基于主体模型在慢性非传染性疾病预测中应用

王润思; 李希

doi:10.11847/zgggws1135054

基于主体模型在慢性非传染性疾病预测中应用

doi: 10.11847/zgggws1135054

王润思¹,
李希^{1, 2, ,}

1.
国家心血管病中心中国医学科学院北京协和医学院阜外医院国家心血管疾病临床医学研究中心国家卫生健康委员会心血管药物临床研究重点实验室心血管疾病国家重点实验室, 北京100037
2.
国家心血管病中心预防医学处

基金项目: 国家重点研发计划项目（2017YFC1310803）；中国医学科学院医学与健康科技创新工程（2017 – I2M – 2 – 002）

详细信息

作者简介:
王润思（1999 – ），女，北京人，硕士在读，研究方向：慢性病流行病学

通讯作者:
李希，E-mail：xi.li@fwoxford.org

中图分类号: R 181
计量
- 文章访问数: 270
- HTML全文浏览量: 150
- PDF下载量: 31
- 被引次数: 0
出版历程
- 接收日期: 2021-04-15
- 网络出版日期: 2022-03-23
- 刊出日期: 2022-07-01

Application of agent-based modeling method in prediction of non-communicable chronic diseases: a review

WANG Run-si¹,
LI Xi^{1, 2
, ,}

1.
National Clinical Research Center for Cardiovascular Diseases, National Health Commission Key Laboratory of Clinical Research for Cardiovascular Medications, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China

摘要

摘要: 慢性非传染性疾病（慢性病）作为全球的首要健康威胁，在全球范围内造成了日益沉重的疾病负担。与传统统计模型相比，基于主体的模型不再着眼于拟合整体的集中特点，而是基于个体建模来模拟个体在个体间的交互作用与环境对个体影响下发生的变化，从而模拟每个个体的特征变化，进而模拟人群的特征，其允许人与环境的特征以及它们的相互作用随时间而变化，并能模拟人与环境的特征随时间变化的动态过程，为此基于主体的模型具有模拟更加接近真实情况的复杂问题的能力。本文从基于主体模型的结构与特点以及该模型在慢性病预测中的应用和局限性等方面进行了综述，旨在为该模型在慢性病领域的预测及模拟应用提供理论与经验支撑。
- 基于主体模型 /
- 慢性非传染性疾病(慢性病) /
- 预测 /
- 应用
Abstract: Non - communicable chronic diseases (NCDs), as a primary global health threat, have caused an increasingly heavy burden all over the world. Compared with traditional statistical model, the agent - based model no longer focuses on fitting general characteristics, but to simulate individual changes under the interaction between individuals and environmental influence on individuals by building individual - based models for simulating the characteristics of a population. By including time - dependent changes in characteristics of human and environment and their interactions, the agent - based model could be used to simulate dynamic changing process of changes in characteristics of human and environment with time. Therefore, the agent - based model can be adopted to simulate complex problems closer to the real situation. The study introduced the structure and characteristics of the agent - based model and reviewed the application and limitations of the model in prediction of NCDs prevalence to provide theoretical and empirical support for the application of the model in NCDs prevention and control.
- agent-based model /
- non-communicable chronic disease /
- prediction /
- application

HTML全文

参考文献(31)

[1]	World Health Organization. World health statistics 2019: monitor-ing health for the SDGs, sustainable development goals[EB/OL]. (2020 – 05 – 13) [2021 – 01 – 31].https://apps.who.int/iris/handle/10665/338072.
[2]	中华人民共和国国家统计局. 中国统计年鉴2019[M]. 北京: 中国统计出版社, 2019.
[3]	马爱娟, 刘爱萍, 王培玉, 等. 糖尿病风险评估不同方法应用比较[J]. 中国公共卫生, 2010, 26(5): 570 – 572. doi: 10.11847/zgggws2010-26-05-33
[4]	方欣, 杨泽, 钟文玲, 等. 福建省缺血性心血管病发病风险评估[J]. 中国公共卫生, 2020, 36(8): 1139 – 1142. doi: 10.11847/zgggws1126994
[5]	Nianogo RA, Arah OA. Agent - based modeling of noncommunicable diseases: a systematic review[J]. American Journal of Public Health, 2015, 105(3): e20 – e31. doi: 10.2105/AJPH.2014.302426
[6]	Jones AP, Homer JB, Murphy DL, et al. Understanding diabetes population dynamics through simulation modeling and experi-mentation[J]. American Journal of Public Health, 2006, 96(3): 488 – 494. doi: 10.2105/AJPH.2005.063529
[7]	谢丽, 赵培忻, 丁海欣. 新发突发传染病驱动的谣言传播建模与仿真 —— 双重网络下的研究[J]. 现代情报, 2020, 40(10): 22 – 33. doi: 10.3969/j.issn.1008-0821.2020.10.003
[8]	Luke DA, Stamatakis KA. Systems science methods in public health: dynamics, networks, and agents[J]. Annual Review of Public Health, 2012, 33: 357 – 376. doi: 10.1146/annurev-publhealth-031210-101222
[9]	Zhang X. Application of discrete event simulation in health care: a systematic review[J]. BMC Health Services Research, 2018, 18(1): 687. doi: 10.1186/s12913-018-3456-4
[10]	Hunter E, Namee BM, Kelleher J. An open - data - driven agent - based model to simulate infectious disease outbreaks[J]. PLoS One, 2018, 13(12): e0208775. doi: 10.1371/journal.pone.0208775
[11]	Lee BY, Brown ST, Korch GW, et al. A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic[J]. Vaccine, 2010, 28(31): 4875 – 4879. doi: 10.1016/j.vaccine.2010.05.002
[12]	Macal CM, North MJ. Tutorial on agent-based modelling and simulation[J]. Journal of Simulation, 2010, 4(3): 151 – 162. doi: 10.1057/jos.2010.3
[13]	Edling C. Book reviews: Joshua M. Epstein and Robert Axtell: Growing artificial societies: social science from the Bottom Up. Washington, DC: Brookings Institution Press and Cambridge, MA: MIT Press, 1996 (available with or without CD)[J]. Acta Soci-ologica, 1998, 41(1): 75 – 77.
[14]	Auchincloss AH, Roux AVD. A new tool for epidemiology: the usefulness of dynamic - agent models in understanding place effects on health[J]. American Journal of Epidemiology, 2008, 168(1): 1 – 8. doi: 10.1093/aje/kwn118
[15]	Bonabeau E. Agent - based modeling: methods and techniques for simulating human systems[J]. Proceedings of the National Aca-demy of Sciences of the United States of America, 2002, 99(S3): 7280 – 7287.
[16]	Li Y, Kong N, Lawley MA, et al. Using systems science for population health management in primary care[J]. Journal of Primary Care and Community Health, 2014, 5(4): 242 – 246. doi: 10.1177/2150131914536400
[17]	Lassnig A, Rienmueller T, Kramer D, et al. A novel hybrid modeling approach for the evaluation of integrated care and economic outcome in heart failure treatment[J]. BMC Medical Informatics and Decision Making, 2019, 19(1): 229. doi: 10.1186/s12911-019-0944-3
[18]	Gao A, Osgood ND, Jiang Y, et al. Projecting prevalence, costs and evaluating simulated interventions for diabetic end stage renal disease in a Canadian population of aboriginal and non - aboriginal people: an agent based approach[J]. BMC Nephrology, 2017, 18(1): 283. doi: 10.1186/s12882-017-0699-y
[19]	Luo LN, Pang BW, Chen J, et al. Assessing the impact of lifestyle interventions on diabetes prevention in China: a modeling approach[J]. International Journal of Environmental Research and Public Health, 2019, 16(10): 1677. doi: 10.3390/ijerph16101677
[20]	Correa MF, Li Y, Kum HC, et al. Assessing the effect of clinical inertia on diabetes outcomes: a modeling approach[J]. Journal of General Internal Medicine, 2019, 34(3): 372 – 378. doi: 10.1007/s11606-018-4773-3
[21]	Xue H, Slivka L, Igusa T, et al. Applications of systems modelling in obesity research[J]. Obesity Reviews, 2018, 19(9): 1293 – 1308. doi: 10.1111/obr.12695
[22]	Kasman M, Hammond RA, Heuberger B, et al. Activating a community: an agent - based model of romp and chomp, a whole - of - community childhood obesity intervention[J]. Obesity, 2019, 27(9): 1494 – 1502.
[23]	Ferguson MC, O'Shea KJ, Hammer LD, et al. Can following formula - feeding recommendations still result in infants who are overweight or have obesity?[J]. Pediatric Research, 2020, 88(4): 661 – 667. doi: 10.1038/s41390-020-0844-3
[24]	Hennessy E, Ornstein JT, Economos CD, et al. Designing an agent - based model for childhood obesity interventions: a case study of child obesity180[J]. Preventing Chronic Disease, 2016, 13: E04.
[25]	Orr MG, Kaplan GA, Galea S. Neighbourhood food, physical activity, and educational environments and black/white disparities in obesity: a complex systems simulation analysis[J]. Journal of Epidemiology and Community Health, 2016, 70(9): 862 – 867. doi: 10.1136/jech-2015-205621
[26]	Lee BY, Ferguson MC, Hertenstein DL, et al. Simulating the impact of sugar - sweetened beverage warning labels in three cities[J]. American Journal of Preventive Medicine, 2018, 54(2): 197 – 204. doi: 10.1016/j.amepre.2017.11.003
[27]	Shi LY, Zhang L, Lu Y. Evaluating social network - based weight loss interventions in Chinese population: an agent - based simulation[J]. PLoS One, 2020, 15(8): e0236716. doi: 10.1371/journal.pone.0236716
[28]	Axelrod R. Advancing the art of simulation in the social sciences[M]//Conte R, Hegselmann R, Terna P. Simulating Social Pheno-mena. Berlin: Springer, 1997: 21 – 40.
[29]	Chen ZM, Lee LM, Chen JS, et al. Cohort profile: the Kadoorie Study of Chronic Disease in China (KSCDC)[J]. International Journal of Epidemiology, 2005, 34(6): 1243 – 1249. doi: 10.1093/ije/dyi174
[30]	Chen ZM, Chen JS, Collins R, et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long - term follow - up[J]. International Journal of Epidemiology, 2011, 40(6): 1652 – 1666. doi: 10.1093/ije/dyr120
[31]	Lu JP, Xuan S, Downing NS, et al. Protocol for the China PEACE (Patient - centered Evaluative Assessment of Cardiac Events) million persons project pilot[J]. BMJ Open, 2016, 6(1): e010200. doi: 10.1136/bmjopen-2015-010200