Advances in Infectious Disease Epidemiology

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Epidemiology".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 542

Special Issue Editor


E-Mail Website
Guest Editor
1. Internal Medicine Specialist, Hospital Universitario de Mostoles, 28935 Madrid, Spain
2. Faculty of Medicine, Universidad Francisco de Vitoria (UFV), 28935 Madrid, Spain
3. Faculty of Medicine, Universidad Rey Juan Carlos, 28922 Madrid, Spain
Interests: infectious diseases; viral diseases; point-of-care ultrasound; travel medicine; epidemiology

Special Issue Information

Dear Colleagues,

Infectious diseases are among the main causes of morbidity and mortality worldwide. Advances in medical science have resulted in improvements in the diagnosis and treatment of patients that would have been considered incurable in the past.

Currently, the emergence of multidrug-resistant bacteria is a cause for great concern. Early detection and effective therapy are the main goals to consider when treating an infected patient. Recent advances in infectious disease epidemiology have been crucial to our understanding and personalized management of infectious diseases.

Some pivotal developments include genomics, math models, machine learning and artificial intelligence, global health approaches, surveillance systems, vaccination strategies, antibiotic stewardship programs, and social science strategies.

These advances have contributed to an effective response to infectious disease outbreaks and pandemics such as COVID-19.

Given the complexity of the infectious response, it is unlikely that a single approach will be sufficient to identify the interaction of different factors that are involved in emerging infectious diseases. Better strategies are necessary to prevent or diminish the collateral effects of pandemics. Educating and training healthcare workers are effective strategies for combating the dissemination and transmission of infectious agents.

This Special Issue aims to collate evidence regarding the epidemiology of infections. Infectious diseases are a persistent challenge for both clinicians and researchers.

We hope that this Special Issue will stimulate work on improving methodologies for the detection and treatment of infections by providing comprehensive discussions so that we can achieve early diagnoses and thus reduce complications.

Dr. Cesar Henriquez-Camacho
Guest Editor

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. Journal of Personalized Medicine 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 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

  • infectious diseases
  • epidemiology
  • genomics
  • machine learning
  • artificial intelligence
  • surveillance
  • vaccination
  • global health
  • stewardship programs

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 863 KiB  
Article
Perioperative Coronavirus Disease 2019 Infection and Its Impact on Postoperative Outcomes: Pulmonary Complications and Mortality Based on Korean National Health Insurance Data
by Hyo Jin Kim, EunJin Ahn, Eun Jung Oh and Si Ra Bang
J. Pers. Med. 2025, 15(4), 157; https://doi.org/10.3390/jpm15040157 - 17 Apr 2025
Viewed by 251
Abstract
Background/Objectives: The coronavirus disease 2019 (COVID-19) pandemic significantly disrupted global healthcare. This study explores the effects of perioperative COVID-19 infection on postoperative outcomes, aiming to refine risk assessment and enhance personalized perioperative care using a comprehensive dataset from the Korean National Health [...] Read more.
Background/Objectives: The coronavirus disease 2019 (COVID-19) pandemic significantly disrupted global healthcare. This study explores the effects of perioperative COVID-19 infection on postoperative outcomes, aiming to refine risk assessment and enhance personalized perioperative care using a comprehensive dataset from the Korean National Health Insurance Service. This analysis extends previous research by providing a large-scale validation of risk factors associated with COVID-19 in a perioperative setting. Methods: In this retrospective cohort study, we analyzed data from 2,903,858 patients who underwent surgery under general anesthesia between January 2020 and December 2021. Patients were categorized into COVID-19 (+) and COVID-19 (−) groups within 30 d before or after surgery. Logistic regression models were used to identify independent risk factors for mortality and pulmonary complications. Results: After propensity score matching, the final cohort comprised 19,235 patients (COVID-19 (+): 3847; COVID-19 (−): 15,388). The COVID-19 (+) group had significantly higher overall mortality than the COVID-19 (−) group. No significant difference was observed between the groups concerning 30 d mortality. Pulmonary complications, including pneumonia and acute respiratory distress syndrome, were significantly more frequent in the COVID-19 (+) group. The independent predictors of 30 d mortality included advanced age, emergency surgery, and the American Society of Anesthesiologists physical status classification. Conclusions: Our study confirms that perioperative COVID-19 infection significantly elevates overall mortality and pulmonary complications, emphasizing the necessity of tailored perioperative management. Incorporating individual risk factors into care protocols not only reduces risks for surgical patients but also enhances treatment approaches. These findings advocate for the implementation of personalized medicine principles in surgical settings to improve patient outcomes during and after the COVID-19 pandemic. This research uses a comprehensive national medical claims dataset to set new standards for studying pandemic health impacts and improving clinical strategies. Full article
(This article belongs to the Special Issue Advances in Infectious Disease Epidemiology)
Show Figures

Figure 1

Back to TopTop