Recent COVID-19 outbreaks pose serious public health challenges all around the world. South Korea had experienced the early outbreak of the COVID-19 pandemic and implemented early effective interventions. The 2020 COVID-19 outbreak in South Korea showed spatial hot spots and super-spreading events. As a result of these super-spreading events, three huge outbreaks of the COVID-19 have occurred in Korea from February to December 2020. To capture the intrinsic nature of heterogeneity, an agent-based model has been developed focusing on early transmission dynamics of COVID-19 in South Korea. Based on the social empirical contact information of early confirmed cases of COVID-19, we have constructed a scale-free network. Our agent-based model has incorporated essential individual variability such as different contact numbers and infectivity levels. In the absence of vaccines or treatment, contact tracing, caseisolation, quarantine are the most critical interventions to prevent larger outbreaks. First, we investigate the impacts of critical factors on various epidemic outputs such as incidence and cumulative incidence. These critical factors include contact numbers, transmission rates, infectivity of presymptomatic or asymptomatic cases, and contact-tracing with quarantine intervention. Furthermore, the effectiveness of case isolation and contact-tracing (followed by quarantine) is evaluated under various scenarios. Our results indicate that case isolation combined with contact-tracing quarantine is much more effective under a moderate level of R-0 (smaller transmission rates or contact numbers) and presymptomatic cases. However, the efficacy of interventions reduces significantly for a higher level of R-0 (larger transmission rates or contact numbers) with a high level of infectivity (in presymptomatic cases). This highlights the key role of efficient contact-tracing and case-isolation to mitigate larger outbreaks or super-spreading events.
机构:
Univ Texas Austin, Dept Integrat Biol, Austin, TX 78712 USA
Univ Hong Kong, Sch Publ Hlth, Li Ka Shing Fac Med, World Hlth Org Collaborating Ctr Infect Dis Epide, Hong Kong, Peoples R China
Hong Kong Sci & Technol Pk, Lab Data Discovery Hlth, Hong Kong, Peoples R ChinaUniv Texas Austin, Dept Integrat Biol, Austin, TX 78712 USA
机构:
Dalian Minzu Univ, Coll Informat & Commun Engn, Dalian 116600, Peoples R ChinaDalian Minzu Univ, Coll Informat & Commun Engn, Dalian 116600, Peoples R China
Sun, Hao-Chen
Liu, Xiao-Fan
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机构:
City Univ Hong Kong, Dept Media & Commun, Web Min Lab, Hong Kong, Peoples R ChinaDalian Minzu Univ, Coll Informat & Commun Engn, Dalian 116600, Peoples R China
Liu, Xiao-Fan
Du, Zhan-Wei
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机构:
Univ Hong Kong, LKS Fac Med, Sch Publ Hlth, WHO Collaborating Ctr Infect Dis Epidemiol & Cont, Hong Kong, Peoples R China
Hong Kong Sci & Technol Pk, Lab Data Discovery Hlth, Hong Kong, Peoples R ChinaDalian Minzu Univ, Coll Informat & Commun Engn, Dalian 116600, Peoples R China
Du, Zhan-Wei
Xu, Xiao-Ke
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机构:
Dalian Minzu Univ, Coll Informat & Commun Engn, Dalian 116600, Peoples R ChinaDalian Minzu Univ, Coll Informat & Commun Engn, Dalian 116600, Peoples R China
Xu, Xiao-Ke
Wu, Ye
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机构:
Beijing Normal Univ, Computat Commun Res Ctr, Zhuhai 519087, Peoples R China
Beijing Normal Univ, Sch Journalism & Commun, Beijing 100875, Peoples R ChinaDalian Minzu Univ, Coll Informat & Commun Engn, Dalian 116600, Peoples R China