Exploring the potential of learning methods and recurrent dynamic model with vaccination: A comparative case study of COVID-19 in Austria, Brazil, and China

计算机科学 流行病模型 接种疫苗 人工智能 分段 机器学习 医学 数学 人口 环境卫生 数学分析 免疫学
作者
Seyed Ali Rakhshan,M. Zaj,F.H. Ghane,Mahdi Soltani Nejad
出处
期刊:Physical review [American Physical Society]
卷期号:109 (1)
标识
DOI:10.1103/physreve.109.014212
摘要

In order to effectively manage infectious diseases, it is crucial to understand the interplay between disease dynamics and human conduct. Various factors can impact the control of an epidemic, including social interventions, adherence to health protocols, mask-wearing, and vaccination. This article presents the development of an innovative hybrid model, known as the Combined Dynamic-Learning Model, that integrates classical recurrent dynamic models with four different learning methods. The model is composed of two approaches: The first approach introduces a traditional dynamic model that focuses on analyzing the impact of vaccination on the occurrence of an epidemic, and the second approach employs various learning methods to forecast the potential outcomes of an epidemic. Furthermore, our numerical results offer an interesting comparison between the traditional approach and modern learning techniques. Our classic dynamic model is a compartmental model that aims to analyze and forecast the diffusion of epidemics. The model we propose has a recurrent structure with piecewise constant parameters and includes compartments for susceptible, exposed, vaccinated, infected, and recovered individuals. This model can accurately mirror the dynamics of infectious diseases, which enables us to evaluate the impact of restrictive measures on the spread of diseases. We conduct a comprehensive dynamic analysis of our model. Additionally, we suggest an optimal numerical design to determine the parameters of the system. Also, we use regression tree learning, bidirectional long short-term memory, gated recurrent unit, and a combined deep learning method for training and evaluation of an epidemic. In the final section of our paper, we apply these methods to recently published data on COVID-19 in Austria, Brazil, and China from 26 February 2021 to 4 August 2021, which is when vaccination efforts began. To evaluate the numerical results, we utilized various metrics such as RMSE and R-squared. Our findings suggest that the dynamic model is ideal for long-term analysis, data fitting, and identifying parameters that impact epidemics. However, it is not as effective as the supervised learning method for making long-term forecasts. On the other hand, supervised learning techniques, compared to dynamic models, are more effective for predicting the spread of diseases, but not for analyzing the behavior of epidemics.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
能干冰露完成签到,获得积分10
2秒前
牛奶拌可乐完成签到 ,获得积分10
4秒前
量子星尘发布了新的文献求助30
4秒前
周小鱼完成签到 ,获得积分10
8秒前
13秒前
21秒前
老张完成签到,获得积分10
27秒前
29秒前
zhugao完成签到,获得积分10
31秒前
34秒前
南风知我意完成签到,获得积分10
37秒前
朴实寻琴完成签到 ,获得积分10
37秒前
可可可爱完成签到 ,获得积分10
40秒前
lsy完成签到,获得积分10
44秒前
量子星尘发布了新的文献求助10
47秒前
48秒前
48秒前
hwen1998完成签到 ,获得积分10
51秒前
52秒前
53秒前
wwb发布了新的文献求助10
56秒前
57秒前
59秒前
LHT完成签到,获得积分10
1分钟前
落寞凌波发布了新的文献求助10
1分钟前
桐桐应助幸福的杨小夕采纳,获得10
1分钟前
韩麒嘉完成签到 ,获得积分10
1分钟前
聪慧的凝海完成签到 ,获得积分0
1分钟前
1分钟前
wwb发布了新的文献求助10
1分钟前
phil完成签到 ,获得积分10
1分钟前
1分钟前
高高菠萝完成签到 ,获得积分10
1分钟前
滴滴滴完成签到 ,获得积分10
1分钟前
yangsi完成签到 ,获得积分10
1分钟前
量子星尘发布了新的文献求助10
1分钟前
1分钟前
酷炫葵阴发布了新的文献求助10
1分钟前
ORANGE完成签到,获得积分10
1分钟前
思源应助松松采纳,获得20
1分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038029
求助须知:如何正确求助?哪些是违规求助? 3575740
关于积分的说明 11373751
捐赠科研通 3305559
什么是DOI,文献DOI怎么找? 1819224
邀请新用户注册赠送积分活动 892652
科研通“疑难数据库(出版商)”最低求助积分说明 815022