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Current Advances in Machine Learning and Biostatistics Approaches in Public Health and Epidemiology

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 June 2023) | Viewed by 5952

Special Issue Editor

Dr. Daisuke Yoneoka

E-Mail Website
Guest Editor
1. Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo 160-8582, Japan
2. Department of Health Policy and Management, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
3. Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan
Interests: biostatistics; machine learning for personalized medicine; spatio temporal models for epidemiological data; statistical methods for GWAS; meta-analysis

Special Issue Information

Dear Colleagues,

Biostatistics/machine learning and big data have intersected in the field of epidemiology and public health. In the past decade, the use of biostatistical and machine learning approaches has contributed significantly to addressing global health issues and allowed considerable progress. We invite papers addressing a variety of topics (not only the use of advanced biostatistical topics, but also epidemiological and public health applications of cutting-edge methods such as deep learning) for the Special Issue of International Journal of Environmental Research and Public Health (IJERPH), especially those combining advanced biostatistics and machine learning methods with a practical focus on providing solutions to globally important issues such as the COVID-19 pandemic, noncommunicable diseases, global warming, etc. We expect that work featured in this Special Issue will differ from that in Machine Learning in Healthcare and Bioinformatics by focusing on data and algorithms related to the public health and epidemiological conditions that are strongly associated with our health, including lifestyle, policy, socioeconomic, psychiatric, behavioral, and environmental factors.

Dr. Daisuke Yoneoka
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

  • machine learning
  • biostatistics
  • computational epidemiology
  • big data
  • natural language processing
  • computer vision
  • information retrieval
  • software
  • COVID-19

Published Papers (3 papers)

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Research

10 pages, 1630 KiB  
Article
A Statistical Model of COVID-19 Infection Incidence in the Southern Indian State of Tamil Nadu
by Tanmay Devi and Kaushik Gopalan
Int. J. Environ. Res. Public Health 2022, 19(17), 11137; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191711137 - 05 Sep 2022
Cited by 1 | Viewed by 1788
Abstract
In this manuscript, we present an analysis of COVID-19 infection incidence in the Indian state of Tamil Nadu. We used seroprevalence survey data along with COVID-19 fatality reports from a six-month period (1 June 2020 to 30 November 2020) to estimate age- and [...] Read more.
In this manuscript, we present an analysis of COVID-19 infection incidence in the Indian state of Tamil Nadu. We used seroprevalence survey data along with COVID-19 fatality reports from a six-month period (1 June 2020 to 30 November 2020) to estimate age- and sex-specific COVID-19 infection fatality rates (IFR) for Tamil Nadu. We used these IFRs to estimate new infections occurring daily using the daily COVID-19 fatality reports published by the Government of Tamil Nadu. We found that these infection incidence estimates for the second COVID wave in Tamil Nadu were broadly consistent with the infection estimates from seroprevalence surveys. Further, we propose a composite statistical model that pairs a k-nearest neighbours model with a power-law characterisation for “out-of-range” extrapolation to estimate the COVID-19 infection incidence based on observed cases and test positivity ratio. We found that this model matched closely with the IFR-based infection incidence estimates for the first two COVID-19 waves for both Tamil Nadu as well as the neighbouring state of Karnataka. Finally, we used this statistical model to estimate the infection incidence during the recent “Omicron wave” in Tamil Nadu and Karnataka. Full article
(This article belongs to the Special Issue Current Advances in Machine Learning and Biostatistics Approaches in Public Health and Epidemiology)
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11 pages, 1203 KiB  
Article
Patterns of Muscle-Related Risk Factors for Sarcopenia in Older Mexican Women
by María Fernanda Carrillo-Vega, Mario Ulises Pérez-Zepeda, Guillermo Salinas-Escudero, Carmen García-Peña, Edward Daniel Reyes-Ramírez, María Claudia Espinel-Bermúdez, Sergio Sánchez-García and Lorena Parra-Rodríguez
Int. J. Environ. Res. Public Health 2022, 19(16), 10239; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191610239 - 18 Aug 2022
Cited by 1 | Viewed by 1692
Abstract
Early detriment in the muscle mass quantity, quality, and functionality, determined by calf circumference (CC), phase angle (PA), gait time (GT), and grip strength (GSt), may be considered a risk factor for sarcopenia. Patterns derived from these parameters could timely identify an early [...] Read more.
Early detriment in the muscle mass quantity, quality, and functionality, determined by calf circumference (CC), phase angle (PA), gait time (GT), and grip strength (GSt), may be considered a risk factor for sarcopenia. Patterns derived from these parameters could timely identify an early stage of this disease. Thus, the present work aims to identify those patterns of muscle-related parameters and their association with sarcopenia in a cohort of older Mexican women with neural network analysis. Methods: Information from the functional decline patterns at the end of life, related factors, and associated costs study was used. A self-organizing map was used to analyze the information. A SOM is an unsupervised machine learning technique that projects input variables on a low-dimensional hexagonal grid that can be effectively utilized to visualize and explore properties of the data allowing to cluster individuals with similar age, GT, GSt, CC, and PA. An unadjusted logistic regression model assessed the probability of having sarcopenia given a particular cluster. Results: 250 women were evaluated. Mean age was 68.54 ± 5.99, sarcopenia was present in 31 (12.4%). Clusters 1 and 2 had similar GT, GSt, and CC values. Moreover, in cluster 1, women were older with higher PA values (p < 0.001). From cluster 3 upward, there is a trend of worse scores for every variable. Moreover, 100% of the participants in cluster 6 have sarcopenia (p < 0.001). Women in clusters 4 and 5 were 19.29 and 90 respectively, times more likely to develop sarcopenia than those from cluster 2 (p < 0.01). Conclusions: The joint use of age, GSt, GT, CC, and PA is strongly associated with the probability women have of presenting sarcopenia. Full article
(This article belongs to the Special Issue Current Advances in Machine Learning and Biostatistics Approaches in Public Health and Epidemiology)
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9 pages, 849 KiB  
Article
Association between Total and Individual PCB Congener Levels in Maternal Serum and Birth Weight of Newborns: Results from the Chiba Study of Mother and Child Health Using Weighted Quantile Sum Regression
by Akifumi Eguchi, Kenichi Sakurai, Midori Yamamoto, Masahiro Watanabe, Aya Hisada, Tomoko Takahashi, Emiko Todaka and Chisato Mori
Int. J. Environ. Res. Public Health 2022, 19(2), 694; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19020694 - 08 Jan 2022
Cited by 3 | Viewed by 1444
Abstract
Maternal exposure to polychlorinated biphenyls (PCBs) during pregnancy is associated with a low birth weight; however, the congener-specific effects of PCB congeners are not well defined. In this study, we used maternal serum samples from the Chiba Study of Mother and Child Health [...] Read more.
Maternal exposure to polychlorinated biphenyls (PCBs) during pregnancy is associated with a low birth weight; however, the congener-specific effects of PCB congeners are not well defined. In this study, we used maternal serum samples from the Chiba Study of Mother and Child Health (C-MACH) cohort, collected at 32 weeks of gestational age, to analyze the effects of PCB congener exposure on birth weight by examining the relationship between newborn birth weight and individual PCB congener levels in maternal serum (n = 291). The median total PCB level in the serum of mothers of male and female newborns at approximately 32 weeks of gestation was 39 and 37 ng g−1 lipid wt, respectively. The effect of the total PCB levels and the effects of PCB congener mixtures were analyzed using a linear regression model and a generalized weighted quantile sum regression model (gWQS). The birth weight of newborns was significantly associated with maternal exposure to PCB mixtures in the gWQS model. The results suggest that exposure to PCB mixtures results in low newborn birth weight. However, specific impacts of individual PCB congeners could not be related to newborn birth weight. Full article
(This article belongs to the Special Issue Current Advances in Machine Learning and Biostatistics Approaches in Public Health and Epidemiology)
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