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Author summary The generation and serial intervals are epidemiological quantities used to describe and predict an ongoing epidemic outbreak. These quantities are related to the contact pattern of individuals, since infection events can take place if infectious and susceptible individuals have a contact. Therefore, intervention measures that reduce the interactions between members of the population are expected to affect both the realized generation and serial intervals. For the current COVID-19 pandemic unprecedented interventions have been adopted worldwide, e.g. strict lockdown, isolation and quarantine, which influence the realized value of generation and serial intervals. The extent of the effect thereof depends on the efficacy of the control measure in place, on the relationship between symptom onset and infectiousness and on the proportion of infectious individuals that can be detected. To get more insight on this, we present an investigation that highlights the effect of quarantine and isolation on realized generation and serial intervals. In particular, we show that not only their variances but also their mean values can differ, suggesting that the use of the mean serial interval as a proxy for the mean generation time can lead to biased estimates of epidemiological quantities. The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation time. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof. ; A.T. acknowledges support from the special research fund of the University of Antwerp. This work also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (NH, AT: grant number 682540 - TransMID project; NH, PL: grant number 101003688 - EpiPose project). P.L. gratefully acknowledges support from the Fonds voor Wetenschappelijk Onderzoek (FWO) via postdoctoral fellowship 1242021N. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Torneri, A (corresponding author), Univ Antwerp, Ctr Hlth Econ Res & Modelling Infect Dis, Antwerp, Belgium ; Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Data Sci Inst, Hasselt, Belgium. andrea.torneri@uantwerp.be

Background: Current outbreaks of COVID-19 are threatening the health care systems of several countries around the world. Control measures, based on isolation, contact tracing, and quarantine, can decrease and delay the burden of the ongoing epidemic. With respect to the ongoing COVID-19 epidemic, recent modeling work shows that these interventions may be inadequate to control local outbreaks, even when perfect isolation is assumed. The effect of infectiousness prior to symptom onset combined with asymptomatic infectees further complicates the use of contact tracing. We aim to study whether antivirals, which decrease the viral load and reduce infectiousness, could be integrated into control measures in order to augment the feasibility of controlling the epidemic. Methods: Using a simulation-based model of viral transmission, we tested the efficacy of different intervention measures to control local COVID-19 outbreaks. For individuals that were identified through contact tracing, we evaluate two procedures: monitoring individuals for symptoms onset and testing of individuals. Additionally, we investigate the implementation of an antiviral compound combined with the contact tracing process. Results: For an infectious disease in which asymptomatic and presymptomatic infections are plausible, an intervention measure based on contact tracing performs better when combined with testing instead of monitoring, provided that the test is able to detect infections during the incubation period. Antiviral drugs, in combination with contact tracing, quarantine, and isolation, result in a significant decrease of the final size and the peak incidence, and increase the probability that the outbreak will fade out. Conclusion: In all tested scenarios, the model highlights the benefits of control measures based on the testing of traced individuals. In addition, the administration of an antiviral drug, together with quarantine, isolation, and contact tracing, is shown to decrease the spread of the epidemic. This control measure could be an effective strategy to control local and re-emerging outbreaks of COVID-19. ; This work is funded by the EpiPose project from the European Union's SC1-PHE-CORONAVIRUS-2020 programme, project number 101003688, by the European Union's Horizon 2020 research and innovation programme (grant agreement 682540-TransMID), the European Union's Horizon 2020 research and innovation program under grant agreement no. 101003627 (SCORE project), and the Stichting Antoine Faes. A.T. acknowledges support from the special research fund of the University of Antwerp. P.L. was supported by funding from the Flemish Government under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen" programme. ; Hens, N (corresponding author), Univ Antwerp, Ctr Hlth Econ Res & Modelling Infect Dis, Antwerp, Belgium; Hasselt Univ, Interuniv Inst Biostatist & Stat Bioinformat, Data Sci Inst, Hasselt, Belgium. niel.hens@uhasselt.be

Background: Estimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies. Aim: We estimate the generation interval, serial interval, proportion of presymptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased. Methods: We used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates, we obtained the serial interval, proportions of pre-symptomatic transmission and reproduction numbers. Results: The mean generation interval was 5.20 days (95% credible interval (Crl): 3.78-6.78) for Singapore and 3.95 days (95% Crl: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% Crl: 32-67) for Singapore and 62% (95% Crl: 50-76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information. Conclusion: High estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals. ; NH acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement 682540 - TransMID). CF, NH and JW acknowledge funding from the European Union's Horizon 2020 research and innovation programme (project EpiPose No 101003688). ; Ganyani, T (corresponding author), Hasselt Univ, Data Sci Inst, I BioStat, Hasselt, Belgium. tapiwa.ganyani@uhasselt.be