Optimization of echinococcosis control measures based on system dynamics.

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-30 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013186
Jinrui Ma, Xingyu Liu, Zengwen He, Haohao Zhang, Yonghui Ma, Ziqiu Fan, Xuehui Zhao, Ji Zhi, Qing Cao, Huiwen Xue, Huitian Gou
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Abstract

This study aimed to provide strategies to optimize the prevention and control measures of echinococcosis in Gansu Province. Based on the transmission mechanism of echinococcosis, a System Dynamics (SD) model of echinococcosis in Gansu Province was established using Vensim software. Through this model, parameters such as vaccination coverage, domestic dog deworming coverage, slaughter management level, and health education level were adjusted to predict the changing trends in the number of human cases and infected sheep. The results showed that the number of human cases was most sensitive to the adjustment of health education level, followed by changes to vaccination coverage. The number of infected sheep was most sensitive to the adjustment of vaccination coverage, followed by the adjustment of slaughter management level. Based on these simulation results, the prevention and control measures for echinococcosis in Gansu Province were further optimized. The results showed that if the health education level and the vaccination coverage were increased by 50% and 40%, respectively, based on the current prevention and control levels, the number of human cases would approach zero by approximately 2026. When the vaccination coverage and slaughter management level increased by 60% and 45%, respectively, based on current prevention and control levels, the number of infected sheep would approach zero by approximately 2027. This study applied the SD method to the prevention and control of echinococcosis for the first time, and the results can provide key decision-making recommendations to optimize the prevention and control measures of echinococcosis in Gansu province.

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基于系统动力学的棘球蚴病控制措施优化。
本研究旨在为优化甘肃省棘球蚴病的防控措施提供对策。基于棘球蚴病的传播机制,利用Vensim软件建立了甘肃省棘球蚴病的系统动力学(SD)模型。通过该模型,调整疫苗接种覆盖率、家犬驱虫覆盖率、屠宰管理水平、健康教育水平等参数,预测人类病例数和感染羊数的变化趋势。结果表明,人感染病例数对健康教育水平的调整最为敏感,其次是疫苗接种覆盖率的变化。接种覆盖率调整对感染羊数最敏感,屠宰管理水平调整次之。根据模拟结果,进一步优化甘肃省棘球蚴病防控措施。结果表明,在现有防控水平的基础上,如果健康教育水平和疫苗接种覆盖率分别提高50%和40%,到2026年左右人间病例数将接近于零。在目前的预防和控制水平基础上,如果疫苗接种覆盖率和屠宰管理水平分别提高60%和45%,到2027年左右,感染羊的数量将接近于零。本研究首次将SD方法应用于棘球蚴病的防控,其结果可为优化甘肃省棘球蚴病防控措施提供关键决策建议。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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