COVID

Research

22 pages, 3485 KiB

Open AccessArticle

Comparing the Change in R₀ for the COVID-19 Pandemic in Eight Countries Using an SIR Model for Specific Periods

by Tak Ching Leung

COVID 2024, 4(7), 930-951; https://doi.org/10.3390/covid4070065 - 1 Jul 2024

Viewed by 2060

Abstract

The reproduction number,

R_{0}

, is an important parameter in epidemic models. It is interpreted as the average number of new cases resulted from each infected individual during the course of infection. In this paper, the

R_{0}

estimates since the outbreak [...] Read more.

The reproduction number,

R_{0}

, is an important parameter in epidemic models. It is interpreted as the average number of new cases resulted from each infected individual during the course of infection. In this paper, the

R_{0}

estimates since the outbreak of COVID-19 till 10 August 2020 for eight countries were computed using the package R{eSIR}. The computed values were examined and compared with the daily

R_{0}

estimates obtained by a static SIR model by aligning the days of infection, assuming a fixed number of days for the infected person to become confirmed/recover/die. The results showed that running R{eSIR} to obtain

R_{0}

estimates provided an easy mean of exploring epidemic data. Care must be taken in the interpretation of

R_{0}

as a measure of severity of the spread of an epidemic. Other factors, such as imported cases, need to be considered. Full article

(This article belongs to the Special Issue Analysis of Modeling and Statistics for COVID-19)

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32 pages, 866 KiB

Open AccessArticle

Assessing the Impact of COVID-19 on Subjective Well-Being and Quality of Life in Mexico: Insights from Structural Equation Modeling

by Ignacio Alejandro Mendoza-Martínez, Edmundo Marroquín-Tovar, Jorge Pablo Rivas-Díaz, Araceli Durand, Gustavo Enrique Sauri-Alpuche and Blanca Rosa Garcia-Rivera

COVID 2024, 4(5), 605-636; https://doi.org/10.3390/covid4050041 - 13 May 2024

Cited by 1 | Viewed by 1230

Abstract

Amid the backdrop of the COVID-19 pandemic, the living conditions of the population were dramatically altered, with social distancing measures and the looming threat to public health leaving a profound impact on people’s lives. This study aims to assess the influence of COVID-19 [...] Read more.

Amid the backdrop of the COVID-19 pandemic, the living conditions of the population were dramatically altered, with social distancing measures and the looming threat to public health leaving a profound impact on people’s lives. This study aims to assess the influence of COVID-19 on subjective well-being and overall quality of life in Mexico. A structural model with latent variables was used. Data were extracted from the National Self-Reported Well-being Survey (SWLS) from October 2020 and January 2021, featuring a robust sample size of 3615 residents from urban areas in Mexico, all aged 18 and above. Findings revealed that around 38% of the variance in overall life satisfaction in October 2020 and January 2021 could be attributed to Personal well-being (0.231), Personal satisfaction (0.320), Satisfaction with the environment (0.076), and Negative emotional states (−0.116). In comparison, October 2019 to January 2020 saw a lower 20% explained variance, primarily associated with Personal well-being (0.184), Personal satisfaction (0.270), and Satisfaction with the environment (0.052). Reliability assessments, including Cronbach’s Alpha coefficients, Rho_a, and Composite Reliability, all surpassed 0.70 for each subscale. In addition, our study confirmed convergent validity, as the Average Variance Extracted (AVE) consistently exceeded 0.50 across all subscales, while the discriminant coefficient exceeded 0.70. Full article

(This article belongs to the Special Issue Analysis of Modeling and Statistics for COVID-19)

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11 pages, 2164 KiB

Open AccessArticle

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://doi.org/10.3390/covid4040038 - 22 Apr 2024

Cited by 1 | Viewed by 1611

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

Open AccessArticle

A Comparative Analysis of COVID-19 Response Measures and Their Impact on Mortality Rate

by Tomokazu Konishi

COVID 2024, 4(2), 130-150; https://doi.org/10.3390/covid4020012 - 24 Jan 2024

Cited by 3 | Viewed by 3628

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

Open AccessArticle

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://doi.org/10.3390/covid4010005 - 8 Jan 2024

Viewed by 1803

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

Open AccessArticle

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://doi.org/10.3390/covid3120122 - 4 Dec 2023

Cited by 1 | Viewed by 1531

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

Open AccessArticle

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://doi.org/10.3390/covid3070069 - 28 Jun 2023

Cited by 2 | Viewed by 3888

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

Open AccessCommunication

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://doi.org/10.3390/covid3040042 - 13 Apr 2023

Cited by 1 | Viewed by 2672

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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