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Microorganisms
Special Issues
Advances in Epidemiology and Modeling
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Advances in Epidemiology and Modeling

A special issue of Microorganisms (ISSN 2076-2607). This special issue belongs to the section "Public Health Microbiology".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 2146

Special Issue Editors

Prof. Dr. Jacques Demongeot

E-Mail Website
Guest Editor
Faculte de Medicine de Grenoble, Universite Grenoble Alpes, F-38700 La Tronche, France
Interests: theoretical biology; bioinformatics; biostatistics; origin of life
Special Issues, Collections and Topics in MDPI journals
Dr. Kayode Oshinubi

E-Mail Website
Assistant Guest Editor
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Interests: computational epidemiology; biostatistics; disease ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent pandemic has been a subject of debate all over the world and has affected the way of life, mobility, and socioeconomic patterns of different countries. The onus lies on us as researchers to devise modeling approaches to address the current challenges and better prepare humanity for future epidemics. This Special Issue aims to bring together articles that address how to better prepare for future epidemics by analyzing and simulating public health data which will bring about nowcasting, hindcasting or forecasting of infectious diseases dynamics so as to better guide public health experts and decision makers. We welcome scholars in relevant fields to submit original research articles and reviews. Research areas may include (but are not limited to) the following:

  • Artificial Intelligence for detection of, responses to, and mitigation of emerging epidemics;
  • Applications for optimal control of emerging infectious diseases;
  • Epidemics, ethics and uncertainty: the roles of public health experts and modelers;
  • Mobility, geographical locations and socioeconomic factors that influence the predictive modeling of epidemics;
  • The role of robust public health data in predictive modeling;
  • Predictive modeling methods and applications to epidemic data.

We look forward to receiving your contributions.

Prof. Dr. Jacques Demongeot
Dr. Kayode Oshinubi
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. Microorganisms 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

  • spatial modeling
  • disease ecology
  • metapopulation
  • predictive modeling
  • emerging epidemics
  • sociodemography
  • mechanistic model
  • public health data
  • optimal control
  • artificial intelligence

Published Papers (2 papers)

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Research

11 pages, 1934 KiB  
Article
A Predictive Model of the Start of Annual Influenza Epidemics
by Elisabet Castro Blanco, Maria Rosa Dalmau Llorca, Carina Aguilar Martín, Noèlia Carrasco-Querol, Alessandra Queiroga Gonçalves, Zojaina Hernández Rojas, Ermengol Coma and José Fernández-Sáez
Microorganisms 2024, 12(7), 1257; https://doi.org/10.3390/microorganisms12071257 - 21 Jun 2024
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Abstract
Influenza is a respiratory disease that causes annual epidemics during cold seasons. These epidemics increase pressure on healthcare systems, sometimes provoking their collapse. For this reason, a tool is needed to predict when an influenza epidemic will occur so that the healthcare system [...] Read more.
Influenza is a respiratory disease that causes annual epidemics during cold seasons. These epidemics increase pressure on healthcare systems, sometimes provoking their collapse. For this reason, a tool is needed to predict when an influenza epidemic will occur so that the healthcare system has time to prepare for it. This study therefore aims to develop a statistical model capable of predicting the onset of influenza epidemics in Catalonia, Spain. Influenza seasons from 2011 to 2017 were used for model training, and those from 2017 to 2018 were used for validation. Logistic regression, Support Vector Machine, and Random Forest models were used to predict the onset of the influenza epidemic. The logistic regression model was able to predict the start of influenza epidemics at least one week in advance, based on clinical diagnosis rates of various respiratory diseases and meteorological variables. This model achieved the best punctual estimates for two of three performance metrics. The most important variables in the model were the principal components of bronchiolitis rates and mean temperature. The onset of influenza epidemics can be predicted from clinical diagnosis rates of various respiratory diseases and meteorological variables. Future research should determine whether predictive models play a key role in preventing influenza. Full article
(This article belongs to the Special Issue Advances in Epidemiology and Modeling)
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21 pages, 6158 KiB  
Article
Do Weather Conditions Still Have an Impact on the COVID-19 Pandemic? An Observation of the Mid-2022 COVID-19 Peak in Taiwan
by Wan-Yi Lin, Hao-Hsuan Lin, Shih-An Chang, Tai-Chi Chen Wang, Juei-Chao Chen and Yu-Sheng Chen
Microorganisms 2024, 12(5), 947; https://doi.org/10.3390/microorganisms12050947 - 7 May 2024
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Abstract
Since the onset of the COVID-19 pandemic in 2019, the role of weather conditions in influencing transmission has been unclear, with results varying across different studies. Given the changes in border policies and the higher vaccination rates compared to earlier conditions, this study [...] Read more.
Since the onset of the COVID-19 pandemic in 2019, the role of weather conditions in influencing transmission has been unclear, with results varying across different studies. Given the changes in border policies and the higher vaccination rates compared to earlier conditions, this study aimed to reassess the impact of weather on COVID-19, focusing on local climate effects. We analyzed daily COVID-19 case data and weather factors such as temperature, humidity, wind speed, and a diurnal temperature range from 1 March to 15 August 2022 across six regions in Taiwan. This study found a positive correlation between maximum daily temperature and relative humidity with new COVID-19 cases, whereas wind speed and diurnal temperature range were negatively correlated. Additionally, a significant positive correlation was identified between the unease environmental condition factor (UECF, calculated as RH*Tmax/WS), the kind of Climate Factor Complex (CFC), and confirmed cases. The findings highlight the influence of local weather conditions on COVID-19 transmission, suggesting that such factors can alter environmental comfort and human behavior, thereby affecting disease spread. We also introduced the Fire-Qi Period concept to explain the cyclic climatic variations influencing infectious disease outbreaks globally. This study emphasizes the necessity of considering both local and global climatic effects on infectious diseases. Full article
(This article belongs to the Special Issue Advances in Epidemiology and Modeling)
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