Complex Biological Systems and Mathematical Biology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 11927

Special Issue Editors

School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
Interests: mathematic biology; infectious disease dynamics; nonlinear dynamics; big data analysis in medicine and public health
School of Mathematics and Statistics, Shaanxi Normal University, Xi’an 710119, China
Interests: infectious disease dynamics; statistical data analysis; big data

Special Issue Information

Dear Colleagues,

The importance of mathematical modelling approaches for understanding and controlling major diseases, including communicable and non-communicable diseases, is widely accepted. Mathematical models play an increasingly important role in the prediction and early warning of emerging infectious disease outbreaks or developments in non-communicable diseases, optimizing non-pharmaceutical interventions, therapeutic strategies, and so on. We believe that modelers can make substantial contributions to innovating modelling and analysis methods, providing a better understanding of the dynamic mechanisms of complex biological systems, and helping to solve the current major issues in biomedicine. For this reason, we have initiated a Special Issue focusing on new developments in the fast-growing field of mathematical models of complex biological systems. Potential topics include—but are not limited to—the following:

  1. Mathematical modelling and analysis to understand complex biological systems;
  2. Dynamic mechanism of complex biological systems;
  3. Optimization and control for complex biological systems;
  4. Data-driven modeling of complex biological systems;
  5. Machine learning approaches to biological systems;
  6. Advanced computational methods and data science for biology and biomedicine, etc.

Dr. Biao Tang
Dr. Xia Wang
Guest Editors

Manuscript Submission Information

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Keywords

  • infectious disease modelling
  • epidemiological dynamics
  • piecewise dynamic systems
  • optimal control in COVID-19
  • human behaviours evolution dynamics
  • data-driven modelling
  • modelling non-communicable diseases
  • machine learning approaches

Published Papers (9 papers)

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Research

16 pages, 491 KiB  
Article
A Mechanistic Model for Long COVID Dynamics
by Jacob Derrick, Ben Patterson, Jie Bai and Jin Wang
Mathematics 2023, 11(21), 4541; https://doi.org/10.3390/math11214541 - 03 Nov 2023
Viewed by 653
Abstract
Long COVID, a long-lasting disorder following an acute infection of COVID-19, represents a significant public health burden at present. In this paper, we propose a new mechanistic model based on differential equations to investigate the population dynamics of long COVID. By connecting long [...] Read more.
Long COVID, a long-lasting disorder following an acute infection of COVID-19, represents a significant public health burden at present. In this paper, we propose a new mechanistic model based on differential equations to investigate the population dynamics of long COVID. By connecting long COVID with acute infection at the population level, our modeling framework emphasizes the interplay between COVID-19 transmission, vaccination, and long COVID dynamics. We conducted a detailed mathematical analysis of the model. We also validated the model using numerical simulation with real data from the US state of Tennessee and the UK. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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15 pages, 9673 KiB  
Article
Pattern Formation in a Predator–Prey Model with Allee Effect and Hyperbolic Mortality on Multiplex Networks
by Lei Shi, Jiaying Zhou and Yong Ye
Mathematics 2023, 11(15), 3339; https://doi.org/10.3390/math11153339 - 30 Jul 2023
Cited by 1 | Viewed by 921
Abstract
With the rapid development of network science, Turing patterns on complex networks have attracted extensive attention from researchers. In this paper, we focus on spatial patterns in multiplex ER (Erdös-Rényi) random networks, taking the predator–prey model with Allee effect and hyperbolic mortality as [...] Read more.
With the rapid development of network science, Turing patterns on complex networks have attracted extensive attention from researchers. In this paper, we focus on spatial patterns in multiplex ER (Erdös-Rényi) random networks, taking the predator–prey model with Allee effect and hyperbolic mortality as an example. In theory, the threshold condition for generating Turing patterns is given using the Turing instability theory of multiplex networks. Numerically, we design relevant experiments to explore the impact of network topology on Turing patterns. The factors considered include model parameters, diffusion rate, average degree of the network, and differences in the average degree of different layers. The results indicate that the importance of diffusion rate and network average degree for Turing patterns is affirmed on the single-layer network. For multiplex networks, the differentiation of average degrees in different layers controls the generation of Turing patterns, which are not affected by the diffusion rates of the two populations. More interestingly, we observe the switching of Turing patterns and spatiotemporal patterns. We believe that these findings contribute to a better understanding of self-organization on complex networks. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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37 pages, 5758 KiB  
Article
Stability Analysis of Plankton–Fish Dynamics with Cannibalism Effect and Proportionate Harvesting on Fish
by Sk Golam Mortoja, Prabir Panja and Shyamal Kumar Mondal
Mathematics 2023, 11(13), 3011; https://doi.org/10.3390/math11133011 - 06 Jul 2023
Cited by 4 | Viewed by 2196
Abstract
Plankton occupy a vital place in the marine ecosystem due to their essential role. However small or microscopic, their absence can bring the entire life process to a standstill. In this work, we have proposed a prey–predator ecological model consisting of phytoplankton, zooplankton, [...] Read more.
Plankton occupy a vital place in the marine ecosystem due to their essential role. However small or microscopic, their absence can bring the entire life process to a standstill. In this work, we have proposed a prey–predator ecological model consisting of phytoplankton, zooplankton, and fish, incorporating the cannibalistic nature of zooplankton harvesting the fish population. Due to differences in their feeding habits, zooplankton are divided into two sub-classes: herbivorous and carnivorous. The dynamic behavior of the model is examined for each of the possible steady states. The stability criteria of the model have been analyzed from both local and global perspectives. Hopf bifurcation analysis has been accomplished with the growth rate of carnivorous zooplankton using cannibalism as a bifurcation parameter. To characterize the optimal control, we have used Pontryagin’s maximum principle. Subsequently, the optimal system has been derived and solved numerically using an iterative method with Runge–Kutta fourth-order scheme. Finally, to facilitate the interpretation of our mathematical results, we have proceeded to investigate it using numerical simulations. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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24 pages, 663 KiB  
Article
Bifurcation of an SIRS Model with a Modified Nonlinear Incidence Rate
by Yingying Zhang and Chentong Li
Mathematics 2023, 11(13), 2916; https://doi.org/10.3390/math11132916 - 29 Jun 2023
Viewed by 739
Abstract
An SIRS epidemic model with a modified nonlinear incidence rate is studied, which describes that the infectivity is strong at first as the emergence of a new disease or the reemergence of an old disease, but then the psychological effect will weaken the [...] Read more.
An SIRS epidemic model with a modified nonlinear incidence rate is studied, which describes that the infectivity is strong at first as the emergence of a new disease or the reemergence of an old disease, but then the psychological effect will weaken the infectivity. Lastly, the infectivity goes to a saturation state as a result of a crowding effect. The nonlinearity of the functional form of the incidence of infection is modified, which is more reasonable biologically. We analyze the stability of the associated equilibria, and the basic reproduction number and the critical value which determine the dynamics of the model are derived. The bifurcation analysis is presented, including backward bifurcation, saddle-node bifurcation, Bogdanov–Takens bifurcation of codimension two and Hopf bifurcation. To study Hopf bifurcation of codimension three of the model when some assumptions hold, the focus values are calculated. Numerical simulations are shown to verify our results. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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27 pages, 1685 KiB  
Article
The Influence of Migration to Regions with Different Coverages of Health Education on Schistosomiasis
by Pan Tang, Shiwen Qian, Lei Shi, Longxing Qi and Tingting Li
Mathematics 2023, 11(12), 2666; https://doi.org/10.3390/math11122666 - 12 Jun 2023
Viewed by 812
Abstract
Background: Health education plays a vital role in the prevention and control of schistosomiasis in China and throughout the world. However, the coverage of health education varies from place to place for various reasons. Moreover, people with different levels of health education migrate [...] Read more.
Background: Health education plays a vital role in the prevention and control of schistosomiasis in China and throughout the world. However, the coverage of health education varies from place to place for various reasons. Moreover, people with different levels of health education migrate between different regions. Methods: In order to analyze the effects of different coverages of health education on schistosomiasis transmission, a schistosomiasis mathematical model with people’s inter-regional migration is constructed in two regions with different coverages of health education. The basic reproduction number is calculated, the global stability of the system is analyzed qualitatively, and a numerical simulation is carried out. Results: (1) The transmission trend of schistosomiasis could be reduced by increasing the migration of the susceptible population from the region with a high coverage of health education to the region with low coverage, or by increasing the migration of the infected population between the two regions. Schistosomiasis can even be eliminated if the migration of the susceptible or infected population from the region with a high coverage of health education to the region with a low coverage is sufficiently large. This is quite different from the prevention and control of other epidemics in which the movement of people should be restricted. (2) A low coverage of health education will have an impact on the number of patients and infected snails in both of the two regions. This result indicates that increasing the coverage of health education can reduce the risk of schistosomiasis not only in the local population but also in the surrounding regions to which people migrate. Conclusions: There is no need to restrict the migration of the infected population between the two regions nor the migration of the susceptible population from the region with a high coverage of health education to the region with a low coverage. However, there is a need to restrict the migration of the susceptible population from the region with a low coverage of health education to the region with a high coverage. These are some suggestions to prevent and control schistosomiasis. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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18 pages, 628 KiB  
Article
Mathematical Analysis of a Bacterial Competition in a Continuous Reactor in the Presence of a Virus
by Abdulrahman Ali Alsolami and Miled El Hajji
Mathematics 2023, 11(4), 883; https://doi.org/10.3390/math11040883 - 09 Feb 2023
Cited by 8 | Viewed by 1216
Abstract
In this paper, we discuss the competition of two species for a single essential growth-limiting nutriment with viral infection that affects only the first species. Although the classical models without viral infection suggest competitive exclusion, this model exhibits the stable coexistence of both [...] Read more.
In this paper, we discuss the competition of two species for a single essential growth-limiting nutriment with viral infection that affects only the first species. Although the classical models without viral infection suggest competitive exclusion, this model exhibits the stable coexistence of both species. We reduce the fourth-dimension proposed model to a three-dimension one. Thus, the coexistence of the two competing species is demonstrated using the theory of uniform persistence applied to the three-variable reduced system. We prove that there is no coexistence of both species without the presence of the virus and the satisfaction of some assumptions on the growth rates of species. Finally, we give some numerical simulations to confirm the obtained theoretical findings. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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18 pages, 739 KiB  
Article
Bayesian Inference for COVID-19 Transmission Dynamics in India Using a Modified SEIR Model
by Kai Yin, Anirban Mondal, Martial Ndeffo-Mbah, Paromita Banerjee, Qimin Huang and David Gurarie
Mathematics 2022, 10(21), 4037; https://doi.org/10.3390/math10214037 - 31 Oct 2022
Cited by 4 | Viewed by 1505
Abstract
We propose a modified population-based susceptible-exposed-infectious-recovered (SEIR) compartmental model for a retrospective study of the COVID-19 transmission dynamics in India during the first wave. We extend the conventional SEIR methodology to account for the complexities of COVID-19 infection, its multiple symptoms, and transmission [...] Read more.
We propose a modified population-based susceptible-exposed-infectious-recovered (SEIR) compartmental model for a retrospective study of the COVID-19 transmission dynamics in India during the first wave. We extend the conventional SEIR methodology to account for the complexities of COVID-19 infection, its multiple symptoms, and transmission pathways. In particular, we consider a time-dependent transmission rate to account for governmental controls (e.g., national lockdown) and individual behavioral factors (e.g., social distancing, mask-wearing, personal hygiene, and self-quarantine). An essential feature of COVID-19 that is different from other infections is the significant contribution of asymptomatic and pre-symptomatic cases to the transmission cycle. A Bayesian method is used to calibrate the proposed SEIR model using publicly available data (daily new tested positive, death, and recovery cases) from several Indian states. The uncertainty of the parameters is naturally expressed as the posterior probability distribution. The calibrated model is used to estimate undetected cases and study different initial intervention policies, screening rates, and public behavior factors, that can potentially strike a balance between disease control and the humanitarian crisis caused by a sudden strict lockdown. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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15 pages, 508 KiB  
Article
Dynamical Behavior of a Fractional Order Model for Within-Host SARS-CoV-2
by Kaushik Dehingia, Ahmed A. Mohsen, Sana Abdulkream Alharbi, Reima Daher Alsemiry and Shahram Rezapour
Mathematics 2022, 10(13), 2344; https://doi.org/10.3390/math10132344 - 04 Jul 2022
Cited by 9 | Viewed by 1338
Abstract
The prime objective of the current study is to propose a novel mathematical framework under the fractional-order derivative, which describes the complex within-host behavior of SARS-CoV-2 by taking into account the effects of memory and carrier. To do this, we formulate a mathematical [...] Read more.
The prime objective of the current study is to propose a novel mathematical framework under the fractional-order derivative, which describes the complex within-host behavior of SARS-CoV-2 by taking into account the effects of memory and carrier. To do this, we formulate a mathematical model of SARS-CoV-2 under the Caputo fractional-order derivative. We derived the conditions for the existence of equilibria of the model and computed the basic reproduction number R0. We used mathematical analysis to establish the proposed model’s local and global stability results. Some numerical resolutions of our theoretical results are presented. The main result of this study is that as the fractional derivative order increases, the approach of the solution to the equilibrium points becomes faster. It is also observed that the value of R0 increases as the value of β and πv increases. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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18 pages, 2536 KiB  
Article
Epidemic Dynamics of Two-Pathogen Spreading for Pairwise Models
by Shanshan Chen, Yijun Ran, Hebo Huang, Zhenzhen Wang and Ke-ke Shang
Mathematics 2022, 10(11), 1906; https://doi.org/10.3390/math10111906 - 02 Jun 2022
Cited by 4 | Viewed by 1543
Abstract
In the real world, pathogens do not exist in isolation. The transmission of one pathogen may be affected by the presence of other pathogens, and certain pathogens generate multiple strains with different spreading features. Hence, the behavior of multi-pathogen transmission has attracted much [...] Read more.
In the real world, pathogens do not exist in isolation. The transmission of one pathogen may be affected by the presence of other pathogens, and certain pathogens generate multiple strains with different spreading features. Hence, the behavior of multi-pathogen transmission has attracted much attention in epidemiological research. In this paper, we use the pairwise approximation method to formulate two-pathogen models capturing cross-immunity, super-infection, and co-infection phenomena, in which each pathogen follows a susceptible-infected-susceptible (SIS) mechanism. For each model, we calculate the basic reproduction number and analyze the stability of equilibria, and discuss the differences from the mean-field approach. We demonstrate that simulations are in good agreement with the analytical results. Full article
(This article belongs to the Special Issue Complex Biological Systems and Mathematical Biology)
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