Age-specific transmission dynamics under suppression control measures during SARS-CoV-2 Omicron BA.2 epidemic

Zhu, Wenlong; Wen, Zexuan; Chen, Yue; Gong, Xun; Zheng, Bo; Liang, Xueyao; Xu, Ao; Yao, Ye; Wang, Weibing

doi:10.1186/s12889-023-15596-w

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Phase 4 was from 22 April to June 1 2022; routine NAAT/RAT, with greater testing intensity in areas with higher risk, was administered beginning 22 April 2022. Previous studies have provided more detailed information on the interventions implemented during the epidemic 25 26…”

Section: Methodsmentioning

confidence: 99%

“…In early March 2022, the SARS-CoV-2 Omicron BA.2 variant spread rapidly in Shanghai, China 24 25. Due to the high transmissibility and immune evasive properties of Omicron BA.2, there were 588 deaths and more than 600 000 COVID-19 cases as of 1 June 2022, even with the implementation of strict NPIs (school closure, citywide home quarantine and nucleic acid amplification testing/rapid antigen testing (NAAT/RAT)) 25 26. We analysed data on the case-contact clusters of the epidemic in Shanghai, assessed the risk of infection of contacts in different settings at various phases of the epidemic and evaluated the association of infectivity and susceptibility to the Omicron variant in individuals with different clinical and demographic characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Relationship of close contact settings with transmission and infection during the SARS-CoV-2 Omicron BA.2 epidemic in Shanghai

Zhu,

Yuan,

Mao

et al. 2023

BMJ Glob Health

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IntroductionWe analysed case-contact clusters during the Omicron BA.2 epidemic in Shanghai to assess the risk of infection of contacts in different settings and to evaluate the effect of demographic factors on the association of infectivity and susceptibility to the Omicron variant.MethodsData on the settings and frequency of contact, demographic characteristics and comorbidities of index cases, contacts and secondary cases were analysed. Independent effect of multiple variables on the risk for transmission and infection was evaluated using generalised estimating equations.ResultsFrom 1 March to 1 June 2022, we identified 450 770 close contacts of 90 885 index cases. The risk for infection was greater for contacts in farmers’ markets (fixed locations where farmers gather to sell products, adjusted OR (aOR): 3.62; 95% CI 2.87 to 4.55) and households (aOR: 2.68; 95% CI 2.15 to 3.35). Children (0–4 years) and elderly adults (60 years and above) had higher risk for infection and transmission. During the course of the epidemic, the risk for infection and transmission in different age groups initially increased, and then decreased on about 21 April (17th day of citywide home quarantine). Compared with medical workers (reference, aOR: 1.00), unemployed contacts (aOR: 1.77; 95% CI 1.53 to 2.04) and preschoolers (aOR: 1.61; 95% CI 1.26 to 2.05) had the highest risk for infection; delivery workers (aOR: 1.90, 95% CI 1.51 to 2.40) and public service workers (aOR: 1.85; 95% CI 1.64 to 2.10) had the highest risk for transmission. Contacts who had comorbidities (aOR: 1.10; 95% CI 1.09 to 1.12) had a higher risk for infection, particularly those with lung diseases or immune deficiency.ConclusionFarmers’ markets and households were the main setting for transmission of Omicron. Children, the elderly, delivery workers and public service workers had the highest risk for transmission and infection. These findings should be considered when implementing targeted interventions.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relationship of close contact settings with transmission and infection during the SARS-CoV-2 Omicron BA.2 epidemic in Shanghai

Zhu,

Yuan,

Mao

et al. 2023

BMJ Glob Health

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Detecting and quantifying heterogeneity in susceptibility using contact tracing data

Tuschhoff,

Kennedy

2024

PLoS Comput Biol

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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 is 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 this 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 is sufficient to detect and quantify heterogeneity in susceptibility.

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Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study

Tang,

Ma,

Liu

et al. 2024

PLoS Comput Biol

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Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modeling framework integrating the spatio-temporal heterogeneity of susceptible individuals into homogeneous models, by introducing a continuous recruitment process for the susceptibles. A neural network approximates the recruitment rate to develop a Universal Differential Equations (UDE) model. Simultaneously, we pre-set a specific form for the recruitment rate and develop a mechanistic model. Data from a COVID Omicron variant outbreak in Shanghai are used to train the UDE model using deep learning methods and to calibrate the mechanistic model using MCMC methods. Subsequently, we project the attack rate and peak of new infections for the first Omicron wave in China after the adjustment of the dynamic zero-COVID policy. Our projections indicate an attack rate and a peak of new infections of 80.06% and 3.17% of the population, respectively, compared with the homogeneous model’s projections of 99.97% and 32.78%, thus providing an 18.6% improvement in the prediction accuracy based on the actual data. Our simulations demonstrate that heterogeneity in the susceptibles decreases herd immunity for ~37.36% of the population and prolongs the outbreak period from ~30 days to ~70 days, also aligning with the real case. We consider that this study lays the groundwork for the development of a new class of models and new insights for modelling heterogeneity.

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Age-specific transmission dynamics under suppression control measures during SARS-CoV-2 Omicron BA.2 epidemic

Cited by 3 publications

References 44 publications

Relationship of close contact settings with transmission and infection during the SARS-CoV-2 Omicron BA.2 epidemic in Shanghai

Relationship of close contact settings with transmission and infection during the SARS-CoV-2 Omicron BA.2 epidemic in Shanghai

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study

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