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ORIGINAL RESEARCH ARTICLE
The concept of a health security system model during a pandemic
Artur Szymonik 1, A,C-D
 
 
 
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1
Military University of Technology, Poland
 
 
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
 
 
Submission date: 2022-11-22
 
 
Final revision date: 2022-12-01
 
 
Acceptance date: 2022-12-30
 
 
Publication date: 2022-12-31
 
 
Corresponding author
Artur Szymonik   

Military University of Technology, Poland
 
 
SLW 2022;57(2):5-18
 
KEYWORDS
TOPICS
ABSTRACT
The necessity of proper health security management is determined by its decisive role in ensuring survival conditions. Estimating the risk in the area of health security and striving to implement appropriate models of the health security system seems to be crucial in the process of managing it. Effective healthcare management is one of the key elements of a healthier society, where positive action in the identified area of research, technology and management has great potential to improve the global response to potential future health crises. The aim of this study is to propose a model of the health security system, taking into account the dynamics of changes in its determinants caused by the COVID - 19 pandemic. The intention of the presented solutions is to optimize the use of available resources during the health crisis. The main research problem of the article is the question: how would the new model of the health security system take into account the needs of health care in the conditions of the COVID - 19 pandemic? Using the observational method, available research as well as exploratory and intervention data related to COVID-19, an attempt was made to propose a model aimed at efficient management of medical care provided to patients. Not only patients with COVID - 19 but also with other diseases, and healthy people, as part of integrated framework for broadly understood prevention. Focusing on technology-oriented models together with a well-matched infrastructure may result in better results in the prevention and care of COVID - 19 patients. The aforementioned technological development will also serve the implementation of new models enabling the application of genomics, proteomics, nanotechnology, materials science and digital devices for early diagnosis and infection control. The presented model has been developed taking into account various factors and conditions that may affect its efficiency, as well as the proper functioning of the health security system.
REFERENCES (28)
1.
Afzal, S., Ghani S., Jenkins-Smith, H.C., Ebert, D.S., Hadwiger, M., Hoteit, I., 2020. A visual analytics based decision making environment for covid-19 modelling and visualization, 2020 IEEE Visualization conference (VIS).
 
2.
Bozyiğit, F., Aktaş, Ö., Kılınç, D., 2021. Linking software requirements and conceptual models: A systematic literature review, Engineering Science and Technology, an International Journal, 24 (1).
 
3.
Covid-19: Operational guidance for maintaining essential health services during an outbreak, World Health Organization, 2020.
 
4.
COVID-19: vulnerable and high risk groups [online], 2021. World Health Organization. Available at: https://www.who.int/westernpac..., [5 December 2021].
 
5.
COVID-19: Who’s at higher risk of serious symptoms? [online], 2021. MAYO CLINIC. Available at: https://www.mayoclinic.org/dis... [5 December 2021].
 
6.
Gilbert, J.K., Boutler, C.J.,1998. Learning Science through Models and Modelling, In B.J. Fraser, K.G. Tobin, International Handbook of Science Education, London.
 
7.
Habli, I., 2020. Alexander, R., Hawkins, R., et al, Enhancing COVID-19 decision making by creating an assurance case for epidemiological models, BMJ Health & Care Informatics.
 
8.
Han, T., Bernardo, Gois F.N., Oliveira, R., et al., 2021. Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML, Soft Computing, January 2021.
 
9.
Kahn E., Ramsey L., Brownson R., et al., 2002. The effectiveness of interventions to increase physical activity, American Journal of preventive Medicine, 22 (4), Supplement 1, May 2002.
 
10.
Kalippan, J., Srinivasan, K., Qaisar, S.M., et al., 2021. Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate, Frontiers in Public Health, September 2021.
 
11.
Kiok Liang Teow, Kelvin Bryan Tan, Hwee Pin Phua, Zhu Zhecheng, 2018. Applying Gravity model to predict demand of public hospital beds, Operations Research for Health Care, 17.
 
12.
Kitler W., (red), 2014. Koncepcja systemu bezpieczeństwa narodowego Rzeczypospolitej Polskiej, AON, Warszawa.
 
13.
Kluczowe czynniki rozwoju systemu ochrony zdrowia i ich znaczenie dla działań Public Relations jednostek ochrony zdrowia [online], 2011. Prawo.pl. Available at: https://www.prawo.pl/zdrowie/k... [27 February 2021].
 
14.
Książkowski, M., 2021. Prof. Filipiak: Polska najgorzej poradziła sobie z pandemią. Zaciągnęliśmy duży dług zdrowotny [online]. Puls Medycyny. Available at: https://pulsmedycyny.pl/prof-f... [05 August 2021].
 
15.
Kuvvetli, Y., Deveci, M., Paksoy, T., et al., 2021. A predictive analytics model for Covid-19 pandemic using artificial neural networks, Decision Analytics Journal, 1 (November).
 
16.
Legido-Quigley, H., Asgari, N., et. al, 2020. Are high performing health systems resilient against the COVID-19 epidemic? Lancet.
 
17.
Lobo Marques, J.A., Bernardo Gois, F.N., Xavier-Neto, J., et al., 2021. Predictive Models for Decision Support in the COVID-19 Crisis, SpringerBriefs in Applied Sciences and Technology, Switzerland.
 
18.
Lowe, J.M., Sen, A.,1996. Gravity model applications in health planning: analysis of an urban hospital market, Journal of Regional Science, 36 (3).
 
19.
McNulty, C., Collin, S., Cooper, E., et al., 2019. Public understanding and use of antibiotics in England: findings from a household survey in 2017, BMJ Open.
 
20.
Panovska-Griffiths, J., Kerr, C.C., Waites, W., Stuart, R.M., 2021. Mathematical modelling as a tool for policy decision making: Applications to the COVID - 19 pandemic, Handbook of Statistics, 44, 291-326.
 
21.
Paplicki, M., 2020. Bezpieczeństwo zdrowotne obywatela w polskim systemie ratownictwa medycznego, Wydział Prawa, Administracji i Ekonomii Uniwersytetu Wrocławskiego, Wrocław.
 
22.
Sanderson, C., Gruen, R., 2006. Analytical Models for Decision Making, Open University Press, London.
 
23.
Simon, H. A., 1976. From substantive to procedural rationality, Models of bounded rationality: behavioural and business organization, Cambidge, MIT Press.
 
24.
Skyttner L., 2005. General Systems Theory: Problems, Perspectives, Practice, World Scientific Publishing, 2nd edition, Singapore.
 
25.
Sockwell, T., Sherk, A., Norström, T., et al., 2018. Estimating the public health impact of disbanding a government alcohol monopoly: application of new methods to the case of Sweden, BMC Public Health.
 
26.
Surma, S., Szyndler A., Narkiewicz K., 2017. Świadomość wybranych czynników ryzyka chorób układu sercowo-naczyniowego w populacji młodych osób, Choroby Serca i Naczyń, 14 (4).
 
27.
Thomson, K., Berry, R., Robinson, T., 2020. An examination of trends in antibiotic prescribing in primary care and the association with area-level deprivation in England, BMC Public Health.
 
28.
Unified Modeling Language [online], 2021. Wikipedia. Available at: https://en.wikipedia.org/wiki/... [05 August 2021].
 
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