Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events
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Abstract
SARS-CoV-2 is difficult to contain because many transmissions occur during pre-symptomatic infection. Unlike influenza, most SARS-CoV-2-infected people do not transmit while a small percentage infect large numbers of people. We designed mathematical models which link observed viral loads with epidemiologic features of each virus, including distribution of transmissions attributed to each infected person and duration between symptom onset in the transmitter and secondarily infected person. We identify that people infected with SARS-CoV-2 or influenza can be highly contagious for less than 1 day, congruent with peak viral load. SARS-CoV-2 super-spreader events occur when an infected person is shedding at a very high viral load and has a high number of exposed contacts. The higher predisposition of SARS-CoV-2 toward super-spreading events cannot be attributed to additional weeks of shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks, likely due to aerosolization.
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Summary: This work assesses the role of within-host viral shedding dynamics and contact heterogeneity on distribution of transmission events in SARS-CoV-2 and influenza. Using multi-scale modeling, with similar resulting generation time and serial interval distributions to published work, predictions are made on the manner and contribution of super spreading to transmission. Distinctions are seen when comparing to applying a similar modeling framework to influenza.
Essential revisions:
Statistical analysis: The model parameters are estimated using an exhaustive grid search, which yields good fits for the best-fit values, but there is no assessment of statistical certainty in the parameter values. The authors essentially adopted a strategy in the spirit of approximate Bayesian computation (ABC), by proposing parameter values, simulating …
Summary: This work assesses the role of within-host viral shedding dynamics and contact heterogeneity on distribution of transmission events in SARS-CoV-2 and influenza. Using multi-scale modeling, with similar resulting generation time and serial interval distributions to published work, predictions are made on the manner and contribution of super spreading to transmission. Distinctions are seen when comparing to applying a similar modeling framework to influenza.
Essential revisions:
Statistical analysis: The model parameters are estimated using an exhaustive grid search, which yields good fits for the best-fit values, but there is no assessment of statistical certainty in the parameter values. The authors essentially adopted a strategy in the spirit of approximate Bayesian computation (ABC), by proposing parameter values, simulating from a model, and comparing summary statistics of the simulated output to known values from the literature. The analysis would be helped by doing a more formal ABC analysis, as this would provide a better sense of how narrowly constrained the parameter values are given the available data. At minimum, it would be more convincing to consider additional parameter sets gridded across a narrowed region of parameter space before selecting an optimal fit.
Model validation The state of our knowledge about these infections is limited, both by the short time during which this research has been conducted, and the paper's need to rely on data taken from before the introduction of confounding factors such as social distancing and widespread mask usage. For this reason, in addition to the included sensitivity analysis for the model parameters, a sense of the sensitivity of the model's conclusions to the data set to which it is being fitted is needed. How much would these results change if there are errors in our understanding of the distribution of individual R0 values, or serial intervals?
Distinction in assumptions for flu and covid The populations on which the histograms for the two diseases are based are quite different. For SARS-CoV-2, the studies are from China (Shenzhen, Tianjin and Hong Kong), while those for influenza are from Switzerland. Could cultural differences be relevant? What about seasonal differences, as the time during which the early SARS-CoV-2 studies occurred was necessarily restricted?
Furthermore, the explanation for the difference between influenza and COVID is based primarily on differences in contact patterns. While the discussion (L. 511-523) clarifies this to be based on the efficiency with which exposures lead to infections (and pre-symptomatic transmission), which does sound like a viral parameter, rather than a social one. These viral factors do seem more believable than having to explain why the patterns of social contact exhibited by influenza patients would differ from those of SARS-CoV-2 patients. More focus on possible mechanistic explanations is warranted.
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SciScore for 10.1101/2020.08.07.20169920: (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:Our analysis has important limitations. First, exposure contacts were assumed to be homogeneous and we do not capture the volume of the exposing aerosol or droplet. For instance, if a large-volume droplet contains ten times more viral particles than an aerosol droplet, then the exposure could be dictated by this volume as well as the …
SciScore for 10.1101/2020.08.07.20169920: (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:Our analysis has important limitations. First, exposure contacts were assumed to be homogeneous and we do not capture the volume of the exposing aerosol or droplet. For instance, if a large-volume droplet contains ten times more viral particles than an aerosol droplet, then the exposure could be dictated by this volume as well as the viral load of the potential transmitter. It is possible that under rare circumstances with extremely high-volume exposures, even persons with extremely low viral loads may transmit. Second, based on the quality of available data, we fit our models for SARS-CoV-2 and influenza to viral RNA and viral culture respectively. Existing data suggest that kinetics of viral RNA and culture are similar during both infections, with culture having lower sensitivity to detect virus.37 Third, our intra-host model of SARS-CoV-2 was fit to heterogeneous data with different sampling techniques and PCR assays.24 Moreover, R0 estimates have varied across the globe. Our estimates of TD50 are necessarily imprecise based on available data and should serve only as a conservative benchmark. Most importantly, we cannot rule out the possibility that a small minority of infected people shed at sufficient levels for transmission for much longer than has been observed to date. Fourth, contagiousness could have different dose response dynamics than viral load dependent infectiousness and may require investigation in the future upon the availability of epidemiologically relevan...
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.
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