Eco-Evolutionary and Computational Epidemiology Modeling of Complex Dynamical Systems

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 4021

Special Issue Editors


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Guest Editor
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Interests: computational epidemiology; biostatistics; disease ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Eco-evolutionary and computational epidemiology modeling of complex dynamical systems aids in understanding population dynamics and the transmission of infectious diseases in the studied ecosystem. Examples of this phenomenon include studying how species interact with their immediate surroundings in the case of eco-evolutionary modeling and how to predict and forecast pandemic peaks in the case of computational epidemiology.

This issue will highlight advancements in research in this area in order to gain a better understanding of eco-evolution and epidemics.

Specific methods and application fields include but are not limited to:

  • Epidemic modeling in a changing world;
  • Modeling of species interaction;
  • Computational analysis of disease dynamics;
  • Intraspecific diversity and biodiversity;
  • Multiscale modeling of emerging diseases;
  • Geoclimatic condition influence on ecological systems;
  • Prediction of physical properties of disease modeling.

Prof. Dr. Jacques Demongeot
Dr. Kayode Oshinubi
Guest Editors

Manuscript Submission Information

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Keywords

  • species interaction
  • environmental factors
  • dynamical system
  • population dynamics
  • epidemiology
  • modeling

Published Papers (2 papers)

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Research

13 pages, 433 KiB  
Article
Modeling the Dynamic Effects of Human Mobility and Airborne Particulate Matter on the Spread of COVID-19
by Klot Patanarapeelert, Rossanan Chandumrong and Nichaphat Patanarapeelert
Computation 2023, 11(11), 211; https://doi.org/10.3390/computation11110211 - 30 Oct 2023
Viewed by 1172
Abstract
Identifying the relationship between human mobility, air pollution, and communicable disease poses a challenge for impact evaluation and public health planning. Specifically, Coronavirus disease 2019 (COVID-19) and air pollution from fine particulates (PM2.5), by which human mobility is mediated in a public health [...] Read more.
Identifying the relationship between human mobility, air pollution, and communicable disease poses a challenge for impact evaluation and public health planning. Specifically, Coronavirus disease 2019 (COVID-19) and air pollution from fine particulates (PM2.5), by which human mobility is mediated in a public health emergency. To describe the interplay between human mobility and PM2.5 during the spread of COVID-19, we proposed a nonlinear model of the time-dependent transmission rate as a function of these factors. A compartmental epidemic model, together with daily confirmed case data in Bangkok, Thailand during 2020–2021, was used to estimate the intrinsic parameters that can determine the impact on the transmission dynamic of the two earlier outbreaks. The results suggested a positive association between mobility and transmission, but this was strongly dependent on the context and the temporal characteristics of the data. For the ascending phase of an epidemic, the estimated coefficient of mobility variable in the second wave was greater than in the first wave, but the value of the mobility component in the transmission rate was smaller. Due to the influence of the baseline value and PM2.5, the estimated basic reproduction number of the second wave was higher than that of the first wave, even though mobility had a greater influence. For the descending phase, the value of the mobility component in the second wave was greater, due to the negative value of the estimated mobility coefficient. Despite this scaling effect, the results suggest a negative association between PM2.5 and the transmission rates. Although this conclusion agrees with some previous studies, the true effect of PM2.5 remains inconclusive and requires further investigation. Full article
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21 pages, 2367 KiB  
Article
Mathematical Modelling of Tuberculosis Outbreak in an East African Country Incorporating Vaccination and Treatment
by Kayode Oshinubi, Olumuyiwa James Peter, Emmanuel Addai, Enock Mwizerwa, Oluwatosin Babasola, Ifeoma Veronica Nwabufo, Ibrahima Sane, Umar Muhammad Adam, Adejimi Adeniji and Janet O. Agbaje
Computation 2023, 11(7), 143; https://doi.org/10.3390/computation11070143 - 17 Jul 2023
Cited by 11 | Viewed by 2371
Abstract
In this paper, we develop a deterministic mathematical epidemic model for tuberculosis outbreaks in order to study the disease’s impact in a given population. We develop a qualitative analysis of the model by showing that the solution of the model is positive and [...] Read more.
In this paper, we develop a deterministic mathematical epidemic model for tuberculosis outbreaks in order to study the disease’s impact in a given population. We develop a qualitative analysis of the model by showing that the solution of the model is positive and bounded. The global stability analysis of the model uses Lyapunov functions and the threshold quantity of the model, which is the basic reproduction number is estimated. The existence and uniqueness analysis for Caputo fractional tuberculosis outbreak model is presented by transforming the deterministic model to a Caputo sense model. The deterministic model is used to predict real data from Uganda and Rwanda to see how well our model captured the dynamics of the disease in the countries considered. Furthermore, the sensitivity analysis of the parameters according to R0 was considered in this study. The normalised forward sensitivity index is used to determine the most sensitive variables that are important for infection control. We simulate the Caputo fractional tuberculosis outbreak model using the Adams–Bashforth–Moulton approach to investigate the impact of treatment and vaccine rates, as well as the disease trajectory. Overall, our findings imply that increasing vaccination and especially treatment availability for infected people can reduce the prevalence and burden of tuberculosis on the human population. Full article
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