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Processes
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Analysis, Control and Modeling of Big Data for COVID-19 and...
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Analysis, Control and Modeling of Big Data for COVID-19 and Other Pandemics

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 5299

Special Issue Editor

Dr. Zuyi (Jacky) Huang

E-Mail Website
Guest Editor
Department of Chemical Engineering, Villanova University, Villanova, PA 19085, USA
Interests: systems biology; immune system modeling; microbial biofilms; drug resistance

Special Issue Information

Dear Colleagues,

Epidemic diseases have negatively impacted human life and health. For example, the COVID-19 pandemic has causes millions of deaths in the last two years. It is important to learn from the past or ongoing epidemics to develop preventive strategies or treatment approaches for future potential epidemics. Fortunately, historical data for certain epidemics have been recorded via surveillance systems. For example, the experimental Household Pulse Survey was designed by the Census Bureau in the United States to quickly and efficiently deploy data collected on how people’s lives have been impacted by the coronavirus pandemic. In this Special Issue “Analysis, Control and Modeling of Big Data for COVID-19 and Other Pandemics”, we solicit contributions that present the analysis or modeling of historical data for most recent epidemics which advance our understanding of epidemics in the following areas, including, but not limited to, epidemic modeling and prediction, analysis of surveillance data, epidemic evolution, spatial-temporal patterns of epidemic, and visualization of epidemic data. Applications of advanced machine learning techniques to big data for epidemics are especially encouraged. Example topics include:

  • Develop models from historical outbreak data to predict the trend of an epidemic;
  • Analyze surveillance data for an epidemic to identify effective approaches in responding to an epidemic;
  • Identify the evolution trend of an epidemic from the mutation in virus DNA sequences;
  • Study the spatial-temporal pattern of epidemic cases;
  • Visualize the large amount of spatial-temporal epidemic data.

Dr. Zuyi (Jacky) Huang
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. Processes 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 2400 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
  • mathematical modeling
  • data analysis
  • epidemic
  • surveillance data

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Published Papers (2 papers)

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Research

20 pages, 2290 KiB  
Article
Modelling and Analysis of Hospital Inventory Policies during COVID-19 Pandemic
by Ateekh Ur Rehman, Syed Hammad Mian, Yusuf Siraj Usmani, Mustufa Haider Abidi and Muneer Khan Mohammed
Processes 2023, 11(4), 1062; https://doi.org/10.3390/pr11041062 - 1 Apr 2023
Cited by 2 | Viewed by 2993
Abstract
The global coronavirus pandemic (COVID-19) started in 2020 and is still ongoing today. Among the numerous insights the community has learned from the COVID-19 pandemic is the value of robust healthcare inventory management. The main cause of many casualties around the world is [...] Read more.
The global coronavirus pandemic (COVID-19) started in 2020 and is still ongoing today. Among the numerous insights the community has learned from the COVID-19 pandemic is the value of robust healthcare inventory management. The main cause of many casualties around the world is the lack of medical resources for those who need them. To inhibit the spread of COVID-19, it is therefore imperative to simulate the demand for desirable medical goods at the proper time. The estimation of the incidence of infections using the right epidemiological criteria has a significant impact on the number of medical supplies required. Modeling susceptibility, exposure, infection, hospitalization, isolation, and recovery in relation to the COVID-19 pandemic is indeed crucial for the management of healthcare inventories. The goal of this research is to examine the various inventory policies such as reorder point, periodic order, and just-in-time in order to minimize the inventory management cost for medical commodities. To accomplish this, a SEIHIsRS model has been employed to comprehend the dynamics of COVID-19 and determine the hospitalized percentage of infected people. Based on this information, various situations are developed, considering the lockdown, social awareness, etc., and an appropriate inventory policy is recommended to reduce inventory management costs. It is observed that the just-in-time inventory policy is found to be the most cost-effective when there is no lockdown or only a partial lockdown. When there is a complete lockdown, the periodic order policy is the best inventory policy. The periodic order and reorder policies are cost-effective strategies to apply when social awareness is high. It has also been noticed that periodic order and reorder policies are the best inventory strategies for uncertain vaccination efficacy. This effort will assist in developing the best healthcare inventory management strategies to ensure that the right healthcare requirements are available at a minimal cost. Full article
(This article belongs to the Special Issue Analysis, Control and Modeling of Big Data for COVID-19 and Other Pandemics)
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19 pages, 4025 KiB  
Article
A Systematic Investigation of American Vaccination Preference via Historical Data
by Jason Chen, Angie Chen, Youran Shi, Kathryn Chen, Kevin Han Zhao, Morwen Xu, Ricky He and Zuyi Huang
Processes 2022, 10(8), 1665; https://doi.org/10.3390/pr10081665 - 22 Aug 2022
Cited by 2 | Viewed by 1795
Abstract
While COVID-19 vaccines are generally available, not all people receive vaccines. To reach herd immunity, most of a population must be vaccinated. It is, thus, important to identify factors influencing people’s vaccination preferences, as knowledge of these preferences allows for governments and health [...] Read more.
While COVID-19 vaccines are generally available, not all people receive vaccines. To reach herd immunity, most of a population must be vaccinated. It is, thus, important to identify factors influencing people’s vaccination preferences, as knowledge of these preferences allows for governments and health programs to increase their vaccine coverage more effectively. Fortunately, vaccination data were collected by U.S. Census Bureau in partnership with the CDC via the Household Pulse Survey (HPS) for Americans. This study presents the first analysis of the 24 vaccination datasets collected by the HPS from January 2021 to May 2022 for 250 million respondents of different ages, genders, sexual orientations, races, education statuses, marital statuses, household sizes, household income levels, and resources used for spending needs, and with different reasons for not receiving or planning to receive a vaccine. Statistical analysis techniques, including an analysis of variance (ANOVA), Tukey multiple comparisons test, and hierarchical clustering (HC), were implemented to analyze the HPS vaccination data in the R language. It was found that sexual orientation, gender, age, and education had statistically significant influences on the vaccination rates. In particular, the gay/lesbian group showed a higher vaccination rate than the straight group; the transgender group had a lower vaccination rate than either the female or the male groups; older respondents showed greater preference for vaccination; respondents with higher education levels also preferred vaccination. As for the other factors that were not significant enough to influence vaccinations in the ANOVA, notable trends were found. Asian Americans had higher vaccination rates than other races; respondents from larger household sizes had a lower chance of getting vaccinated; the unmarried group showed the lowed vaccination rate in the marital category; the respondents depending on borrowed money from the Supplemental Nutrition Assistance Program (SNAP) showed a lower vaccination rate than people with regular incomes. Concerns regarding the side-effects and the safety of the vaccines were the two major reasons for vaccination hesitance at the beginning of the pandemic, while having no trust in the vaccines and no trust in the government became more common in the later stage of the pandemic. The findings in this study can be used by governments or organizations to improve their vaccination campaigns or methods of combating future pandemics. Full article
(This article belongs to the Special Issue Analysis, Control and Modeling of Big Data for COVID-19 and Other Pandemics)
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