Description
days ago
EBTEHAL BAAWADH
Epidemiology to Understanding of COVID-19.
COLLAPSE
How does epidemiology contribute to the understanding of COVID-19?
Epidemiology plays a crucial role in understanding COVID-19. It helps determine the virus’s origin, transmission patterns, and factors that increase disease severity (Maryam et al., 2023). Epidemiological data provides valuable information on the distribution and determinants of the disease, aiding in decision-making for healthcare and society (Bulut & Kato, 2020). By studying the life cycle and spread of the virus, epidemiology contributes to the development of diagnostic methods and prevention strategies (Bulut & Kato, 2020). Furthermore, epidemiological studies during the pandemic highlight the importance of following methodological advances and avoiding biases in data collection (Martínez-Alés & Keyes, 2023). Epidemiology also provides insights into the global impact of COVID-19, including the number of cases and mortalities reported worldwide (Afreen et al., 2023). Overall, epidemiology is essential for understanding the epidemiological characteristics of COVID-19, guiding public health interventions, and informing policy decisions.
Tools and methods are epidemiologists uniquely able to contribute to help contain the pandemic.
Epidemiologists have contributed to containing the COVID-19 pandemic using various tools and methods. Mathematical modeling has been utilized to enhance the understanding of the social and economic impacts of the pandemic (Cifuentes‐Faura et al., 2022). Epidemiological data analysis has been crucial in determining the extent of the pandemic and its effects on healthcare and society (Bulut & Kato, 2020). Applied epidemiology, as the intelligence arm of health emergencies, provides information on the movement of pathogens and ways to stop them, informing decision-making to improve population health (Griffith et al., 2022). Simulation approaches, such as system dynamics models, agent-based models, and discrete event simulations, have been used to evaluate interventions, predict the pandemic, and assess its impacts (Zhang et al., 2022). Spatial tools and methodologies have been employed to identify spatial and spatiotemporal variations of COVID-19 and the drivers behind these variations, aiding in developing prevention and control strategies (Nazia et al., 2022).
References
Afreen, U., Afreen, U., & Bano, D. (2023). COVID-19 pandemic: outbreak, epidemiology and immunology. In BENTHAM SCIENCE PUBLISHERS eBooks (pp. 1–21). https://doi.org/10.2174/9789815165944123010005
Bulut, C., & Kato, Y. (2020). Epidemiology of COVID-19. Turkish Journal of Medical Sciences, 50(SI-1), 563–570. https://doi.org/10.3906/sag-2004-172
Cifuentes‐Faura, J., Faura-Martínez, Ú., & Lechuga, M. L. (2022). Mathematical modeling and the use of network models as epidemiological tools. Mathematics, 10(18), 3347. https://doi.org/10.3390/math10183347
Griffith, M. M., Parry, A. E., Housen, T., Stewart, T., & Kirk, M. (2022). COVID-19 and investment in applied epidemiology. Bulletin of the World Health Organization, 100(7), 415-415A. https://doi.org/10.2471/blt.22.288687
Martínez-Alés, G., & Keyes, K. M. (2023). Invited commentary: Modern Epidemiology Confronts COVID-19—Reflections from Psychiatric Epidemiology. American Journal of Epidemiology, 192(6), 856–860. https://doi.org/10.1093/aje/kwad045
Maryam, S., Fatima, R., Ashfaq, M., Hassan, N., Bibi, A., Syed, A., & Kamran, M. (2023). Epidemiological studies of COVID-19 disease. International Journal of Health Sciences (IJHS), 7(S1), 990–1030. https://doi.org/10.53730/ijhs.v7ns1.14303
Nazia, N., Butt, Z. A., Bedard, M. L., Tang, W., Sehar, H., & Law, J. (2022). Methods used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic review. International Journal of Environmental Research and Public Health, 19(14), 8267. https://doi.org/10.3390/ijerph19148267
Zhang, W., Liu, S., Osgood, N. D., Zhu, H., Qian, Y., & Jia, P. (2022). Using simulation modelling and systems science to help contain COVID‐19: A systematic review. Systems Research and Behavioral Science, 40(1), 207–234. https://doi.org/10.1002/sres.2897
