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Mathematics
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Statistical Methods for the Analysis of Infectious Diseases
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Statistical Methods for the Analysis of Infectious Diseases

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 14136

Special Issue Editors

Prof. Dr. Gianluca Baio

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Guest Editor
Department of Statistical Science, University College London, London WC1E 7HB, UK
Interests: Bayesian statistics; health economics
Prof. Dr. Marta G. Blangiardo

E-Mail Website
Guest Editor
Department of Biostatistics and Epidemiology, Imperial College London, London, UK
Interests: Bayesian statistics; epidemiology; spatio-temporal modelling
Dr. Michela Cameletti

E-Mail Website
Guest Editor
Department of Management, Economics and Quantitative Methods, University of Bergamo, Bergamo, Italy
Interests: bayesian statistics; epidemiology; spatio-temporal modelling
Dr. Virgilio Gómez Rubio

E-Mail Website
Guest Editor
Department of Mathematics, School of Industrial Engineering - Albacete, University of Castilla-La Mancha, Albacete, Spain
Interests: Bayesian statistics; joint models; spatio-temporal modelling

Special Issue Information

The COVID-19 pandemic has posed a number of new challenges for statistical methods in the analysis of public health data. Examples include modeling incomplete noisy spatiotemporal data (e.g., number of infected and deaths), methods for the analysis of existing and new treatments applied to infectious diseases, and analysis of health systems to better face the pandemic, to mention a few. The aim of this Special Issue is to contribute to the global pandemic effort by providing a framework where to present and discuss novel ideas and statistical methods for the analysis of these new challenges. 

In this context, submitted works can come from a broad range of statistical areas, such as spatiotemporal modeling of infectious diseases, health economics, efficient computational methods for real-time disease surveillance, imputation of missing data, and analysis of health data, for example. The focus of the Special Issue is primarily on the analysis of statistical problems related to infectious diseases, and of course papers about the COVID-19 pandemic will be welcome. Papers not directly related to this disease but within the areas of interest of this Special Issue will also be of interest. Software development (e.g., R packages) will also be considered, provided that they represent an important contribution to any of the topics covered in the Special Issue. 

Topics adequate for this Special Issue include but are not limited to the following:

  • Computational methods for the analysis of infectious diseases;
  • Detection of disease outbreaks;
  • Disease surveillance methods;
  • Health economics;
  • Joint modeling of survival and longitudinal data;
  • Spatiotemporal modeling for disease mapping. 

Submissions must contain relevant new research on these topics. Review papers on relevant topics will also be considered for publication. Authors are encouraged to make their submissions fully reproducible by, among others, providing the datasets and computer codes needed to reproduce all the analyses and figures in the paper. These files can be included as supplementary materials to the paper. 

The submission deadline is 31 March 2021. However, papers will go through the peer-review process as soon as they are submitted.

Prof. Dr. Gianluca Baio
Prof. Dr. Marta G. Blangiardo
Dr. Michela Cameletti
Dr. Virgilio Gómez Rubio
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. Mathematics 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 2600 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

  • Computational methods for modeling diseases
  • Disease surveillance
  • Health economics
  • Infectious diseases
  • Spatiotemporal models
  • Spatiotemporal epidemiology

Published Papers (4 papers)

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Research

23 pages, 1109 KiB  
Article
COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression
by Christian Acal, Manuel Escabias, Ana M. Aguilera and Mariano J. Valderrama
Mathematics 2021, 9(11), 1237; https://doi.org/10.3390/math9111237 - 28 May 2021
Cited by 4 | Viewed by 2584
Abstract
The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed [...] Read more.
The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed to estimate the missing values of the functional responses associated with hospitalized and intensive care curves. The estimation of the functional coefficient model in terms of principal components’ regression with the completely observed data provides a prediction equation for the imputation of the unobserved data for the response. An application with data from the first wave of COVID-19 in Spain is developed after properly homogenizing, registering and smoothing the data in a common interval so that the observed curves become comparable. Finally, Canonical Correlation Analysis is performed on the functional principal components to interpret the relationship between hospital occupancy rate and illness response variables. Full article
(This article belongs to the Special Issue Statistical Methods for the Analysis of Infectious Diseases)
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13 pages, 986 KiB  
Article
The Influence of Latent and Chronic Infection on Pathogen Persistence
by Xander O’Neill, Andy White, Damian Clancy, Francisco Ruiz-Fons and Christian Gortázar
Mathematics 2021, 9(9), 1007; https://doi.org/10.3390/math9091007 - 29 Apr 2021
Viewed by 3253
Abstract
We extend the classical compartmental frameworks for susceptible-infected-susceptible (SIS) and susceptible-infected-recovered (SIR) systems to include an exposed/latent class or a chronic class of infection. Using a suite of stochastic continuous-time Markov chain models we examine [...] Read more.
We extend the classical compartmental frameworks for susceptible-infected-susceptible (SIS) and susceptible-infected-recovered (SIR) systems to include an exposed/latent class or a chronic class of infection. Using a suite of stochastic continuous-time Markov chain models we examine the impact of latent and chronic infection on the mean time to extinction of the infection. Our findings indicate that the mean time to pathogen extinction is increased for infectious diseases which cause exposed/latent infection prior to full infection and that the extinction time is increased further if these exposed individuals are also capable of transmitting the infection. A chronic infection stage can decrease or increase the mean time to pathogen extinction and in particular this depends on whether chronically infected individuals incur disease-induced mortality and whether they are able to transmit the infection. We relate our findings to specific infectious diseases that exhibit latent and chronic infectious stages and argue that infectious diseases with these characteristics may be more difficult to manage and control. Full article
(This article belongs to the Special Issue Statistical Methods for the Analysis of Infectious Diseases)
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13 pages, 1450 KiB  
Article
Modeling the COVID-19 Pandemic Dynamics in Egypt and Saudi Arabia
by Mahmoud M. Mansour, Mohammed A. Farsi, Salah M. Mohamed and Enayat M. Abd Elrazik
Mathematics 2021, 9(8), 827; https://doi.org/10.3390/math9080827 - 10 Apr 2021
Cited by 8 | Viewed by 2737
Abstract
During the abrupt outbreak of the COVID-19 pandemic, the public health system of most of the world’s nations has been tested. However, it is the concern of governments and other responsible entities to provide the correct statistics and figures to take any practicable [...] Read more.
During the abrupt outbreak of the COVID-19 pandemic, the public health system of most of the world’s nations has been tested. However, it is the concern of governments and other responsible entities to provide the correct statistics and figures to take any practicable necessary steps such as allocation of the requisite quarantine operations, calculation of the needed number of places in hospitals, determination of the extent of personal security, and determining the degree of isolation of infectious people, among others. Where the statistical literature supposes that a model governs every real phenomenon, once we know the model, we can evaluate the dilemma. Therefore, in this article, we compare the COVID-19 pandemic dynamics of two neighboring Arabic countries, Egypt and Saudi Arabia, to provide a framework to arrange appropriate quarantine activities. A new generalized family of distributions is developed to provide the best description of COVID-19 daily cases and data on daily deaths in Egypt and Saudi Arabia. Some of the mathematical properties of the proposed family are studied. Full article
(This article belongs to the Special Issue Statistical Methods for the Analysis of Infectious Diseases)
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8 pages, 1224 KiB  
Communication
Modeling the Coronavirus Disease 2019 Incubation Period: Impact on Quarantine Policy
by Daewoo Pak, Klaus Langohr, Jing Ning, Jordi Cortés Martínez, Guadalupe Gómez Melis and Yu Shen
Mathematics 2020, 8(9), 1631; https://doi.org/10.3390/math8091631 - 21 Sep 2020
Cited by 10 | Viewed by 3789
Abstract
The incubation period of coronavirus disease 2019 (COVID-19) is not always observed exactly due to uncertain onset times of infection and disease symptoms. In this paper, we demonstrate how to estimate the distribution of incubation and its association with patient demographic factors when [...] Read more.
The incubation period of coronavirus disease 2019 (COVID-19) is not always observed exactly due to uncertain onset times of infection and disease symptoms. In this paper, we demonstrate how to estimate the distribution of incubation and its association with patient demographic factors when the exact dates of infection and symptoms’ onset may not be observed. The findings from analysis of the confirmed COVID-19 cases indicate that age could be associated with the incubation period, and an age-specific quarantine policy might be more efficient than a unified one in confining COVID-19. Full article
(This article belongs to the Special Issue Statistical Methods for the Analysis of Infectious Diseases)
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