Contact tracing data from the first COVID-19 pandemic wave in Shandong Province, China

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In a recent study published in the latest issue of the Epidemics, researchers evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission risks in different contact settings for prioritizing disease control from potential super-spreaders and their contacts.

Study: Variability in transmission risk of SARS-CoV-2 in close contact settings: A contact tracing study in Shandong Province, China. Image Credit: elenabsl/Shutterstock
Study: Variability in transmission risk of SARS-CoV-2 in close contact settings: A contact tracing study in Shandong Province, China. Image Credit: elenabsl/Shutterstock

Background

Contact tracing is a great tool to evaluate the relative transmissibility of SARS-CoV-2 across different settings, including households, healthcare, and air travel. When combined with an accurate assessment of heterogeneity of SARS-CoV-2 infectivity accounting for different duration of exposure at an individual level, it could predict the possibility of super-spreading events early to devise an appropriate intervention strategy.

Study design

In the present study, researchers applied an individual-based Bayesian transmission model to the contact tracing data from the first coronavirus disease 2019 (COVID-19) pandemic wave in Shandong Province, China. They estimated the secondary attack rates (SAR) in different contact settings and evaluated the potential risk factors for infection and transmission. SAR reflects a pathogen’s transmissibility via measuring one’s susceptibility to contracting a viral infection, including COVID-19 from an infectious person via close contact.

They collected demographic, clinical, and laboratory test data of symptomatic and asymptomatic SARS-CoV-2 infections (index cases) from municipal centers for disease control and prevention (CDC) of Jinan, Jining, and Qingdao cities in Shandong Province, China. They retrospectively retrieved the surveillance database for the study between January 22nd and May 30th, 2020.

During the study period, they identified reverse transcription-polymerase chain reaction (RT-PCR)-confirmed COVID-19 cases and traced their close contacts who were quarantined for 14 days. Additionally, the team collected their nasopharyngeal swab specimens on days 1, 4, 7, and 14 for RT-PCR testing.

As per the study case definitions, close contacts were people who had unprotected contact within one meter with a suspected or confirmed SARS-CoV-2 case within two days before symptom onset or, if it was asymptomatic, date of collection of the first RT-PCR-positive specimen. A close contact group may have multiple primary cases, referred to as co-primary cases.

First, the researchers calculated a crude SAR, the average of secondary cases among close contacts across all close contact groups with a single primary case. Assuming all secondary cases were infected by their primary case, they referred to it as the data-based SAR. However, not all secondary COVID-19 cases were infected by a single primary case, as there could be tertiary transmissions too, or infection could be acquired from an outside cluster. Fortunately, the Bayesian model accounted for and removed this discrepancy while predicting daily transmission dynamics.

The study covariates included age, sex, city, and occupation of each close contact, as well as disease severity of each case and symptom status during incubation and illness. The primary study findings considered a mean incubation period of five days and a maximum infectious period of 21 days.

Study findings

Among 97 lab-confirmed index cases included in the transmission modeling analysis, there were eight clusters with a single primary case and two co-primary cases; additionally, there were 3158 close contacts in the remaining 81 clusters. The average size of these 89 clusters of close contact groups was 23. Overall, on average, a primary case generated 1.05 secondary cases in the Shandong Province.

The female to male ratio among primary cases and secondary cases was 65% vs. 35% and 59% vs. 41%, respectively. Compared to secondary cases, primary cases were more likely to be severe (14% vs. 5%) and less likely to be asymptomatic (6% vs. 10%).

The overall data-based SAR was 3.53%; additionally, it was the highest (8.64%) among close contacts over 60 years. Among the contact settings examined during the study, the household was associated with the highest data-based SAR of 10.1%, whereas air transportation was associated with the lowest, 0.43%.

The estimated daily transmission probability of infected individuals to pass on the infection to close contacts during their incubation period was 0.044 within households, 0.032 in healthcare facilities, 0.023 at workplaces, 0.004 during air travel, and 0.002 in all other settings. It was slightly lower during the illness period, and the estimated relative infectivity was the highest when both the incubation and the infectious period were longer.

Further, the multivariate analysis revealed that:

i) close contacts younger than 60 years had 36-49% lower odds of infection than those over 60;

ii) medical professionals were 65% less likely to be infected than non-medical contacts;

iii) the odds of infection was much lower during air travel, odds ratio (ORs) 0.08 compared to within households; and

iv) the risk of infection in workplaces and healthcare facilities was slightly lower, with ORs of 0.52 and 0.73, respectively.

The study model predicted the possibility of superspreading events in two households and one healthcare facility (three close contact groups), each with a single primary case.

Conclusions

To summarize, the estimated household SARs in Shandong, calculated for the whole infectious period of 22 days, were higher than most household transmission studies in China. SARS-CoV-2 was more transmissible in household settings than in workplaces and healthcare facilities, and the transmission risk was much less during air travel or in other contact settings. In addition to the highest SAR, the household settings favored the highest number of secondary cases per primary case. Additionally, age, the medical profession, and city were risk modifiers.

Simulating transmission dynamics among close contacts of primary cases, assuming a mean incubation of five days and a maximum infectious period of 22 days, showed that 64% of cases did not generate secondary transmissions, and 20% cases resulted in 80% of secondary transmissions.

To conclude, the study highlights that vaccination and non-pharmaceutical interventions should be prioritized for large-close contact groups in household settings. While transmission risk was lower during air travel, considering viral exposure among so many flight passengers and the implication for long-distance dissemination warrants prevention efforts in this setting as well.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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