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Healthcare
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Healthcare Resource Management in Large-Scale Epidemics
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Healthcare Resource Management in Large-Scale Epidemics

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: closed (1 January 2021) | Viewed by 19768

Special Issue Editor

Prof. Dr. Yu-Jun Zheng

E-Mail Website
Guest Editor
School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Interests: artificial intelligence; operations research; public health; UAV search and rescue
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

A novel coronavirus (2019-nCoV)-infected pneumonia has been detected in over forty thousand patients and has claimed over nine hundred lives as of February 10, 2020. Over thirty thousand patients and 871 deaths were reported in Hubei Province, China. According to local health authorities, such high morbidity and mortality are mainly due to the shortage of healthcare resources. In this century, we have also witnessed outbreaks of SARS, Ebola, Chikungunya, and Zika, all causing significant damage to society. These large-scale epidemic outbreaks present a challenging problem: how do we efficiently plan and use healthcare resources? The purpose of this Special Issue of Healthcare is to initiate a dialogue on models, methods, and applications related to emergency healthcare resource management in large-scale epidemics. In particular, we welcome studies at the intersection of multiple disciplines, including public health, epidemiology, computer science, and operations research.

Prof. Yu-Jun Zheng
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. Healthcare 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 2700 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

  • epidemics
  • healthcare resource management
  • disease control
  • public health

Published Papers (5 papers)

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Research

14 pages, 4569 KiB  
Article
Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic
by Abdullah Bin Shams, Ehsanul Hoque Apu, Ashiqur Rahman, Md. Mohsin Sarker Raihan, Nazeeba Siddika, Rahat Bin Preo, Molla Rashied Hussein, Shabnam Mostari and Russell Kabir
Healthcare 2021, 9(2), 156; https://doi.org/10.3390/healthcare9020156 - 03 Feb 2021
Cited by 33 | Viewed by 8128
Abstract
Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities [...] Read more.
Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues. Full article
(This article belongs to the Special Issue Healthcare Resource Management in Large-Scale Epidemics)
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17 pages, 531 KiB  
Article
Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control
by Hai-Feng Ling, Zheng-Lian Su, Xun-Lin Jiang and Yu-Jun Zheng
Healthcare 2021, 9(2), 126; https://doi.org/10.3390/healthcare9020126 - 28 Jan 2021
Cited by 11 | Viewed by 1902
Abstract
In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these [...] Read more.
In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Healthcare Resource Management in Large-Scale Epidemics)
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19 pages, 1644 KiB  
Article
‘One Health’ Actors in Multifaceted Health Systems: An Operational Case for India
by Sandul Yasobant, Walter Bruchhausen, Deepak Saxena and Timo Falkenberg
Healthcare 2020, 8(4), 387; https://doi.org/10.3390/healthcare8040387 - 07 Oct 2020
Cited by 4 | Viewed by 2584
Abstract
The surging trend of (re)emerging diseases urges for the early detection, prevention, and control of zoonotic infections through the One Health (OH) approach. The operationalization of the OH approach depends on the contextual setting, the presence of the actors across the domains of [...] Read more.
The surging trend of (re)emerging diseases urges for the early detection, prevention, and control of zoonotic infections through the One Health (OH) approach. The operationalization of the OH approach depends on the contextual setting, the presence of the actors across the domains of OH, and the extent of their involvement. In the absence of national operational guidelines for OH in India, this study aims to identify potential actors with an attempt to understand the current health system network strength (during an outbreak and non-outbreak situations) at the local health system of Ahmedabad, India. This case study adopted a sequential mixed methods design conducted in two phases. First, potential actors who have been involved directly or indirectly in zoonoses prevention and control were identified through in-depth interviews. A network study was conducted as part of the second phase through a structured network questionnaire. Interest and influence matrix, average degree, network density, and degree of centralization were calculated through Atlas.Ti (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany), UCINET (Analytic Technologies, Lexington, KY, USA) software. The identified actors were categorized based on power, administrative level (either at the city or district level), and their level of action: administrative (policy planners, managers), providers (physicians, veterinarians), and community (health workers, community leaders). The matrix indicated that administrative actors from the district level were ‘context setters’ and the actors from the city level were either ‘players’ or ‘subjects’. The network density showed a strength of 0.328 during the last outbreak of H5N1, which decreased to 0.163 during the non-outbreak situation. Overall, there was low collaboration observed in this study, which ranged from communication (during non-outbreaks) to coordination (during outbreaks). The private and non-governmental actors were not integrated into collaborative activities. This study concludes that not only collaboration is needed for OH among the sectors pertaining to the human and the animal health system but also better structured (‘inter-level’) collaboration across the governance levels for effective implementation. Full article
(This article belongs to the Special Issue Healthcare Resource Management in Large-Scale Epidemics)
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20 pages, 5000 KiB  
Article
Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality
by Liang Geng, Renbin Xiao and Jie Chen
Healthcare 2020, 8(3), 245; https://doi.org/10.3390/healthcare8030245 - 30 Jul 2020
Cited by 5 | Viewed by 2510
Abstract
The healthcare resources supply network design for resilience is an effective way to deal with uncertainty disruption. In this article we propose a model of supply network self-organization evolution, and establish self-organized criticality as a cause of cascade failure. Our main purpose is [...] Read more.
The healthcare resources supply network design for resilience is an effective way to deal with uncertainty disruption. In this article we propose a model of supply network self-organization evolution, and establish self-organized criticality as a cause of cascade failure. Our main purpose is to keep the system in a resilient range, i.e., critical state. A network structural design with smaller degree distribution exponent can achieve better absorptive capacity at macro level. An interactive rule design with extremal optimization has better adaptive capacity at micro level. Using macro statistic and indicator micro performance indicator, we demonstrate that our design can slow the development to a supercritical state and can improve the resilience of the supply network. Full article
(This article belongs to the Special Issue Healthcare Resource Management in Large-Scale Epidemics)
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14 pages, 1575 KiB  
Article
Competition in Public Procurement in the Czech and Slovak Public Health Care Sectors
by Juraj Nemec, Matus Kubak, Milan Krapek and Maria Horehajova
Healthcare 2020, 8(3), 201; https://doi.org/10.3390/healthcare8030201 - 07 Jul 2020
Cited by 14 | Viewed by 3991
Abstract
Sustainability of health financing is a critical issue for all countries, especially now in the COVID-19 period. The final level of achievements of critical public health goals is connected not only with the efforts of the people involved, but also with the availability [...] Read more.
Sustainability of health financing is a critical issue for all countries, especially now in the COVID-19 period. The final level of achievements of critical public health goals is connected not only with the efforts of the people involved, but also with the availability of funding to cover the costs of the actions needed. One of the “internal sources” providing more resources to cover public health care costs is effective public procurement in the health care sector. According to existing scientific literature, a low rate of competition represents one important factor that has a direct negative impact on the efficiency of public procurement. The aim of our article is to examine the degree of competitiveness of public procurement in the Czech and Slovak health care systems and its impact on the final price of a contract. The research fully attested the findings of those studies carried out so far – the higher the number of tenderers, the lower the final price, even in the Czech and Slovak health sectors. However, the average number of tenderers is only around two and in the Czech Republic for more than half of the tenders only one bid was submitted. Full article
(This article belongs to the Special Issue Healthcare Resource Management in Large-Scale Epidemics)
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