Transmission roles of symptomatic and asymptomatic COVID-19 cases: a modelling study
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Abstract
Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9–80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0–34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.
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SciScore for 10.1101/2021.05.11.21257060: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations in this study. First, data collection likely missed potential cases of the epidemic, despite intensified efforts devoted by the local investigation team to trace contacts. Due to this, we introduced a compartment in our model to adjust for poor case ascertainment and missing cases. Second, transmissibility …
SciScore for 10.1101/2021.05.11.21257060: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations in this study. First, data collection likely missed potential cases of the epidemic, despite intensified efforts devoted by the local investigation team to trace contacts. Due to this, we introduced a compartment in our model to adjust for poor case ascertainment and missing cases. Second, transmissibility and susceptibility were two factors related to symptomatic and asymptomatic transmission estimation and can be difficult to capture simultaneously. We used the susceptibility estimates from a previous study21 as priors in our model to account for this parameter identification problem. Third, the contact survey data we used in our model were obtained in Shanghai, a city adjacent to Zhejiang province. Although the two regions share a similar culture and modes of social activities, there were potential uncertainties associated with the discrepancies in contact matrices. To address this limitation, we introduced a correction parameter in our model, so these uncertainties were partially adjusted for in the analyses. In summary, our results suggest individual-level transmissibility of COVID-19 increases with patient age, therefore targeting older age groups with prevention and intervention strategies is expected to be more efficient. While asymptomatic cases are difficult to trace, the burden of asymptomatic transmission is still sizable and should not be ignored. The results from our study can be used to inform policy decisions on pandemic control a...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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