New Method for Identification and Response to Infectious Disease Patterns Based on Comprehensive Health Service Data
Keywords:
Comprehensive Health Data, Disease Patterns, Health Service Data, Infectious Diseases
Abstract
Infectious diseases continue to pose a major threat to global public health and require early detection and effective response strategies. Despite advances in information technology and data analysis, the full potential of health data in identifying disease patterns and trends remains underutilised. This study aims to propose a comprehensive new mathematical model (new method) that utilises health data to identify infectious disease patterns and trends by exploring the potential of data-driven care approaches in addressing public health challenges associated with infectious diseases. The research methods used are exploratory data collection and analytical model development. The research results obtained mathematical models and algorithms that consider data of period, time, patterns, and trends of dangerous diseases, statistical analysis, and recommendations. Data visualisation and in-depth analysis were conducted in the research to improve the ability to respond to infectious disease threats and provide better decision-making solutions in improving outbreak response, as well as improving preparedness in addressing public health challenges. This research contributes to health practitioners and decision-makers.
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References
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[31] C. Marks et al., “Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action,” Transl. Res., vol. 234, no. 2, pp. 88–113, 2021, doi: https://doi.org/10.1016/j.trsl.2021.03.018.
[32] I. H. Sarker, “Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective,” SN Comput. Sci., vol. 2, no. 5, pp. 377–400, 2021, doi: https://doi.org/10.1007/s42979-021-00765-8.
[33] A. Ruckert, K. Zinszer, C. Zarowsky, R. Labonté, and H. Carabin, “What role for One Health in the COVID-19 pandemic?,” Can. J. Public Heal., vol. 111, no. 5, pp. 641–644, 2020, doi: https://doi.org/10.17269/s41997-020-00409-z.
[34] N. Azzopardi-Muscat, A. Brambilla, F. Caracci, and S. Capolongo, “Synergies in design and health. The role of architects and urban health planners in tackling key contemporary public health challenges,” Acta Bio Medica Atenei Parm., vol. 91, no. 3, pp. 9–23, 2020, doi: 10.23750/abm.v91i3-S.9414.
[35] G. Bammer et al., “Expertise in research integration and implementation for tackling complex problems: when is it needed, where can it be found and how can it be strengthened?,” Palgrave Commun., vol. 6, no. 1, pp. 1–16, 2020, doi: https://doi.org/10.1057/s41599-019-0380-0.
[36] I. Bardhan, H. Chen, and E. Karahanna, “Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management,” MIS Q., vol. 44, no. 1, pp. 185–200, 2020, doi: 10.25300/MISQ/2020/14644.
[37] L. Peek and S. Guikema, “Interdisciplinary theory, methods, and approaches for hazards and disaster research: An introduction to the special issue,” Risk Anal., vol. 41, no. 7, pp. 1047–1058, 2021, doi: https://doi.org/10.1111/risa.13777.
[38] S. Wongvibulsin, S. S. Martin, S. Saria, S. L. Zeger, and S. A. Murphy, “An individualized, data-driven digital approach for precision behavior change,” Am. J. Lifestyle Med., vol. 14, no. 3, pp. 289–293, 2020, doi: https://doi.org/10.1177/1559827619843489.
[39] M. C. Fitzpatrick, C. T. Bauch, J. P. Townsend, and A. P. Galvani, “Modelling microbial infection to address global health challenges,” Nat. Microbiol., vol. 4, no. 10, pp. 1612–1619, 2019, doi: https://doi.org/10.1038/s41564-019-0565-8.
Published
2024-06-18
How to Cite
Vinsensia, D., Amri, S., Sihotang, J., & Sihotang, H. (2024). New Method for Identification and Response to Infectious Disease Patterns Based on Comprehensive Health Service Data. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(3), 581-590. https://doi.org/https://doi.org/10.30812/matrik.v23i3.4000
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