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Analysis of Modeling and Statistics for COVID-19
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Analysis of Modeling and Statistics for COVID-19

A special issue of COVID (ISSN 2673-8112).

Deadline for manuscript submissions: 30 June 2024 | Viewed by 8563

Special Issue Editors

Prof. Dr. Martin Kröger

E-Mail Website
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, Collections and Topics in MDPI journals
Prof. Dr. Reinhard Schlickeiser

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Guest Editor
Theoretical Physics Institute, Ruhr University Bochum, 44780 Bochum, Germany
Interests: astrophysics; space physics; cosmic rays; plasma physics; astroparticle physics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pierre Magal

E-Mail Website
Guest Editor
Institut de Mathématiques de Bordeaux, Université de Bordeaux, 351 cours de la libération, 33400 Talence, France
Interests: disease mathematical modeling; computational epidemiology; data-based epidemiological modeling; population dynamics; differential equations; dynamical systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jacques Demongeot

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Guest Editor
Faculty of Medicine, University Grenoble Alpes, Saint Martin d'Heres, France
Interests: theoretical biology; biostatistics; medical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modeling the COVID-19 outbreak requires considering a large amount of data at the world level concerning the reported number of new cases, deaths, and people vaccinated, by region and by age group or other groups. 

These data must first be analyzed with statistical methods (time series analysis, principal component analysis, technical classification, etc.), then be used to build refutable models of different types, deterministic (differentiable or discrete) or stochastic. 

The consideration of the spatial dimension can lead to diffusion models, that of the age of the patients to population dynamics models and that of the advance in the dynamics of the infection to variable reproduction number models (due to characteristics of contagiousness, virulence, and susceptibility in the host and the virus changing over time caused by viral mutations, environmental changes, host immunity, public health policy, etc.). 

A combination of these three types of models is also possible, with the additional consideration of stochastic variability on the observed data and the parameters introduced into the models. All articles dealing with statistical and dynamic aspects of COVID-19 disease, allowing its statistical description, the study of its mechanisms, and the forecasting of its evolution will be considered in the Special Issue “Analysis of Modeling and Statistics for COVID-19”.

Prof. Dr. Martin Kröger
Prof. Dr. Reinhard Schlickeiser
Prof. Dr. Pierre Magal
Prof. Dr. Jacques Demongeot
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 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. COVID is an international peer-reviewed open access monthly 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 1000 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

  • COVID-19 statistics
  • epidemiological modeling
  • time series analysis
  • prediction techniques
  • outbreak spatial diffusion
  • daily reproduction number
  • contagion modeling
  • viral mutation modeling
  • virulence mechanisms
  • host immunity modeling
  • mitigation measures dynamics
  • vaccination policy

Published Papers (6 papers)

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Research

11 pages, 2164 KiB  
Article
Mutation Trajectory of Omicron SARS-CoV-2 Virus, Measured by Principal Component Analysis
by Tomokazu Konishi and Toa Takahashi
COVID 2024, 4(4), 571-581; https://0-doi-org.brum.beds.ac.uk/10.3390/covid4040038 - 22 Apr 2024
Viewed by 448
Abstract
Since 2019, the SARS-CoV-2 virus has caused a global pandemic, resulting in widespread infections and ongoing mutations. Analyzing these mutations is essential for predicting future impacts. Unlike influenza mutations, SARS-CoV-2 mutations displayed distinct selective patterns that were concentrated in the spike protein and [...] Read more.
Since 2019, the SARS-CoV-2 virus has caused a global pandemic, resulting in widespread infections and ongoing mutations. Analyzing these mutations is essential for predicting future impacts. Unlike influenza mutations, SARS-CoV-2 mutations displayed distinct selective patterns that were concentrated in the spike protein and small ORFs. In contrast to the gradual accumulation seen in influenza mutations, SARS-CoV-2 mutations lead to the abrupt emergence of new variants and subsequent outbreaks. This phenomenon may be attributed to their targeted cellular substances; unlike the influenza virus, which has mutated to evade acquired immunity, SARS-CoV-2 appeared to mutate to target individuals who have not been previously infected. The Omicron variant, which emerged in late 2021, demonstrates significant mutations that set it apart from previous variants. The rapid mutation rate of SARS-CoV-2 has now reached a level comparable to 30 years of influenza variation. The most recent variant, JN.1, exhibits a discernible trajectory of change distinct from previous Omicron variants. Full article
(This article belongs to the Special Issue Analysis of Modeling and Statistics for COVID-19)
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21 pages, 2822 KiB  
Article
A Comparative Analysis of COVID-19 Response Measures and Their Impact on Mortality Rate
by Tomokazu Konishi
COVID 2024, 4(2), 130-150; https://0-doi-org.brum.beds.ac.uk/10.3390/covid4020012 - 24 Jan 2024
Viewed by 1356
Abstract
(1) Background: The coronavirus disease 2019 (COVID-19) pandemic significantly affected the population worldwide, with varying responses implemented to control its spread. This study aimed to compare the epidemic data compiled by the World Health Organization (WHO) to understand the impact of the measures [...] Read more.
(1) Background: The coronavirus disease 2019 (COVID-19) pandemic significantly affected the population worldwide, with varying responses implemented to control its spread. This study aimed to compare the epidemic data compiled by the World Health Organization (WHO) to understand the impact of the measures adopted by each country on the mortality rate. (2) Methods: The increase or decrease in the number of confirmed cases was understood in logarithmic terms, for which logarithmic growth rates “K” were used. The mortality rate was calculated as the percentage of deaths from the confirmed cases, which was also used for logarithmic comparison. (3) Results: Countries that effectively detected and isolated patients had a mortality rate 10 times lower than those who did not. Although strict lockdowns were once effective, they could not be implemented on an ongoing basis. After their cancellation, large outbreaks occurred because of medical breakdowns. The virus variants mutated with increased infectivity, which impeded the measures that were once effective, including vaccinations. Although the designs of mRNA vaccines were renewed, they could not keep up with the virus mutation rate. The only effective defence lies in steadily identifying and isolating patients. (4) Conclusions: these findings have crucial implications for the complete containment of the pandemic and future pandemic preparedness. Full article
(This article belongs to the Special Issue Analysis of Modeling and Statistics for COVID-19)
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19 pages, 8851 KiB  
Article
Public Decision Policy for Controlling COVID-19 Outbreaks Using Control System Engineering
by H. Daniel Patiño, Julián Pucheta, Cristian Rodríguez Rivero and Santiago Tosetti
COVID 2024, 4(1), 44-62; https://0-doi-org.brum.beds.ac.uk/10.3390/covid4010005 - 08 Jan 2024
Viewed by 1028
Abstract
This work is a response to the appeal of various international health organizations and the Automatic Control Community for collaboration in addressing Coronavirus/COVID-19 challenges during the initial stages of the pandemic. Specifically, this study presents scientific evidence supporting the efficacy of three primary [...] Read more.
This work is a response to the appeal of various international health organizations and the Automatic Control Community for collaboration in addressing Coronavirus/COVID-19 challenges during the initial stages of the pandemic. Specifically, this study presents scientific evidence supporting the efficacy of three primary non-pharmacological strategies for pandemic mitigation. We propose a control system to aid in formulating a public decision policy aimed at managing the spread of COVID-19 caused by the SARS-CoV-2 virus, commonly known as coronavirus. The primary objective is to prevent overwhelming healthcare systems by averting the saturation of intensive care units (ICUs). In the context of COVID-19, understanding the peak infection rate and its time delay is crucial for preparing healthcare infrastructure and ensuring an adequate supply of intensive care units equipped with automatic ventilators. While it is widely recognized that public policies encompassing confinement and social distancing can flatten the epidemiological curve and provide time to bolster healthcare resources, there is a dearth of studies examining this pivotal issue from the perspective of control system theory. In this study, we introduce a control system founded on three prevailing non-pharmacological tools for epidemic and pandemic mitigation: social distancing, confinement, and population-wide testing and isolation in regions experiencing community transmission. Our analysis and control system design rely on the susceptible-exposed–infected–recovered–deceased (SEIRD) mathematical model, which describes the temporal dynamics of a pandemic, tailored in this research to account for the temporal and spatial characteristics of SARS-CoV-2 behavior. This model incorporates the influence of conducting tests with subsequent population isolation. An On–off control strategy is analyzed, and a proportional–integral–derivative (PID) controller is proposed to generate a sequence of public policy decisions. The proposed control system employs the required number of critical beds and ICUs as feedback signals and compares these with the available bed capacity to generate an error signal, which is utilized as input for the PID controller. The control actions outlined involve five phases of “Social Distancing and Confinement” (SD&C) to be implemented by governmental authorities. Consequently, the control system generates a policy sequence for SD&C, with applications occurring on a weekly or biweekly basis. The simulation results underscore the favorable impact of these three mitigation strategies against the coronavirus, illustrating their efficacy in controlling the outbreak and thereby mitigating the risk of healthcare system collapse. Full article
(This article belongs to the Special Issue Analysis of Modeling and Statistics for COVID-19)
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20 pages, 912 KiB  
Article
Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model
by Nicollas S. S. da Costa, Maria do Carmo S. de Lima and Gauss M. Cordeiro
COVID 2023, 3(12), 1761-1780; https://0-doi-org.brum.beds.ac.uk/10.3390/covid3120122 - 04 Dec 2023
Viewed by 844
Abstract
This work aims to study the factors that explain the COVID-19 vaccination rate through a generalized odd log-logistic Lindley regression model with a shape systematic component. To accomplish this, a dataset of the vaccination rate of 254 counties in the state of Texas, [...] Read more.
This work aims to study the factors that explain the COVID-19 vaccination rate through a generalized odd log-logistic Lindley regression model with a shape systematic component. To accomplish this, a dataset of the vaccination rate of 254 counties in the state of Texas, US, was used, and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The mathematical properties investigated provide important information about the characteristics of the distribution. Diagnostic analysis and deviance residuals are addressed to examine the fit of the model. The proposed model shows effectiveness in identifying the key variables of COVID-19 vaccination rates at the county level, which can contribute to improving vaccination campaigns. Moreover, the findings corroborate with prior studies, and the new distribution is a suitable alternative model for future works on different datasets. Full article
(This article belongs to the Special Issue Analysis of Modeling and Statistics for COVID-19)
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19 pages, 3207 KiB  
Article
Assessing COVID-19 Effects on Inflation, Unemployment, and GDP in Africa: What Do the Data Show via GIS and Spatial Statistics?
by Butte Gotu and Habte Tadesse
COVID 2023, 3(7), 956-974; https://0-doi-org.brum.beds.ac.uk/10.3390/covid3070069 - 28 Jun 2023
Viewed by 2436
Abstract
What are the effects of Corona Virus Disease 19 (COVID-19) on inflation, unemployment, and GDP in Africa? Using geo-coded cross-sectional data taken from the World Health Organization and International Monetary Fund, we investigate the spatial distribution of COVID-19 and its effects on inflation, [...] Read more.
What are the effects of Corona Virus Disease 19 (COVID-19) on inflation, unemployment, and GDP in Africa? Using geo-coded cross-sectional data taken from the World Health Organization and International Monetary Fund, we investigate the spatial distribution of COVID-19 and its effects on inflation, unemployment, and Gross Domestic Product (GDP) in Africa by employing the Geographic Information System (GIS), multivariate analysis of covariance (MANCOVA), and spatial statistics. The entire dataset was analyzed using Stata, ArcGIS, and R software. The result shows (1) that there is evidence of a spatial pattern of COVID-19 cases and death rate clustering behavior in Africa, verifying the existence of spatial autocorrelation. The result also reveals (2) that COVID-19 has a negative effect on unemployment, inflation, and GDP in Africa. We confirmed that (3) temperature, rainfall, and humidity were statistically significantly associated with the spread of the COVID-19 pandemic in Africa. The comparison of the GDP of African countries before and after the pandemic shows (4) a large decrease in GDP, the highest in Seychelles (23 percent). The result of the study shows (5) that there has been a significant increase in inflation and unemployment rates in all countries since the outbreak of the pandemic as compared to the time before the outbreak. There is also evidence that (6) there is a significant relationship between death rate due to COVID-19 and population density; temperature with COVID-19 cases and death rate; and precipitation with death rate due to COVID-19. Therefore, respective governments and the international community need to pay attention to controlling/reducing the impact of COVID-19 on inflation, unemployment, and GDP, focusing on the indicated demographic and environmental variables. Full article
(This article belongs to the Special Issue Analysis of Modeling and Statistics for COVID-19)
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9 pages, 1399 KiB  
Communication
Key Epidemic Parameters of the SIRV Model Determined from Past COVID-19 Mutant Waves
by Reinhard Schlickeiser and Martin Kröger
COVID 2023, 3(4), 592-600; https://0-doi-org.brum.beds.ac.uk/10.3390/covid3040042 - 13 Apr 2023
Cited by 1 | Viewed by 1509
Abstract
Monitored infection and vaccination rates during past past waves of the coronavirus are used to infer a posteriori two-key parameter of the SIRV epidemic model, namely, the real-time variation in (i) the ratio of recovery to infection rate and (ii) the ratio of [...] Read more.
Monitored infection and vaccination rates during past past waves of the coronavirus are used to infer a posteriori two-key parameter of the SIRV epidemic model, namely, the real-time variation in (i) the ratio of recovery to infection rate and (ii) the ratio of vaccination to infection rate. We demonstrate that using the classical SIR model, the ratio between recovery and infection rates tends to overestimate the true ratio, which is of relevance in predicting the dynamics of an epidemic in the presence of vaccinations. Full article
(This article belongs to the Special Issue Analysis of Modeling and Statistics for COVID-19)
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