ijerph-logo

Journal Browser

Journal Browser

Environmental and Public Health Informatics: Managing Hazards and Risk in the Information Era 2.0

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Air".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 2531

Special Issue Editor


E-Mail Website
Guest Editor
Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Environmental and public health informatics is a growing interdisciplinary field at the intersection of human health, information science and technology, and social and behavioral science that focuses on data collection, sharing, and analysis. Data are transformed into valuable environmental and public health information in order to inform and develop strategic plans, policies, and programs.

The coronavirus pandemic has raised global awareness about the importance of health data and informatics. The volume of data used by health and environmental professionals has increased exponentially, including data about human individuals as well as aggregate data about communities. This Special Issue examines the use of environmental and public health informatics to shed light on opportunities and challenges related to the health of our communities.

This Special Issue seeks to enhance the integration of environment and humans to help to develop management solutions that can reduce environmental risk and promote human health. We are particularly interested in interdisciplinary collaboration that promotes the theoretical and/or applied aspects of environmental information sciences, regardless of disciplinary boundaries. Innovations in public health data analytics and bioinformatics are critically important to take full advantage of big data and to promote evidence-based public health since public health issues are becoming more urgent and complex. There are large amounts of electronic data dealing with environmental hazards, pollution, public health, and other fields. The topics addressed include:

  * Decision and risk analysis for human health
  * human hazards
  * Mathematical methods for understanding hazards and public health
  * Biostatistics and its applications
  * Environmental data, systems modeling, and optimization
  * Control of waste treatment and pollution reduction processes
  * Environmental systems science, hazards, and threat analyses
  * Environmental, ecological, and resource management and planning
  * Monitoring and analytical techniques of environmental quality
  * Artificial intelligence and expert systems for understanding
    environmental hazards
  * Other areas of information technology

In summary, this Special Issue examines mathematical and statistical approaches to further the field of environmental and human informatics in the era of big and open data. As this is an interdisciplinary field of science, we encourage environmental and bioinformatics papers that combine computer science, statistics, mathematics, and engineering to analyze and interpret biological data.

Prof. Dr. Jason Levy
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. International Journal of Environmental Research and Public Health 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 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.

Published Papers (1 paper)

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

Research

20 pages, 3573 KiB  
Article
The Prediction of Public Risk Perception by Internal Characteristics and External Environment: Machine Learning on Big Data
by Qihui Xie and Yanan Xue
Int. J. Environ. Res. Public Health 2022, 19(15), 9545; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19159545 - 03 Aug 2022
Cited by 3 | Viewed by 1840
Abstract
Presently, the public’s perception of risk in terms of topical social issues is mainly measured quantitively using a psychological scale, but this approach is not accurate enough for everyday data. In this paper, we explored the ways in which public risk perception can [...] Read more.
Presently, the public’s perception of risk in terms of topical social issues is mainly measured quantitively using a psychological scale, but this approach is not accurate enough for everyday data. In this paper, we explored the ways in which public risk perception can be more accurately predicted in the era of big data. We obtained internal characteristics and external environment predictor variables through a literature review, and then built our prediction model using the machine learning of a BP neural network via three steps: the calculation of the node number of the implication level, a performance test of the BP neural network, and the computation of the weight of every input node. Taking the public risk perception of the Sino–US trade friction and the COVID-19 pandemic in China as research cases, we found that, according to our tests, the node number of the implication level was 15 in terms of the Sino–US trade friction and 14 in terms of the COVID-19 pandemic. Following this, machine learning was conducted, through which we found that the R2 of the BP neural network prediction model was 0.88651 and 0.87125, respectively, for the two cases, which accurately predicted the public’s risk perception of the data on a certain day, and simultaneously obtained the weight of every predictor variable in each case. In this paper, we provide comments and suggestions for building a model to predict the public’s perception of topical issues. Full article
Show Figures

Figure 1

Back to TopTop