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Vaccines
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Advanced Studies in Epidemiology and Statistical Modeling of COVID-19 and...
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Advanced Studies in Epidemiology and Statistical Modeling of COVID-19 and Other Infectious Diseases

A special issue of Vaccines (ISSN 2076-393X). This special issue belongs to the section "Epidemiology".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 6032

Special Issue Editors

Dr. Pablo Caballero-Pérez

E-Mail Website
Guest Editor
Department of Community Nursing, Preventive Medicine and Public Health and History of Science, University of Alicante, 03690 Alicante, Spain
Interests: immunization; vaccinology; medical statistics
Dr. Giovanni Sebastiani

E-Mail Website
Guest Editor
1. Istituto per le Applicazioni del Calcolo Mauro Picone, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
2. Mathematics Department “Guido Castelnuovo”, Sapienza University of Rome, 00185 Rome, Italy
3. Department of Mathematics and Statistics, University of Troms∅, N-9037 Troms∅, Norway
Interests: mathematics; statistics

Special Issue Information

Dear Colleagues,

Probably, infectious diseases have always spread among living species. There are documents showing that during the 16th century it became clear that some diseases were caused by unseen transmissible agents. Besides quarantine and disinfection, inoculation (variolation) is reported to be a common practice in Asian and African medicine. The mathematician Daniel Bernoulli, a pioneer of probability and statistics, in the second half of the 18th century demonstrated the effectiveness of inoculation. At the beginning of the 19th century, Edward Jenner, the father of immunology, first introduced inoculation from cows (vacca in latin), i.e., vaccination. The first scientific publications on deterministic mathematical models to quantitatively describe the evolution of epidemics appeared in the first half of the 20th century. However, even nowadays, probabilistic models and statistics are fundamental in epidemiology. To fight against the COVID-19 pandemic and to control it, ancient and modern methods have been used, such as quarantine, face masks, disinfection, and mRNA vaccines. In addition, probabilistic models and statistical methods have been providing a significant contribution to this aim, for example to identify the factors that are mainly influencing COVID-19 diffusion, to predict the effect of measures for reducing virus diffusion, and to optimize vaccination campaigns. Beside COVID-19, it is also important to study other infectious diseases, e.g., tuberculosis, AIDS, monkeypox, etc.

This Special Issue will focus on some advanced statistical studies on COVID-19 and other infection diseases.

Dr. Pablo Caballero-Pérez
Dr. Giovanni Sebastiani
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. Vaccines 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 2700 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 methods for forecasting
  • time series analysis
  • spatial statistics
  • spatio-temporal models
  • probabilistic models for epidemics
  • statistical inference
  • statistical data mining
  • pattern recognition
  • Bayesian statistics
  • neural network inference

Published Papers (4 papers)

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Research

22 pages, 2947 KiB  
Article
Mathematical Modeling of COVID-19 Cases and Deaths and the Impact of Vaccinations during Three Years of the Pandemic in Peru
by Olegario Marín-Machuca, Ruy D. Chacón, Natalia Alvarez-Lovera, Pedro Pesantes-Grados, Luis Pérez-Timaná and Obert Marín-Sánchez
Vaccines 2023, 11(11), 1648; https://doi.org/10.3390/vaccines11111648 - 27 Oct 2023
Cited by 1 | Viewed by 1211
Abstract
The COVID-19 pandemic has caused widespread infections, deaths, and substantial economic losses. Vaccine development efforts have led to authorized candidates reducing hospitalizations and mortality, although variant emergence remains a concern. Peru faced a significant impact due to healthcare deficiencies. This study employed logistic [...] Read more.
The COVID-19 pandemic has caused widespread infections, deaths, and substantial economic losses. Vaccine development efforts have led to authorized candidates reducing hospitalizations and mortality, although variant emergence remains a concern. Peru faced a significant impact due to healthcare deficiencies. This study employed logistic regression to mathematically model COVID-19’s dynamics in Peru over three years and assessed the correlations between cases, deaths, and people vaccinated. We estimated the critical time (tc) for cases (627 days), deaths (389 days), and people vaccinated (268 days), which led to the maximum speed values on those days. Negative correlations were identified between people vaccinated and cases (−0.40) and between people vaccinated and deaths (−0.75), suggesting reciprocal relationships between those pairs of variables. In addition, Granger causality tests determined that the vaccinated population dynamics can be used to forecast the behavior of deaths (p-value < 0.05), evidencing the impact of vaccinations against COVID-19. Also, the coefficient of determination (R2) indicated a robust representation of the real data. Using the Peruvian context as an example case, the logistic model’s projections of cases, deaths, and vaccinations provide crucial insights into the pandemic, guiding public health tactics and reaffirming the essential role of vaccinations and resource distribution for an effective fight against COVID-19. Full article
(This article belongs to the Special Issue Advanced Studies in Epidemiology and Statistical Modeling of COVID-19 and Other Infectious Diseases)
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13 pages, 501 KiB  
Article
Investigating the Marginal and Herd Effects of COVID-19 Vaccination for Reducing Case Fatality Rate: Evidence from the United States between March 2021 to January 2022
by Tenglong Li, Zilong Wang, Shuyue He and Ying Chen
Vaccines 2023, 11(6), 1078; https://doi.org/10.3390/vaccines11061078 - 09 Jun 2023
Viewed by 1349
Abstract
Vaccination campaigns have been rolled out in most countries to increase vaccination coverage and protect against case mortality during the ongoing pandemic. To evaluate the effectiveness of COVID-19 vaccination, it is vital to disentangle the herd effect from the marginal effect and parameterize [...] Read more.
Vaccination campaigns have been rolled out in most countries to increase vaccination coverage and protect against case mortality during the ongoing pandemic. To evaluate the effectiveness of COVID-19 vaccination, it is vital to disentangle the herd effect from the marginal effect and parameterize them separately in a model. To demonstrate this, we study the relationship between the COVID-19 vaccination coverage and case fatality rate (CFR) based on U.S. vaccination coverage at county level, with daily records from 11 March 2021 to 26 January 2022 for 3109 U.S. counties. Using segmented regression, we discovered three breakpoints of the vaccination coverage, at which herd effects could potentially exist. Controlling for county heterogeneity, we found the size of the marginal effect was not constant but actually increased as the vaccination coverage increased, and only the herd effect at the first breakpoint to be statistically significant, which implied an indirect benefit of vaccination may exist at the early stage of a vaccination campaign. Our results demonstrated that public-health researchers should carefully differentiate and quantify the herd and marginal effects when analyzing vaccination data, to better inform vaccination-campaign strategies as well as evaluate vaccination effectiveness. Full article
(This article belongs to the Special Issue Advanced Studies in Epidemiology and Statistical Modeling of COVID-19 and Other Infectious Diseases)
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26 pages, 1051 KiB  
Article
Assessing the Impact of Vaccination on the Dynamics of COVID-19 in Africa: A Mathematical Modeling Study
by Yvette Montcho, Robinah Nalwanga, Paustella Azokpota, Jonas Têlé Doumatè, Bruno Enagnon Lokonon, Valère Kolawole Salako, Martin Wolkewitz and Romain Glèlè Kakaï
Vaccines 2023, 11(4), 857; https://doi.org/10.3390/vaccines11040857 - 17 Apr 2023
Cited by 2 | Viewed by 1632
Abstract
Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing [...] Read more.
Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing the burden of COVID-19 in eight African countries considering SARS-CoV-2 cumulative case data for each country for the third wave of the COVID-19 pandemic. The model stratifies the total population into two subgroups based on individual vaccination status. We use the detection and death rates ratios between vaccinated and unvaccinated individuals to quantify the vaccine’s effectiveness in reducing new COVID-19 infections and death, respectively. Additionally, we perform a numerical sensitivity analysis to assess the combined impact of vaccination and reduction in the SARS-CoV-2 transmission due to control measures on the control reproduction number (Rc). Our results reveal that on average, at least 60% of the population in each considered African country should be vaccinated to curtail the pandemic (lower the Rc below one). Moreover, lower values of Rc are possible even when there is a low (10%) or moderate (30%) reduction in the SARS-CoV-2 transmission rate due to NPIs. Combining vaccination programs with various levels of reduction in the transmission rate due to NPI aids in curtailing the pandemic. Additionally, this study shows that vaccination significantly reduces the severity of the disease and death rates despite low efficacy against COVID-19 infections. The African governments need to design vaccination strategies that increase vaccine uptake, such as an incentive-based approach. Full article
(This article belongs to the Special Issue Advanced Studies in Epidemiology and Statistical Modeling of COVID-19 and Other Infectious Diseases)
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21 pages, 1584 KiB  
Article
New Insights into the Estimation of Reproduction Numbers during an Epidemic
by Giovanni Sebastiani and Ilaria Spassiani
Vaccines 2022, 10(11), 1788; https://doi.org/10.3390/vaccines10111788 - 25 Oct 2022
Viewed by 1308
Abstract
In this paper, we deal with the problem of estimating the reproduction number Rt during an epidemic, as it represents one of the most used indicators to study and control this phenomenon. In particular, we focus on two issues. First, to estimate [...] Read more.
In this paper, we deal with the problem of estimating the reproduction number Rt during an epidemic, as it represents one of the most used indicators to study and control this phenomenon. In particular, we focus on two issues. First, to estimate Rt, we consider the use of positive test case data as an alternative to the first symptoms data, which are typically used. We both theoretically and empirically study the relationship between the two approaches. Second, we modify a method for estimating Rt during an epidemic that is widely used by public institutions in several countries worldwide. Our procedure is not affected by the problems deriving from the hypothesis of Rt local constancy, which is assumed in the standard approach. We illustrate the results obtained by applying the proposed methodologies to real and simulated SARS-CoV-2 datasets. In both cases, we also apply some specific methods to reduce systematic and random errors affecting the data. Our results show that the Rt during an epidemic can be estimated by using the positive test data, and that our estimator outperforms the standard estimator that makes use of the first symptoms data. It is hoped that the techniques proposed here could help in the study and control of epidemics, particularly the current SARS-CoV-2 pandemic. Full article
(This article belongs to the Special Issue Advanced Studies in Epidemiology and Statistical Modeling of COVID-19 and Other Infectious Diseases)
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