已发表论文

中国疾控中心为了控制中国传染病和地方病而提供的公共卫生服务的系统动力学模型

 

Authors Li M, Yu W, Tian W, Ge Y, Liu Y, Ding T, Zhang L

Received 24 August 2018

Accepted for publication 29 January 2019

Published 13 March 2019 Volume 2019:12 Pages 613—625

DOI https://doi.org/10.2147/IDR.S185177

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Cristina Weinberg

Peer reviewer comments 2

Editor who approved publication: Dr Joachim Wink

Background: Infectious and endemic diseases are a serious public health concern worldwide, and their prevention and treatment are globally controversial. This study aimed to establish an system dynamics (SD) model to analyze the factors influencing public health services provided by the Chinese Centers for Disease Control and Prevention (China CDC) to implement infectious and endemic disease control in China, by establishing more effective interventions to provide public health services and thus achieving the goal of controlling infectious and endemic diseases.
Materials and methods: An SD model was constructed using the Vensim DSS program. Intervention experiments were performed using the SD model, which reflected the influences on disease control by adjusting the governmental investment and compensation level for public health products.
Results: The experimental results showed that increasing the governmental investment in China CDC and compensation level for public health products will significantly increase the public health product rate provided by China CDC.
Discussion: Problems with infectious and endemic disease prevention and treatment are the result of the system’s incomplete functioning and limited health resources. To address the current problems and improve the system, the government should increase its investment in the public health service system and improve the compensation system to ensure smooth implementation of infectious and endemic disease prevention and treatment and, ultimately, improve public health in China.
Keywords: epidemic model, transmission, spread, epidemiology, public health, system dynamics, disease management




Figure 5 Sensitivity analysis of the model.