doi.org/10.1098/rsif.2008.0271 doi: 10.1098/rsif.2008.0271
![]() |
[12] |
J. M. Epstein, J. Parker, D. Cummings, R. A. Hammond, Coupled contagion dynamics of fear and disease: mathematical and computational explorations, PloS One, 16 (2008), e3955. http://dx.doi.org/10.1371/journal.pone.0003955 doi: 10.1371/journal.pone.0003955
![]() |
[13] |
D. H. Zanette, S. Risau-Gusmán, Infection spreading in a population with evolving contacts, J. Biol. Phys., 34 (2008), 135–148. http://dx.doi.org/10.1007/s10867-008-9060-9 doi: 10.1007/s10867-008-9060-9
![]() |
[14] |
T. Gross, C. J. Dommar D'Lima, B. Blasius, Epidemic dynamics on an adaptive network, Phys. Rev. Lett., 96 (2006), 208701. https://doi.org/10.1103/PhysRevLett.96.208701 doi: 10.1103/PhysRevLett.96.208701
![]() |
[15] |
L. B. Shaw, I. B. Schwartz, Fluctuating epidemics on adaptive networks, Phys. Rev. E, 77 (2008), 066101. https://doi.org/10.1103/PhysRevE.77.066101 doi: 10.1103/PhysRevE.77.066101
![]() |
[16] |
C. T. Bauch, Imitation dynamics predict vaccinating behaviour, Proc. Royal Soc. B Biol. Sci., 272 (2005), 1669–1675. https://doi.org/10.1098/rspb.2005.3153 doi: 10.1098/rspb.2005.3153
![]() |
[17] |
M. Juga, F. Nyabadza, F. Chirove, An Ebola virus disease model with fear and environmental transmission dynamics, Infect. Disease Model., 6 (2021), 545–559. https://doi.org/10.1016/j.idm.2021.03.002 doi: 10.1016/j.idm.2021.03.002
![]() |
[18] |
N. Zhang, W. Jia, H. Lei, P. Wang, P. Zhao, Y. Guo, et al., Effects of human behavior changes during the coronavirus disease 2019 (COVID-19) pandemic on influenza spread in Hong Kong, Clin. Infect. Dis., 73 (2021), e1142–e1150. https://doi.org/10.1093/cid/ciaa1818 doi: 10.1093/cid/ciaa1818
![]() |
[19] |
U. Kollamparambil, A.Oyenubi, Behavioural response to the Covid-19 pandemic in South Africa, PloS One, 16 (2021), e0250269. https://doi.org/10.1371/journal.pone.0250269 doi: 10.1371/journal.pone.0250269
![]() |
[20] |
F. Nyabadza, F. Chirove, C. Chukwu, M. V. Visaya, Modelling the potential impact of social distancing on the COVID-19 epidemic in South Africa, Comput. Math. Meth. Medic., 2020 (2020), 5379278. https://doi.org/10.1155/2020/5379278 doi: 10.1155/2020/5379278
![]() |
[21] |
S. P. Gatyeni, C. W. Chukwu, F. Chirove, Fatmawati, F. Nyabadza, Application of optimal control to the dynamics of COVID-19 disease in South Africa, Sci. Afr., 16 (2022), e01268. https://doi.org/10.1016/j.sciaf.2022.e01268 doi: 10.1016/j.sciaf.2022.e01268
![]() |
[22] |
C. J. Edholm, B. Levy, L. Spence, F. B. Agusto, F. Chirove, C. W. Chukwu, et al., A vaccination model for COVID-19 in Gauteng, South Africa, Infect. Disease Model., 7 (2022), 333–345. https://doi.org/10.1016/j.idm.2022.06.002 doi: 10.1016/j.idm.2022.06.002
![]() |
[23] |
C. W. Chukwu, Fatmawati, Modelling fractional-order dynamics of COVID-19 with environmental transmission and vaccination: a case study of Indonesia, AIMS Math., 7 (2022), 4416–4438. https://doi.org/10.3934/math.2022246 doi: 10.3934/math.2022246
![]() |
[24] |
J. Mushanyu, W. Chukwu, F. Nyabadza, G. Muchatibaya, Modelling the potential role of super spreaders on COVID-19 transmission dynamics, Int. J. Math. Model. Numer. Optim., 12 (2022), 191–209. https://dx.doi.org/10.1504/IJMMNO.2022.122123 doi: 10.1504/IJMMNO.2022.122123
![]() |
[25] |
J. Mushanyu, C. W. Chukwu, C. E. Madubueze, Z. Chazuka, C. P. Ogbogbo, A deterministic compartmental model for investigating the impact of escapees on the transmission dynamics of COVID-19, Healthc. Anal., 4 (2023), 100275. https://doi.org/10.1016/j.health.2023.100275 doi: 10.1016/j.health.2023.100275
![]() |
[26] |
S. Gao, P. Binod, C. W. Chukwu, T. Kwofie, S. Safdar, L. Newman, et al., A mathematical model to assess the impact of testing and isolation compliance on the transmission of COVID-19, Infect. Disease Model., 8 (2023), 427–444. https://doi.org/10.1016/j.idm.2023.04.005 doi: 10.1016/j.idm.2023.04.005
![]() |
[27] |
S. M. Simelane, P. G. Dlamini, F. J. Osaye, G. Obaido, B. Ogbukiri, K. Aruleba, et al., Modeling the impact of public health education on tungiasis dynamics with saturated treatment: Insight through the Caputo fractional derivative, Math. Biosci. Eng., 20 (2023), 7696–7720. http://dx.doi.org/10.3934/mbe.2023332 doi: 10.3934/mbe.2023332
![]() |
[28] |
C. Chukwu, R. Alqahtani, C. Alfiniyah, F. Herdicho, Tasmi, A Pontryagin's maximum principle and optimal control model with cost-effectiveness analysis of the COVID-19 epidemic, Decis. Anal. J., 8 (2023), 100273. https://doi.org/10.1016/j.dajour.2023.100273 doi: 10.1016/j.dajour.2023.100273
![]() |
[29] |
Fatmawati, E. Yuliani, C. Alfiniyah, M. L. Juga, C. W. Chukwu, On the modeling of COVID-19 transmission dynamics with two strains: insight through caputo fractional derivative, Fractal Fract., 6 (2022), 346. https://doi.org/10.3390/fractalfract6070346 doi: 10.3390/fractalfract6070346
![]() |
[30] |
E. Bonyah, M. Juga, L. Matsebula, C. Chukwu, On the modeling of COVID-19 spread via fractional derivative: a stochastic approach, Math. Models Comput. Simul., 15 (2023), 338–356. https://doi.org/10.1134/S2070048223020023 doi: 10.1134/S2070048223020023
![]() |
[31] |
T. Li, Y. Guo, Modeling and optimal control of mutated COVID-19 (Delta strain) with imperfect vaccination, Chaos Solitons Fract., 156 (2022), 111825. https://doi.org/10.1016/j.chaos.2022.111825 doi: 10.1016/j.chaos.2022.111825
![]() |
[32] |
Y. Guo, T. Li, Modeling the competitive transmission of the Omicron strain and Delta strain of COVID-19, J. Math. Anal. Appl., 526 (2023), 127283. https://doi.org/10.1016/j.jmaa.2023.127283 doi: 10.1016/j.jmaa.2023.127283
![]() |
[33] |
G. Perone, Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy, Eur. J. Health Econ., 23 (2022), 917–940. https://doi.org/10.1007/s10198-021-01347-4 doi: 10.1007/s10198-021-01347-4
![]() |
[34] | A. A. Ismail, T. Wood, H. C. Bravo, Improving long-horizon forecasts with expectation-biased LSTM networks, preprint paper, 2018. https://doi.org/10.48550/arXiv.1804.06776 |
[35] |
Z. Tarek, M. Y. Shams, S. K. Towfek, H. K. Alkahtani, A. Ibrahim, A. A. Abdelhamid, et al., An optimized model based on deep learning and gated recurrent unit for COVID-19 death prediction, Biomimetics, 8 (2023), 552. https://doi.org/10.3390/biomimetics8070552 doi: 10.3390/biomimetics8070552
![]() |
[36] |
O. Diekmann, J. A. P. Heesterbeek, J. A. Metz, On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations, J. Math. Biol., 28 (1990), 365–382. https://doi.org/10.1007/bf00178324 doi: 10.1007/bf00178324
![]() |
[37] |
H. W. Hethcote, The mathematics of infectious diseases, SIAM Rev., 42 (2000), 599–653. https://doi.org/10.1137/S0036144500371907 doi: 10.1137/S0036144500371907
![]() |
[38] |
P. Van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
![]() |
[39] | R. Gupta, S. K. Pal, Trend analysis and forecasting of COVID-19 outbreak in India, MedRxiv, 2020. https://doi.org/10.1101/2020.03.26.20044511 |
[40] |
P. Wang, X. Zheng, G. Ai, D. Liu, B. Zhu, Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: case studies in Russia, Peru and Iran, Chaos Solitons Fract., 140 (2020), 110214. https://doi.org/10.1016/j.chaos.2020.110214 doi: 10.1016/j.chaos.2020.110214
![]() |
[41] |
S. Dash, C. Chakraborty, S. K. Giri, S. K. Pani, Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics, Pattern Recogn. Lett., 151 (2021), 69–75. https://doi.org/10.1016/j.patrec.2021.07.027 doi: 10.1016/j.patrec.2021.07.027
![]() |
[42] | COVID-19 ZA South Africa Dashboard, Department of health, South Africa, 2022. Available from: https://dsfsi.github.io/covid19za-dash/. |
[43] |
R. T. Aruleba, T. A. Adekiya, N. Ayawei, G. Obaido, K. Aruleba, I. D. Mienye, et al., COVID-19 diagnosis: a review of rapid antigen, RT-PCR and artificial intelligence methods, Bioeng., 9 (2022), 153. https://doi.org/10.3390/bioengineering9040153 doi: 10.3390/bioengineering9040153
![]() |
[44] |
E. Esenogho, I. D. Mienye, T. G. Swart, K. Aruleba, G. Obaido, A neural network ensemble with feature engineering for improved credit card fraud detection, IEEE Access, 10 (2022), 16400–16407. https://doi.org/10.1109/ACCESS.2022.3148298 doi: 10.1109/ACCESS.2022.3148298
![]() |
[45] | K. Cho, B. Van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, et al., Learning phrase representations using RNN encoder-decoder for statistical machine translation, preprint paper, 2014. https://doi.org/10.48550/arXiv.1406.1078 |
[46] | R. Dey, F. M. Salem, Gate-variants of gated recurrent unit (GRU) neural networks, In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), IEEE, 2017, 1597–1600. https://doi.org/10.1109/MWSCAS.2017.8053243 |
[47] |
M. Diagne, H. Rwezaura, S. Tchoumi, J. Tchuenche, A mathematical model of COVID-19 with vaccination and treatment, Comput. Math. Meth. Medic., 2021 (2021), 1250129. https://doi.org/10.1155/2021/1250129 doi: 10.1155/2021/1250129
![]() |
[48] |
M. O. Adewole, A. A. Onifade, F. A. Abdullah, F. Kasali, A. I. Ismail, Modeling the dynamics of COVID-19 in Nigeria, Int. J. Appl. Comput. Math., 7 (2021), 67. https://doi.org/10.1007/s40819-021-01014-5 doi: 10.1007/s40819-021-01014-5
![]() |
[49] |
A. Babaei, H. Jafari, S. Banihashemi, M. Ahmadi, Mathematical analysis of a stochastic model for spread of Coronavirus, Chaos Solitons Fract., 145 (2021), 110788. https://doi.org/10.1016/j.chaos.2021.110788 doi: 10.1016/j.chaos.2021.110788
![]() |
[50] |
C. T. Deressa, Y. O. Mussa, G. F. Duressa, Optimal control and sensitivity analysis for transmission dynamics of Coronavirus, Res. Phys., 19 (2020), 103642. https://doi.org/10.1016/j.rinp.2020.103642 doi: 10.1016/j.rinp.2020.103642
![]() |
[51] |
B. Tang, X. Wang, Q. Li, N. L. Bragazzi, S. Tang, Y. Xiao, et al., Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions, J. Clin. Medic., 9 (2020), 462. https://doi.org/10.3390/jcm9020462 doi: 10.3390/jcm9020462
![]() |
[52] |
S. M. Garba, L. M. S. Lubuma, B. Tsanou, Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa, Math. Biosci., 328 (2020), 108441. https://doi.org/10.1016/j.mbs.2020.108441 doi: 10.1016/j.mbs.2020.108441
![]() |
[53] | South Africa Life Expectancy 1950–2023, Macrotrends, 2023. Available from: https://www.macrotrends.net/countries/ZAF/south-africa/life-expectancy |
[54] |
M. Q. Shakhany, K. Salimifard, Predicting the dynamical behavior of COVID-19 epidemic and the effect of control strategies, Chaos Solitons Fract., 146 (2021), 110823. https://doi.org/10.1016/j.chaos.2021.110823 doi: 10.1016/j.chaos.2021.110823
![]() |
[55] |
S. M. Blower, H. Dowlatabadi, Sensitivity and uncertainty analysis of complex models of disease transmission: an HIV model, as an example, Int. Statist. Rev., 62 (1994), 229–243. https://doi.org/10.2307/1403510 doi: 10.2307/1403510
![]() |
[56] |
L. M. Bogart, B. O. Ojikutu, K. Tyagi, D. J. Klein, M. G. Mutchler, L. Dong, et al., COVID-19 related medical mistrust, health impacts, and potential vaccine hesitancy among Black Americans living with HIV, J. Acq. Imm. Def. Synd., 86 (2021), 200–207. https://doi.org/10.1097/QAI.0000000000002570 doi: 10.1097/QAI.0000000000002570
![]() |
[57] | J. Mphahlele, Conspiracy theories on COVID-19 vaccine can be as deadly as virus itself, South African Med. Res. Council: Cape Town, 2021. |
[58] |
C. Jacob, P. Hausemer, A. Zagoni-bogsch, A. Diers-lawson, The effect of communication and disinformation during the Covid-19 pandemic, European Parliament, 2023. https://doi.org/10.2861/501274 doi: 10.2861/501274
![]() |
[59] |
O. F. Norheim, J. M. Abi-Rached, L. K. Bright, K. Bærøe, O. L. Ferraz, S. Gloppen, et al., Difficult tradeoffs in response to COVID-19: the case for open and inclusive decision making, Nature Medic., 27 (2021), 10–13. https://doi.org/10.1038/s41591-020-01204-6 doi: 10.1038/s41591-020-01204-6
![]() |
[60] | Infodemic, World Health Organization, 2024. Available from: https://www.who.int/health-topics/infodemic#tab = tab_1. |
1. | Soubhagya Kumar Sahoo, Artion Kashuri, Munirah Aljuaid, Soumyarani Mishra, Manuel De La Sen, On Ostrowski–Mercer’s Type Fractional Inequalities for Convex Functions and Applications, 2023, 7, 2504-3110, 215, 10.3390/fractalfract7030215 | |
2. | Artion Kashuri, Soubhagya Kumar Sahoo, Pshtiwan Othman Mohammed, Eman Al-Sarairah, Y. S. Hamed, Some New Hermite-Hadamard Type Inequalities Pertaining to Fractional Integrals with an Exponential Kernel for Subadditive Functions, 2023, 15, 2073-8994, 748, 10.3390/sym15030748 | |
3. | Artion Kashuri, Soubhagya Kumar Sahoo, Munirah Aljuaid, Muhammad Tariq, Manuel De La Sen, Some New Hermite–Hadamard Type Inequalities Pertaining to Generalized Multiplicative Fractional Integrals, 2023, 15, 2073-8994, 868, 10.3390/sym15040868 | |
4. | Thongchai Botmart, Soubhagya Kumar Sahoo, Bibhakar Kodamasingh, Muhammad Amer Latif, Fahd Jarad, Artion Kashuri, Certain midpoint-type Fejér and Hermite-Hadamard inclusions involving fractional integrals with an exponential function in kernel, 2023, 8, 2473-6988, 13785, 10.3934/math.2023700 | |
5. | Muhammad Tariq, Sotiris K. Ntouyas, Asif Ali Shaikh, A Comprehensive Review on the Fejér-Type Inequality Pertaining to Fractional Integral Operators, 2023, 12, 2075-1680, 719, 10.3390/axioms12070719 | |
6. | Çetin Yıldız, Gauhar Rahman, Luminiţa-Ioana Cotîrlă, On Further Inequalities for Convex Functions via Generalized Weighted-Type Fractional Operators, 2023, 7, 2504-3110, 513, 10.3390/fractalfract7070513 | |
7. | Yu Peng, Serap Özcan, Tingsong Du, Symmetrical Hermite–Hadamard type inequalities stemming from multiplicative fractional integrals, 2024, 183, 09600779, 114960, 10.1016/j.chaos.2024.114960 | |
8. | XIAOHUA ZHANG, YUNXIU ZHOU, TINGSONG DU, PROPERTIES AND 2α̃-FRACTAL WEIGHTED PARAMETRIC INEQUALITIES FOR THE FRACTAL (m,h)-PREINVEX MAPPINGS, 2023, 31, 0218-348X, 10.1142/S0218348X23501347 | |
9. | Muhammad Bilal Khan, Ali Althobaiti, Cheng-Chi Lee, Mohamed S. Soliman, Chun-Ta Li, Some New Properties of Convex Fuzzy-Number-Valued Mappings on Coordinates Using Up and Down Fuzzy Relations and Related Inequalities, 2023, 11, 2227-7390, 2851, 10.3390/math11132851 |