Evaluation of infectious diseases control using an individual model under the test-trace-isolate program
Main Article Content
Abstract
The global public health situation is constantly threatened by infectious diseases. To effectively control the spread of these diseases, it is crucial to quickly disrupt the transmission pathways of the pathogens. During the COVID-19 pandemic, testing, tracing, and isolation programs effectively responded to disease outbreaks in some areas but have largely failed in many other countries. This study presents a computational model to evaluate the effectiveness of various prevention and control measures in managing epidemic transmission dynamics. The model utilizes an individual-based model and dynamic close-contact networks to simulate the spread of infectious diseases. By considering the dynamic contact network formed by different individuals and their activities in various social environments, the model can track the spread of the disease and changes in the infection status of each individual through simulation. Using COVID-19 as an example, the model simulations demonstrate that infections increase rapidly after a local outbreak without preventive measures, quickly reach a peak of daily new infections. However, implementing test-trace-isolate measures significantly decreases the scale of infections and the number of daily new cases. Further stringent preventive measures to reduce individual contact are required to achieve the goal of zero infections. The results emphasize the importance of early detection and isolation in curbing the spread of the virus. The model established in this study can be used to evaluate and optimize prevention and control measures to achieve the goal of zero infections.
Keywords:
dynamic contact network, mask wearing rate, vaccination rate, individual-based model
Article Details
How to Cite
DENG, Yue; LI, Mingjing; LEI, Jinzhi.
Evaluation of infectious diseases control using an individual model under the test-trace-isolate program.
Medical Research Archives, [S.l.], v. 12, n. 11, nov. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5987>. Date accessed: 12 dec. 2024.
doi: https://doi.org/10.18103/mra.v12i11.5987.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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3. P. Yuan, Y. Tan, L. Yang, E. Aruffo, N. H. Ogden, G. Yang, H. Lu, Z. Lin, W. Lin, W. Ma, M. Fan,K. Wang, J. Shen, T. Chen, H. Zhu. Assessing the mechanism of citywide test-trace-isolate Zero-COVID policy and exit strategy of COVID-19 pandemic, Infect Dis Poverty 11 (1) (2022) 104.
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7. H. W. Hethcote. The mathematics of infectious diseases, SIAM Rev. 42 (2000) 599–653.
8. B. Tang, F. Xia, S. Tang, N. L. Bragazzi, Q. Li, X. Sun, J. Liang, Y. Xiao, J. Wu. The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemic in the final phase of the current outbreak in China, Int J Infect Dis 96 (2020) 636 – 647.
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11. F. Wu, X. Liang, J. Lei. Modelling COVID-19 epidemic with confirmed cases-driven contact tracing quarantine, Infect Dis Model 8 (2023) 415–426.
12. L. Yin, H. Zhang, Y. Li, K. Liu, T. Chen, W. Luo, S. Lai, Y. Li, X. Tang, L. Ning. Effectiveness of contact tracing, mask wearing and prompt testing on suppressing COVID-19 resurgences in megacities: An individual-based modelling study, Social Science Electronic Publishing (2021) doi: 10.2139/ssrn.3765491.
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16. S. L. Chang, N. Harding, C. Zachreson, O. M. Cliff, M. Prokopenko, Modelling transmission and control of the COVID-19 pandemic in Australia, Nat Commun 11 (2020).
17. N. Masuda, N. Konno, K. Aihara, Transmission of severe acute respiratory syndrome in dynamical small-world networks, Phys Rev E 69 (3) (2004) 031917.
18. G. Hartvigsen, J. M. Dresch, A. L. Zielinski, A. J. Macula, C. C. Leary. Network structure, and vaccination strategy and effort interact to affect the dynamics of influenza epidemics, J Theor Biol 246 (2) (2007) 205–213.
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33. Y. Deng, S. Xing, M. Zhu, J. Lei, Impact of insufficient detection in COVID-19 outbreaks, Math Biosci Eng 18 (6) (2021) 9727–9742.