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Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 41433
Related Special Issue: Spreading of Infectious Diseases Like COVID-19 and Influenza: Modelling and Propagation Control 2.0

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Prof. Dr. Johann Michael Köhler

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Institute of Chemistry and Bioengineering, Department of Physical Chemistry and Microreaction Technology, Technische Universität Ilmenau, 98693 Ilmenau, Germany
Interests: microfluidic synthesis of metal nanoparticles; electrical properties of nanoparticles; non-spherical and composite nanoparticles; nanoparticles in sensing and labelling
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Prof. Dr. Stefan Wölfl

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Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Im Neuenheimer Feld 364, 69120 Heidelberg, Germany
Interests: bioanalytics; cellular biosensors for drug testing and environmental analysis; gene regulation in mammalian cells in disease models; influence of basic biochemical processes on DNA-damage and stress response
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Special Issue Information

Dear Colleagues,

The recent propagation of Covid19 demands new strategies for the monitoring and control of epidemic diseases. Despite the fact that the understanding of the molecular biology of viruses is much better than decades before, humanity is confronted with the global spread of a very dangerous viral pathogen. Recently, all countries have been looking suitable strategies to reduce contacts between persons as well as to keep the regular running everyday life as far as possible. The first months of Covid-19 spreading have shown that the mechanisms of virus transfer, infection, and the stimulation of immune reactions are not sufficiently understood. Besides experimental biosciences and medicine, the modelling of infection, infectious propagations, individual immune reactions, and herd immunity should be described by suitable models.

The Special Issue is dedicated to new approaches of modelling of infection, epidemic propagation, and all aspects of connecting individual responses to pathogens with pandemic developments and their consequences. Contributions from different fields – coming from medical, molecular biological, ecological, kinetical, biophysical, physicochemical, and mathematical point of views, as well as interdisciplinary concepts, are welcome.

The deadline for submitting manuscripts is 31 October 2020. However, authors are encouraged to submit their papers earlier in order to contribute to the recent scientific discussions and for supporting the correct decisions for managing the control of the recent pandemic as soon as possible.

Prof. Dr. Johann Michael Köhler
Prof. Dr. Stefan Wölfl
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

24 pages, 3455 KiB

Open AccessArticle

A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths

by Paolo S. Valvo

Appl. Sci. 2020, 10(23), 8500; https://doi.org/10.3390/app10238500 - 28 Nov 2020

Cited by 11 | Viewed by 4545

Abstract

The paper presents a phenomenological epidemiological model for the description and prediction of the time trends of COVID-19 deaths worldwide. A bimodal distribution function—defined as the mixture of two lognormal distributions—is assumed to model the time distribution of deaths in a country. The asymmetric lognormal distribution enables better data fitting with respect to symmetric distribution functions. Besides, the presence of a second mode allows the model to also describe second waves of the epidemic. For each country, the model has six parameters, which are determined by fitting the available data through a nonlinear least-squares procedure. The fitted curves can then be extrapolated to predict the future trends of the total and daily number of deaths. Results for the six continents and the World are obtained by summing those computed for the 210 countries in the Our World in Data (OWID) dataset. To assess the accuracy of predictions, a validation study is first conducted. Then, based on data available as of 30 September 2020, the future trends are extrapolated until the end of year 2020. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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24 pages, 5985 KiB

Open AccessArticle

On an SEIR Epidemic Model with Vaccination of Newborns and Periodic Impulsive Vaccination with Eventual On-Line Adapted Vaccination Strategies to the Varying Levels of the Susceptible Subpopulation

by Malen Etxeberria-Etxaniz, Santiago Alonso-Quesada and Manuel De la Sen

Appl. Sci. 2020, 10(22), 8296; https://doi.org/10.3390/app10228296 - 23 Nov 2020

Cited by 23 | Viewed by 3812

Abstract

This paper investigates a susceptible-exposed-infectious-recovered (SEIR) epidemic model with demography under two vaccination effort strategies. Firstly, the model is investigated under vaccination of newborns, which is fact in a direct action on the recruitment level of the model. Secondly, it is investigated under a periodic impulsive vaccination on the susceptible in the sense that the vaccination impulses are concentrated in practice in very short time intervals around a set of impulsive time instants subject to constant inter-vaccination periods. Both strategies can be adapted, if desired, to the time-varying levels of susceptible in the sense that the control efforts be increased as those susceptible levels increase. The model is discussed in terms of suitable properties like the positivity of the solutions, the existence and allocation of equilibrium points, and stability concerns related to the values of the basic reproduction number. It is proven that the basic reproduction number lies below unity, so that the disease-free equilibrium point is asymptotically stable for larger values of the disease transmission rates under vaccination controls compared to the case of absence of vaccination. It is also proven that the endemic equilibrium point is not reachable if the disease-free one is stable and that the disease-free equilibrium point is unstable if the reproduction number exceeds unity while the endemic equilibrium point is stable. Several numerical results are investigated for both vaccination rules with the option of adapting through ime the corresponding efforts to the levels of susceptibility. Such simulation examples are performed under parameterizations related to the current SARS-COVID 19 pandemic. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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14 pages, 1003 KiB

Open AccessArticle

SIM-D: An Agent-Based Simulator for Modeling Contagion in Population

by Muhammad Waleed, Tai-Won Um, Tariq Kamal, Aftab Khan and Zaka Ullah Zahid

Appl. Sci. 2020, 10(21), 7745; https://doi.org/10.3390/app10217745 - 02 Nov 2020

Cited by 5 | Viewed by 2851

Abstract

The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this tool is that it is generic; therefore, it can simulate a wide variety of infectious disease models (SIR), susceptible-infectious-susceptible (SIS) and susceptible-infectious (SI). The proposed simulation engine will help the policy-makers and public health authorities study the behavior of disease spreading; thus, allowing for better planning. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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16 pages, 427 KiB

Open AccessArticle

Optimal Containment Control Strategy of the Second Phase of the COVID-19 Lockdown in Morocco

by Mustapha Lhous, Omar Zakary, Mostafa Rachik, El Mostafa Magri and Abdessamad Tridane

Appl. Sci. 2020, 10(21), 7559; https://doi.org/10.3390/app10217559 - 27 Oct 2020

Cited by 7 | Viewed by 1952

Abstract

This work investigates the optimal control of the second phase of the COVID-19 lockdown in Morocco. The model consists of susceptible, exposed, infected, recovered, and quarantine compartments (SEIRQD model), where we take into account contact tracing, social distancing, quarantine, and treatment measures during the nationwide lockdown in Morocco. First, we present different components of the model and their interactions. Second, to validate our model, the nonlinear least-squares method is used to estimate the model’s parameters by fitting the model outcomes to real data of the COVID-19 in Morocco. Next, to investigate the impact of optimal control strategies on this pandemic in the country. We also give numerical simulations to illustrate and compare the obtained results with the actual situation in Morocco. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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13 pages, 2084 KiB

Open AccessArticle

Epidemiological Modeling of COVID-19 in Saudi Arabia: Spread Projection, Awareness, and Impact of Treatment

by Yousef Alharbi, Abdulrahman Alqahtani, Olayan Albalawi and Mohsen Bakouri

Appl. Sci. 2020, 10(17), 5895; https://doi.org/10.3390/app10175895 - 26 Aug 2020

Cited by 18 | Viewed by 5083

Abstract

The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible–infectious–recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models’ fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared (

R^{2}

), adjusted R² (

R_{adj}^{2}

), and Akaike’s information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69,

R^{2}

= 99.88%,

R_{adj}^{2}

= 99.98%, and

AIC

= 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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18 pages, 5708 KiB

Open AccessArticle

SEIRD COVID-19 Formal Characterization and Model Comparison Validation

by Pau Fonseca i Casas, Víctor García i Carrasco and Joan Garcia i Subirana

Appl. Sci. 2020, 10(15), 5162; https://doi.org/10.3390/app10155162 - 27 Jul 2020

Cited by 23 | Viewed by 6342

Abstract

Based on a SEIRD model (Susceptible, Exposed, Infective, Recovered and Deceased) for COVID-19 infection with a new parametrization using a high infection rate, and a low fatality, we define the model in System Dynamics, Python, and Specification and Description Language (SDL). The different implementations obtained can be improved depending on the capabilities of the approach and, more interestingly, can be used to improve the Validation and Verification processes. In this paper, we are focused on describing how this comparison with other models’ validation processes allows us to find the parameters of the system dynamics model, hence the parameters of the pandemic. This is a crucial element, specifically in this case, because the data are not complete or validated for different reasons. We use using existing data from Korea and Spain and showing that the proposed method and the obtained parametrization for the model fit with the empirical evidence. We discuss some implications of the validation process and the model parametrization. We use this approach to implement a Decision support system that shows the current pandemic situation in Catalonia. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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11 pages, 2666 KiB

Open AccessArticle

Improved Epidemic Dynamics Model and Its Prediction for COVID-19 in Italy

by Han Wang, Kang Xu, Zhongyi Li, Kexin Pang and Hua He

Appl. Sci. 2020, 10(14), 4930; https://doi.org/10.3390/app10144930 - 17 Jul 2020

Cited by 12 | Viewed by 2449

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health crisis due to its high contagious characteristics. In this article, we propose a new epidemic-dynamics model combining the transmission characteristics of COVID-19 and then use the reported epidemic data from 15 February to 30 June to simulate the spread of the Italian epidemic. Numerical simulations showed that (1) there was a remarkable amount of asymptomatic individuals; (2) the lockdown measures implemented by Italy effectively controlled the spread of the outbreak; (3) the Italian epidemic has been effectively controlled, but SARS-CoV-2 will still exist for a long time; and (4) the intervention of the government is an important factor that affects the spread of the epidemic. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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9 pages, 647 KiB

Open AccessArticle

RRM Prediction of Erythrocyte Band3 Protein as Alternative Receptor for SARS-CoV-2 Virus

by Irena Cosic, Drasko Cosic and Ivan Loncarevic

Appl. Sci. 2020, 10(11), 4053; https://doi.org/10.3390/app10114053 - 11 Jun 2020

Cited by 38 | Viewed by 8701

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new coronavirus causing a worldwide pandemic. It is infecting respiratory organs and, in more severe cases, the lungs, where it is infecting the human cells through the angiotensin-converting enzyme 2 (ACE2) receptor. In severe cases, it is characterized not only by difficulties in breathing through infected lungs, but also with disproportionally and, thus far, unexplained low levels of oxygen in the blood. Here, we propose that, besides the infection of respiratory organs through ACE2 receptors, there is an additional infection in the red blood cells (erythrocytes). There could be a possible for SARS-CoV-2 to pass through the alveoli membrane in the lungs and infect the red blood cells through another receptor. Using our own biophysical model, the Resonant Recognition Model, we propose that the red blood cell (RBC) Band3 protein on the surface of red blood cells is a possible entry point for the SARS-CoV-2 virus into red blood cells. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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11 pages, 1507 KiB

Open AccessArticle

A Critical Analysis of Corona Related Data: What the More Reliable Data Can Imply for Western-Europe

by Robert J. Meier

Appl. Sci. 2020, 10(10), 3398; https://doi.org/10.3390/app10103398 - 14 May 2020

Cited by 2 | Viewed by 1799

Abstract

We present a less common type of discussion about COVID-19 data, beginning with the observation that the number of people reported deceased following COVID-19 infection is currently the most reliable dataset to be used. When the available real-life data are visualized for a number of European countries, they reveal the commonly seen exponential increase, though with different absolute rates, and over time different periods. More interesting information is obtained upon inspection of the daily increments in deaths. These curves look very similar to those for China, and seem to indicate that in European countries that have imposed more strict human–human contact measures, in particular Italy and Spain, where we have seen a decrease in daily deaths since early April, it is to be expected it will take 40–50 days from the end of March until this number has fallen to negligible levels. Taking the initial increase in the number of deaths for Germany, and combining this with typical values for the mortality reported in the literature and the published number of daily contacts for the working population, we calculated an initial increase in infections of 20 per day by a single infected person with an average human–human contact number of 22, decreasing to 5.5 after the first 10 days. The high number at the outset is likely related to outbreaks in a high local concentration of people. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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12 pages, 3477 KiB

Open AccessCommunication

Infection-Immunity Competition: A Simple Model for Illustrating the Background of Individual Response on Herd Immunity

by Johann Michael Köhler

Appl. Sci. 2020, 10(9), 3078; https://doi.org/10.3390/app10093078 - 28 Apr 2020

Cited by 2 | Viewed by 2817

Abstract

For achieving herd immunity, the proportion of individuals who are immunized, and the proportion of susceptible individuals are normally regarded as the key factors. Here, it is discussed that the immunity is not a yes/no decision in all cases, but a limited (relative) immunity should be kept in mind. This effect would cause a dependence of infection from the level of immunity and the strength of single-infection impact events (virus load). As a result, a stepwise enhancement of low-level immunity could be achieved in case of infection contacts at low concentrations of infectious particles. This behavior is probably important for airborne infection paths. Therefore, it might play a role in the case of the recent SARS (new coronavirus) pandemic and could have a strong effect on herd immunity. Full article

(This article belongs to the Special Issue Spreading of Infection Diseases like COVID-19 and Influenza: Modelling and Propagation Control)

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