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Topical Collection "Public Health Surveillance and Infectious Disease Control"

Editors

Dr. Giovanni Improta
E-Mail Website
Collection Editor
1. Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy
2. Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, 80131 Naples, Italy
Interests: public health; health technology assessment; Lean Six Sigma; bioengineering; healthcare decision making; biomedical signal processing and analysis; quality improvement in healthcare; modeling and analysis of biomedical data; machine learning and data mining for healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Emma Montella
E-Mail Website
Collection Editor
Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy
Interests: public health; preventive medicine; epidemiology
Dr. Giovanni Boccia
E-Mail Website
Collection Editor
Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, 84081 Salerno, Italy
Interests: public health; occupational medicine; health economics; preventive medicine; epidemiology; environmental health and food control
Special Issues, Collections and Topics in MDPI journals
Dr. Ilaria Loperto
E-Mail Website
Collection Editor
Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy
Interests: public health; preventive medicine; epidemiology; lean six sigma

Topical Collection Information

Dear Colleagues,

Never before has interest in infectious diseases reached such a high level as today. The particular historical period we are experiencing has placed before us the power that viruses and bacteria can have over the health of the population. According to the World Health Organization (WHO), the diseases hitherto identified as being at epidemic risk are only warning signs of a new era of potentially rapidly spreading epidemics that will put the national health systems of most of the countries of the world in serious crisis.

It is easy to understand how all this can influence not only health, but also the economy. It becomes very important to be able to identify, using the new knowledge at our disposal, tools of interest in terms of global health, specifically regarding infectious diseases. However, infections represent a problem and a considerable cost even at the lowest level, such as that of individual hospitals. Being able to manage and limit them therefore becomes of strategic interest for hospital management.

Given the complexity of the field of interest, Global Health requires a transdisciplinary and multi-methodological approach, which makes use of the contribution of many sciences including economics, engineering, and biomedicine.

We therefore welcome all articles, systematic reviews, and other original productions that address some of the core research topics related to this Topical Collection, including but not limited to:

  • Global infectious diseases;
  • Hospital infections and healthcare acquired infections;
  • Community infections;
  • Surveillance and control.

Dr. Giovanni Improta
Dr. Emma Montella
Dr. Giovanni Boccia
Dr. Ilaria Loperto
Collection 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 collection 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. International Journal of Environmental Research and Public Health 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 2500 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

  • Global Health
  • infectious diseases
  • hospital infections
  • community infections
  • surveillance and control

Published Papers (3 papers)

2022

Article
Knowledge, Attitude, and Practices towards Dengue Fever among University Students of Dhaka City, Bangladesh
Int. J. Environ. Res. Public Health 2022, 19(7), 4023; https://doi.org/10.3390/ijerph19074023 - 28 Mar 2022
Viewed by 777
Abstract
Dhaka has become the worst affected city in Bangladesh regarding dengue fever (DF). A large number of university students are residing in this city with a high DF risk. This cross-sectional study was conducted to assess the DF status and responses among these [...] Read more.
Dhaka has become the worst affected city in Bangladesh regarding dengue fever (DF). A large number of university students are residing in this city with a high DF risk. This cross-sectional study was conducted to assess the DF status and responses among these students through their Knowledge, Attitude, and Practices (KAP) survey. A total of 625 students participated in an online self-reported survey. Statistical analyses were performed to assess the status and KAP regarding DF. University students from the city perceived their living places as moderately safe (45.28%) against DF, whereas about 20% reported their DF infection history. Some of these students had exemplary DF knowledge (66.72%), attitude (89.28%), and practices (68.32%). However, many of them were also observed with a lack of knowledge about this disease’s infectious behavior, recognizing Aedes mosquito breeding sites, multiple infection cases, and the risk of DF viral infection during pregnancy. Fair correlations (p < 0.001) were determined in the KAP domain. Gender, residential unit, major, and dengue-relevant subjects were found to be significant predictors (p < 0.05) of KAP level in the univariate analysis. Major subject and residential units remained significant predictors of overall KAP level in further multiple analysis. This study revealed the urgency of infectious disease-related subjects and the relevant demonstration into the university curriculum. The study’s findings can assist the university, government and non-governmental organizations, and the health and social workers to prepare a comprehensive dengue response and preparedness plan. Full article
Article
A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection
Int. J. Environ. Res. Public Health 2022, 19(6), 3533; https://doi.org/10.3390/ijerph19063533 - 16 Mar 2022
Viewed by 500
Abstract
Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more [...] Read more.
Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision. Full article
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Figure 1

Article
Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study
Int. J. Environ. Res. Public Health 2022, 19(5), 2498; https://doi.org/10.3390/ijerph19052498 - 22 Feb 2022
Viewed by 547
Abstract
Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of [...] Read more.
Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy). Full article
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