Special Issue "Physics Methods in Coronavirus Pandemic Analysis"

A special issue of Physics (ISSN 2624-8174). This special issue belongs to the section "Life Physics".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Reinhard Schlickeiser
E-Mail Website
Guest Editor
Theoretical Physics Institute, Ruhr University Bochum, 44780 Bochum, Germany
Interests: astrophysics; plasma physics
Prof. Dr. Martin Kröger
E-Mail Website1 Website2
Guest Editor
Polymer Physics, Department of Materials, ETH Zurich, Leopold-Ruzicka-Weg 4, CH-8093 Zurich, Switzerland
Interests: polymer physics; computational physics; applied mathematics; stochastic differential equations; coarse-graining; biophysics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect information about models or theoretical procedures that have been newly developed for, or successfully applied to, the ongoing Covid-19 pandemic. Suitable contributions should offer potentially helpful predictions, clarify the inter-dependence of phenomena, or confirm the usefulness of an existing approach. In light of the rising number of contributions addressing various aspects of the crisis, we will not accept incremental research contributions to this Special Issue. We are looking for clearly presented descriptions of either novel or more classical and promising approaches to understanding and controlling the spread of a pandemic. Purely theoretical works (including model development or statistical analysis) are welcome. Numerical works should either be reproducible with minor effort using the manuscript at hand, or have a computer code as part of the Supplementary Information. Large-scale simulations should be sufficiently well described, and offered either as part of the Supplementary Information or as an online application, using currently available data.

Prof. Dr. Reinhard Schlickeiser
Prof. Dr. Martin Kröger
Guest Editors

Manuscript Submission Information

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 papers will be 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. Physics is an international peer-reviewed open access quarterly 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 1200 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.

Keywords

  • statistical analysis
  • forecasting
  • time evolution
  • extrapolation
  • parameter estimation
  • simulation
  • algorithms
  • machine learning
  • classification
  • dynamical models
  • epidemiologic models

Published Papers (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
A Local and Time Resolution of the COVID-19 Propagation—A Two-Dimensional Approach for Germany Including Diffusion Phenomena to Describe the Spatial Spread of the COVID-19 Pandemic
Physics 2021, 3(3), 536-548; https://0-doi-org.brum.beds.ac.uk/10.3390/physics3030033 - 07 Jul 2021
Viewed by 426
Abstract
The understanding of factors that affect the dissemination of a viral infection is fundamental to help combat it. For instance, during the COVID-19 pandemic that changed the lives of people all over the world, one observes regions with different incidences of cases. One [...] Read more.
The understanding of factors that affect the dissemination of a viral infection is fundamental to help combat it. For instance, during the COVID-19 pandemic that changed the lives of people all over the world, one observes regions with different incidences of cases. One can speculate that population density might be one of the variables that affect the incidence of cases. In populous areas, such as big cities or congested urban areas, higher COVID-19 incidences could be observed than in rural regions. It is natural to think that if population density is such an important factor, then a gradient or difference in population density might lead to a diffusion process that will proceed until equilibrium is reached. The aim of this paper consists of the inclusion of a diffusion concept into the COVID-19 modeling. With this concept, one covers a gradient-driven transfer of the infection next to epidemic growth models (SIR-type models). This is discussed for a certain period of the German situation based on the quite different incidence data for the different federal states of Germany. With this ansatz, some phenomena of the actual development of the pandemic are found to be confirmed. The model provides a possibility to investigate certain scenarios, such as border-crossings or local spreading events, and their influence on the COVID-19 propagation. The resulting information can be a basis for the decisions of politicians and medical persons in charge of managing a pandemic. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Article
SIR-PID: A Proportional–Integral–Derivative Controller for COVID-19 Outbreak Containment
Physics 2021, 3(3), 459-472; https://0-doi-org.brum.beds.ac.uk/10.3390/physics3030031 - 27 Jun 2021
Viewed by 622
Abstract
Ongoing social restrictions, including social distancing and lockdown, adopted by many countries to inhibit spread of the the COVID-19 epidemic, must attempt to find a trade-off between induced economic damage, healthcare system collapse, and the costs in terms of human lives. Applying and [...] Read more.
Ongoing social restrictions, including social distancing and lockdown, adopted by many countries to inhibit spread of the the COVID-19 epidemic, must attempt to find a trade-off between induced economic damage, healthcare system collapse, and the costs in terms of human lives. Applying and removing restrictions on a system with a given latency as represented by an epidemic outbreak (and formally comparable with mechanical inertia), may create critical instabilities, overshoots, and strong oscillations in the number of infected people around the desirable set-point, defined in a practical way as the maximum number of hospitalizations acceptable by a given healthcare system. A good understanding of the system reaction to any change of the input control variable can be reasonably achieved using a proportional–integral–derivative controller (PID), which is a widely used technique in various physics and technological applications. In this paper, this control theory to is proposed to be applied epidemiology, to understand the reaction of COVID-19 propagation to social restrictions and to reduce epidemic damages through the correct tuning of the containment policy. Regarding the synthesis of this interdisciplinary approach, the extended to the susceptible–infectious–recovered (SIR) model name “SIR-PID” is suggested. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Article
Analytical Modeling of the Temporal Evolution of Epidemics Outbreaks Accounting for Vaccinations
Physics 2021, 3(2), 386-426; https://0-doi-org.brum.beds.ac.uk/10.3390/physics3020028 - 25 May 2021
Cited by 4 | Viewed by 1222
Abstract
With the vaccination against Covid-19 now available, how vaccination campaigns influence the mathematical modeling of epidemics is quantitatively explored. In this paper, the standard susceptible-infectious-recovered/removed (SIR) epidemic model is extended to a fourth compartment, V, of vaccinated persons. This extension involves the time [...] Read more.
With the vaccination against Covid-19 now available, how vaccination campaigns influence the mathematical modeling of epidemics is quantitatively explored. In this paper, the standard susceptible-infectious-recovered/removed (SIR) epidemic model is extended to a fourth compartment, V, of vaccinated persons. This extension involves the time t-dependent effective vaccination rate, v(t), that regulates the relationship between susceptible and vaccinated persons. The rate v(t) competes with the usual infection, a(t), and recovery, μ(t), rates in determining the time evolution of epidemics. The occurrence of a pandemic outburst with rising rates of new infections requires k+b<12η, where k=μ(0)/a(0) and b=v(0)/a(0) denote the initial values for the ratios of the three rates, respectively, and η1 is the initial fraction of infected persons. Exact analytical inverse solutions t(Q) for all relevant quantities Q=[S,I,R,V] of the resulting SIRV model in terms of Lambert functions are derived for the semi-time case with time-independent ratios k and b between the recovery and vaccination rates to the infection rate, respectively. These inverse solutions can be approximated with high accuracy, yielding the explicit time-dependences Q(t) by inverting the Lambert functions. The values of the three parameters k, b and η completely determine the reduced time evolution of the SIRV-quantities Q(τ). The influence of vaccinations on the total cumulative number and the maximum rate of new infections in different countries is calculated by comparing with monitored real time Covid-19 data. The reduction in the final cumulative fraction of infected persons and in the maximum daily rate of new infections is quantitatively determined by using the actual pandemic parameters in different countries. Moreover, a new criterion is developed that decides on the occurrence of future Covid-19 waves in these countries. Apart from in Israel, this can happen in all countries considered. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Article
Comparison of Main Covid-19 Outbreaks and Interpretation Based on Age Differences
Physics 2021, 3(1), 119-127; https://0-doi-org.brum.beds.ac.uk/10.3390/physics3010010 - 16 Mar 2021
Viewed by 621
Abstract
The main scope of this study is a critical comparison of data coming from different regions in the world, where significant outbreaks of the Covid-19 pandemic took place, accounting for age differences among the considered samples. Scaling laws are derived, driving interpretations of [...] Read more.
The main scope of this study is a critical comparison of data coming from different regions in the world, where significant outbreaks of the Covid-19 pandemic took place, accounting for age differences among the considered samples. Scaling laws are derived, driving interpretations of the death toll in the analyzed clusters. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Article
Simulation of Spatial Spread of the COVID-19 Pandemic on the Basis of the Kinetic-Advection Model
Physics 2021, 3(1), 85-102; https://0-doi-org.brum.beds.ac.uk/10.3390/physics3010008 - 18 Feb 2021
Cited by 1 | Viewed by 899
Abstract
A new two-parameter kinetic equation model is proposed to describe the spatial spread of the virus in the current pandemic COVID-19. The migration of infection carriers from certain foci inherent in some countries is considered. The one-dimensional model is applied to three countries: [...] Read more.
A new two-parameter kinetic equation model is proposed to describe the spatial spread of the virus in the current pandemic COVID-19. The migration of infection carriers from certain foci inherent in some countries is considered. The one-dimensional model is applied to three countries: Russia, Italy, and Chile. Both their geographical location and their particular shape stretching in the direction from the centers of infection (Moscow, Lombardy, and Santiago, respectively) make it possible to use such an approximation. The dynamic density of the infected is studied. Two parameters of the model are derived from known data. The first is the value of the average spreading rate associated with the transfer of infected persons in transport vehicles. The second is the frequency of the decrease in numbers of the infected as they move around the country, associated with the arrival of passengers at their destination. An analytical solution is obtained. Simple numerical methods are also used to perform a series of calculations. Calculations us to make some predictions, for example, about the time of recovery in Russia, if the beginning of recovery in Moscow is known. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Article
An Analysis of the Italian Lockdown in Retrospective Using Particle Swarm Optimization in Machine Learning Applied to an Epidemiological Model
Physics 2020, 2(3), 368-382; https://0-doi-org.brum.beds.ac.uk/10.3390/physics2030020 - 01 Aug 2020
Cited by 4 | Viewed by 1849
Abstract
A critical analysis of the open data provided by the Italian Civil Protection Centre during phase 1 of Covid-19 epidemic—the so-called Italian lockdown—is herein proposed in relation to four of the most affected Italian regions, namely Lombardy, Reggio Emilia, Valle d’Aosta, and Veneto. [...] Read more.
A critical analysis of the open data provided by the Italian Civil Protection Centre during phase 1 of Covid-19 epidemic—the so-called Italian lockdown—is herein proposed in relation to four of the most affected Italian regions, namely Lombardy, Reggio Emilia, Valle d’Aosta, and Veneto. A possible bias in the data induced by the extent in the use of medical swabs is found in relation to Valle d’Aosta and Veneto. Observed data are then interpreted using a Susceptible-Infectious-Recovered (SIR) epidemiological model enhanced with asymptomatic (infected and recovered) compartments, including lockdown effects through time-dependent model parameters. The initial number of susceptible individuals for each region is also considered as a parameter to be identified. The issue of parameters identification is herein addressed by a robust machine learning approach based on particle swarm optimization. Model predictions provide relevant information for policymakers in terms of the effect of lockdown measures in the different regions. The number of susceptible individuals involved in the epidemic, important for a safe release of lockdown during the next phases, is predicted to be around 10% of the population for Lombardy, 16% for Reggio Emilia, 18% for Veneto, and 40% for Valle d’Aosta. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Article
The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece
Physics 2020, 2(2), 325-339; https://0-doi-org.brum.beds.ac.uk/10.3390/physics2020017 - 22 Jun 2020
Cited by 15 | Viewed by 2271
Abstract
Within the context of Greece promising a success story in the fight against the disease, this paper proposes a novel method for studying the evolution of the Greek COVID-19 infection curve in relation to the anti-COVID-19 policies applied to control the pandemic. Based [...] Read more.
Within the context of Greece promising a success story in the fight against the disease, this paper proposes a novel method for studying the evolution of the Greek COVID-19 infection curve in relation to the anti-COVID-19 policies applied to control the pandemic. Based on the ongoing spread of COVID-19 and the insufficient data for applying classic time-series approaches, the analysis builds on the visibility graph algorithm to study the Greek COVID-19 infection curve as a complex network. By using the modularity optimization algorithm, the generated visibility graph is divided into communities defining periods of different connectivity in the time-series body. These periods reveal a sequence of different typologies in the evolution of the disease, starting with a power pattern, where a second order polynomial (U-shaped) pattern intermediates, being followed by a couple of exponential patterns, and ending up with a current logarithmic pattern revealing that the evolution of the Greek COVID-19 infection curve tends towards saturation. In terms of Gaussian modeling, this successive compression of the COVID-19 infection curve into five parts implies that the pandemic in Greece is about to reach the second (decline) half of the bell-shaped distribution. The network analysis also illustrates stability of hubs and instability of medium and low-degree nodes, implying a low probability of meeting maximum (infection) values in the future and high uncertainty in the variability of other values below the average. The overall approach contributes to the scientific research by proposing a novel method for the structural decomposition of a time-series into periods, which allows removing from the series the disconnected past-data facilitating better forecasting, and provides insights of good policy and decision-making practices and management that may help other countries improve their performance in the war against COVID-19. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Article
Covid-19 Predictions Using a Gauss Model, Based on Data from April 2
Physics 2020, 2(2), 197-212; https://0-doi-org.brum.beds.ac.uk/10.3390/physics2020013 - 05 Jun 2020
Cited by 23 | Viewed by 2396
Abstract
We study a Gauss model (GM), a map from time to the bell-shaped Gaussian function to model the deaths per day and country, as a simple, analytically tractable model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a [...] Read more.
We study a Gauss model (GM), a map from time to the bell-shaped Gaussian function to model the deaths per day and country, as a simple, analytically tractable model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e., initial exponential spread to eventual saturation, and an agent-based model, we apply the GM to existing data, as of 2 April 2020, from 25 countries during first corona pandemic wave and study the model’s predictions. We find that logarithmic daily fatalities caused by the coronavirus disease 2019 (Covid-19) are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical χ 2 -fit with 95% confidence, we are able to obtain the characteristic parameters of the GM, i.e., a width, peak height, and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Article
A Gaussian Model for the Time Development of the Sars-Cov-2 Corona Pandemic Disease. Predictions for Germany Made on 30 March 2020
Physics 2020, 2(2), 164-170; https://0-doi-org.brum.beds.ac.uk/10.3390/physics2020010 - 19 May 2020
Cited by 14 | Viewed by 1919
Abstract
For Germany, it is predicted that the first wave of the corona pandemic disease reaches its maximum of new infections on 11 April 2020 3.4 + 5.4 days with 90% confidence. With a delay of about 7 days the maximum demand on [...] Read more.
For Germany, it is predicted that the first wave of the corona pandemic disease reaches its maximum of new infections on 11 April 2020 3.4 + 5.4 days with 90% confidence. With a delay of about 7 days the maximum demand on breathing machines in hospitals occurs on 18 April 2020 3.4 + 5.4 days. The first pandemic wave ends in Germany end of May 2020. The predictions are based on the assumption of a Gaussian time evolution well justified by the central limit theorem of statistics. The width and the maximum time and thus the duration of this Gaussian distribution are determined from a statistical χ 2 -fit to the observed doubling times before 28 March 2020. Full article
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Show Figures

Figure 1

Back to TopTop