Estimating effective reproduction number using generation time versus serial interval, with application to COVID-19 in the Greater Toronto Area, Canada
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Abstract
B ackground
The effective reproductive number R e ( t ) is a critical measure of epidemic potential. R e ( t ) can be calculated in near real time using an incidence time series and the generation time distribution—the time between infection events in an infector-infectee pair. In calculating R e ( t ), the generation time distribution is often approximated by the serial interval distribution—the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid -19, rendering such an approximation improper in some contexts.
M ethods
We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of R e ( t ) for covid -19 in the Greater Toronto Area of Canada using: negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution.
R esults
We estimated the generation time of covid -19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of R e ( t ) due to larger mean, while negative-permitting serial interval distribution caused underestimation of R e ( t ) due to larger variance.
I mplications
Approximation of the generation time distribution of covid -19 with non-negative or negative-permitting serial interval distributions when calculating R e ( t ) may result in over or underestimation of transmission potential, respectively.
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SciScore for 10.1101/2020.05.24.20109215: (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
Software and Algorithms Sentences Resources Given I(t) and G(τ), probabilistic estimates of Re(t) can then be resolved in a Bayesian framework, as implemented in the EpiEstim R package. EpiEstimsuggested: (EpiEstim, RRID:SCR_018538)We then compared estimates of Re(t) using the mle generation time distribution versus serial interval distributions reported in the literature, including negative-permitting (Normal [11]), and non-negative (Gamma [12], Log-Normal [20]) distributions. Gammasuggested: (GAMMA, RRID:SCR_009484)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following …SciScore for 10.1101/2020.05.24.20109215: (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
Software and Algorithms Sentences Resources Given I(t) and G(τ), probabilistic estimates of Re(t) can then be resolved in a Bayesian framework, as implemented in the EpiEstim R package. EpiEstimsuggested: (EpiEstim, RRID:SCR_018538)We then compared estimates of Re(t) using the mle generation time distribution versus serial interval distributions reported in the literature, including negative-permitting (Normal [11]), and non-negative (Gamma [12], Log-Normal [20]) distributions. Gammasuggested: (GAMMA, RRID:SCR_009484)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our approach to estimating generation time had three notable limitations. First, like similar works [13, 14], we assumed that generation time and incubation period were independent, although Klinkenberg et al. [16] showed that correlation between the two exist in infections such as measles. Second, as noted above, our approach did not use person-level serial interval or incubation period data such as in [13] and [16], possibly resulting in compounding errors from parametric approximation of both input (serial interval, incubation period) and output (generation time) distributions. However, depending on the availability and reliability of person-level data, our approach could in some cases be favourable. Finally, we did not perform uncertainty analysis using the reported confidence intervals for the serial interval and incubation period distribution parameters. Future work could overcome this limitation by exploring joint estimation of generation time, serial interval, and Re(t) within the Bayesian framework defined in [1].
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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