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The 2nd Edition: Big Data for Public Health Research and Practice

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 3614

Special Issue Editor

Dr. Quynh C. Nguyen

E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA
Interests: neighborhoods; health disparities; social media; Google Street View; health technology; artificial intelligence; social epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To enable decision making, we need timely data on the determinants of health and well-being. Big data can often be operational or “organic” data generated not for research purposes, including social media; news feeds; Google Street View images; online reviews; blogs; and billing, pharmacy, and laboratory data. These data are providing new ways of obtaining information on factors such as social norms, built environment features, health behaviors, and individual characteristics that can impact health.

After the success of the previous Special Issue on “Big Data for Public Health Research and Practice”, we are pleased to invite researchers to contribute to the second Special Issue. Similarly, the aim of this Special Issue is to collect a series of articles related to big data and the development of technologies and advanced computational methods that can be leveraged to conduct public health research and practice. Practical experiences and experiments are also welcome.

Some possible topics are listed below; however, other topics are also welcomed:

  • The use of electronic health records, billing data, and pharmacy data to understand individualized risk factors and treatment success;
  • The characterization of built environments with big data derived from various sources (e.g., Google Street View images and remote sensing imagery data), as well as their impacts on people’s health;
  • The use of Artificial Intelligence to build conversational or question-and-answer chatbots for personalized health information delivery;
  • Using various forms of user-generated content (e.g., GPS data, accelerometer data, users’ review data, social media data, and web search data) to study individual behaviors and social/cultural environments, as well as their impacts on people’s health;
  • The development of new methods or tools (e.g., natural language processing, machine learning, database management, high performance computing, data mining, cloud computing, computer vision, visualization, geographic information systems, and spatial analysis) for big-data-based health research;
  • The use of big data in COVID-19-related research;
  • The application or development of causal inference methods for big data;
  • Investigating and addressing data quality and uncertainty issues;
  • The blending and integration of big data from different sources.

Dr. Quynh C. Nguyen
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.

Keywords

  • big data
  • natural language processing
  • machine learning
  • deep learning
  • artificial intelligence
  • environment
  • public health
  • health interventions using technology

Related Special Issue

Published Papers (2 papers)

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Research

15 pages, 2930 KiB  
Article
Quantifying Social Interventions for Combating COVID-19 via a Symmetry-Based Model
by Lei Zhang, Guang-Hui She, Yu-Rong She, Rong Li and Zhen-Su She
Int. J. Environ. Res. Public Health 2023, 20(1), 476; https://doi.org/10.3390/ijerph20010476 - 28 Dec 2022
Viewed by 1354
Abstract
The COVID-19 pandemic has revealed new features in terms of substantial changes in rates of infection, cure, and death as a result of social interventions, which significantly challenges traditional SEIR-type models. In this paper we developed a symmetry-based model for quantifying social interventions [...] Read more.
The COVID-19 pandemic has revealed new features in terms of substantial changes in rates of infection, cure, and death as a result of social interventions, which significantly challenges traditional SEIR-type models. In this paper we developed a symmetry-based model for quantifying social interventions for combating COVID-19. We found that three key order parameters, separating degree (S) for susceptible populations, healing degree (H) for mild cases, and rescuing degree (R) for severe cases, all display logistic dynamics, establishing a novel dynamic model named SHR. Furthermore, we discovered two evolutionary patterns of healing degree with a universal power law in 23 areas in the first wave. Remarkably, the model yielded a quantitative evaluation of the dynamic back-to-zero policy in the third wave in Beijing using 12 datasets of different sizes. In conclusion, the SHR model constitutes a rational basis by which we can understand this complex epidemic and policymakers can carry out sustainable anti-epidemic measures to minimize its impact. Full article
(This article belongs to the Special Issue The 2nd Edition: Big Data for Public Health Research and Practice)
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16 pages, 791 KiB  
Article
A Multistate Study on Housing Factors Influential to Heat-Related Illness in the United States
by Ming Hu, Kai Zhang, Quynh Camthi Nguyen, Tolga Tasdizen and Krupali Uplekar Krusche
Int. J. Environ. Res. Public Health 2022, 19(23), 15762; https://doi.org/10.3390/ijerph192315762 - 26 Nov 2022
Cited by 3 | Viewed by 1670
Abstract
As climate change increases the frequency and intensity of devastating and unpredictable extreme heat events, developments to the built environment should consider instigating practices that minimize the likelihood of indoor overheating during hot weather. Heatwaves are the leading cause of death among weather-related [...] Read more.
As climate change increases the frequency and intensity of devastating and unpredictable extreme heat events, developments to the built environment should consider instigating practices that minimize the likelihood of indoor overheating during hot weather. Heatwaves are the leading cause of death among weather-related causes worldwide, including in developed and developing countries. In this empirical study, a four-step approach was used to collect, extract and analyze data from twenty-seven states in the United States. Three housing characteristic categories (i.e., general housing conditions, living conditions, and housing thermal inertia) and eight variables were extracted from the American Housing Survey database, ResStock database and CDC’s National Environmental Public Health Tracking Network. Multivariable regression models were used to understand the influential variables, a multicollinearity test was used to determine the dependence of those variables, and then a logistic model was used to verify the results. Three variables—housing age (HA), housing crowding ratio (HCR), and roof condition (RC)—were found to be correlated with the risk of heat-related illness (HRI) indexes. Then, a logistic regression model was generated using the three variables to predict the risk of heat-related emergency department visits (EDV) and heat-related mortality (MORD) on a state level. The results indicate that the proposed logistic regression model correctly predicted 100% of the high-risk states for MORD for the eight states tested. Overall, this analysis provides additional evidence about the housing character variables that influence HRI. The outcomes also reinforce the concept of the built environment determined health and demonstrate that the built environment, especially housing, should be considered in techniques for mitigating climate change-exacerbated health conditions. Full article
(This article belongs to the Special Issue The 2nd Edition: Big Data for Public Health Research and Practice)
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