Household transmission modeling qualified variation of individual infectiousness among infected persons, which could be caused by both biological factors and host behaviors.
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4) Additional work should be done to help disentangle differences between biological or behavioral factors since this will greatly change the interpretation of inferred parameters. In addition, more contextual information should be provided should be done.Tsang et al. evaluate the degree of transmission heterogeneity among COVID-19 cases using data compiled from 17 household transmission studies.
Overall, the study design is sound and the analysis of multiple datasets is a strength. However, the modeling approach and the biological interpretation/significance of the results could be more clearly explained. Also, given the large number of individual-level parameters estimated, it would be ideal to see a direct assessment of parameter identifiability , and model validation on simulated data .
The study has several strengths. First, by choosing analyzing data from the household study, the authors were able to control for several key factors contributing to the overall transmission heterogeneity but not variation in individual infectiousness, including the number of contacts as well the setting of transmission .
Through the formulation of the household transmission model, the authors essentially assume a lognormal distribution of individual infectiousness, which is a long-tailed distribution by nature. I think this is an important point that needs to be highlighted.
We have added the correlation plots for the posterior distribution of parameters, and also rerun a longer MCMC chain to ensure that our estimates of infectiousness variations are valid. Please see Response 7 for more details. 4) Additional work should be done to help disentangle differences between biological or behavioral factors since this will greatly change the interpretation of inferred parameters. In addition, more contextual information should be provided should be done.
Although the study focuses on the household size as a confounder of heterogeneity in infectiousness, the ways that the model controls for household size and the interpretation of the inferred heterogeneity parameter, σ_var could be explained more clearly. Furthermore, it seems that basic empirical relationships between individual infectiousness and household size are not explored.
We conducted a simulation study, and found there is no important systematic bias, and that 88-100% of 95% credible interval covered the simulation value. We also added the correlation plots in the revised manuscript. The correlation between parameters are moderate. We run long MCMC chain to ensure our results are valid. We conduct a sensitivity by running the MCMC chain with 500,000 iterations and the results are similar to the one with 100,000 iterations.
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