Modeling the spatiotemporal transmission of COVID-19 epidemic by coupling the heterogeneous impact of detection rates: A case study in Hong Kong

IF 3.8 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Jialyu He , Xintao Liu , Xiaolin Zhu , Hsiang-Yu Yuan , Wu Chen
{"title":"Modeling the spatiotemporal transmission of COVID-19 epidemic by coupling the heterogeneous impact of detection rates: A case study in Hong Kong","authors":"Jialyu He ,&nbsp;Xintao Liu ,&nbsp;Xiaolin Zhu ,&nbsp;Hsiang-Yu Yuan ,&nbsp;Wu Chen","doi":"10.1016/j.healthplace.2025.103422","DOIUrl":null,"url":null,"abstract":"<div><div>During the COVID-19 epidemic, many infections may have been undiagnosed in communities (hidden cases) due to low detection rates, thus exacerbating the overall prevalence of the epidemic. However, the heterogeneity of detection rates poses a challenge in simulating the proportion and spatial distribution of hidden cases. Coupling the heterogeneous impact of detection rates to extend epidemic modeling is necessary for forecasting the health burden and mitigating the inequity of testing resources. In this study, we developed an agent-based model integrated with the Susceptible-Exposed-Reported-Hidden-Removed (SERHR) model to simulate the spatiotemporal transmission of reported and hidden cases (RH-ABM). The RH-ABM was fitted with data for the fifth wave of infection in Hong Kong induced by the Omicron variant. We conducted multi-scenario simulations based on various testing strategies to assess the local variation in attack rates. The RH-ABM predicted that maintaining a constant high detection rate would reduce the average attack rate from 65.62% to 53.09%. Increasing detection rates in groups with many individuals and daily close contact can also assist in controlling the health burden of outbreaks. The variation in the attack rates is strongly associated with changes in the region-stratified detection rates. In addition, The RH-ABM estimated that allocating limited testing resources based on demographic distribution and human mobility data is effective for controlling the average attack rate.</div></div>","PeriodicalId":49302,"journal":{"name":"Health & Place","volume":"92 ","pages":"Article 103422"},"PeriodicalIF":3.8000,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health & Place","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1353829225000115","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0

Abstract

During the COVID-19 epidemic, many infections may have been undiagnosed in communities (hidden cases) due to low detection rates, thus exacerbating the overall prevalence of the epidemic. However, the heterogeneity of detection rates poses a challenge in simulating the proportion and spatial distribution of hidden cases. Coupling the heterogeneous impact of detection rates to extend epidemic modeling is necessary for forecasting the health burden and mitigating the inequity of testing resources. In this study, we developed an agent-based model integrated with the Susceptible-Exposed-Reported-Hidden-Removed (SERHR) model to simulate the spatiotemporal transmission of reported and hidden cases (RH-ABM). The RH-ABM was fitted with data for the fifth wave of infection in Hong Kong induced by the Omicron variant. We conducted multi-scenario simulations based on various testing strategies to assess the local variation in attack rates. The RH-ABM predicted that maintaining a constant high detection rate would reduce the average attack rate from 65.62% to 53.09%. Increasing detection rates in groups with many individuals and daily close contact can also assist in controlling the health burden of outbreaks. The variation in the attack rates is strongly associated with changes in the region-stratified detection rates. In addition, The RH-ABM estimated that allocating limited testing resources based on demographic distribution and human mobility data is effective for controlling the average attack rate.
求助全文
约1分钟内获得全文 求助全文
来源期刊
Health & Place
Health & Place PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
7.70
自引率
6.20%
发文量
176
审稿时长
29 days
期刊介绍: he journal is an interdisciplinary journal dedicated to the study of all aspects of health and health care in which place or location matters.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信