Author Correction: Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China.
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ABSTRACT:
SUBMITTER: Hu S
PROVIDER: S-EPMC8085642 | biostudies-literature | 2021 Apr
REPOSITORIES: biostudies-literature
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Author Correction: Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China.
Hu Shixiong S Wang Wei W Wang Yan Y Litvinova Maria M Luo Kaiwei K Ren Lingshuang L Sun Qianlai Q Chen Xinghui X Zeng Ge G Li Jing J Liang Lu L Deng Zhihong Z Zheng Wen W Li Mei M Yang Hao H Guo Jinxin J Wang Kai K Chen Xinhua X Liu Ziyan Z Yan Han H Shi Huilin H Chen Zhiyuan Z Zhou Yonghong Y Sun Kaiyuan K Vespignani Alessandro A Viboud Cécile C Gao Lidong L Ajelli Marco M Yu Hongjie H
Nature communications 20210430 1
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Project description:The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which are typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed these data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone are sufficient to detect and quantify heterogeneity in susceptibility.
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