Monitoring key epidemiological parameters of SARS-CoV-2 transmission

Mug., Kraemer; Pybus, Oliver G.; Fraser, Christophe; Cauchemez, Simon; Rambaut, Andrew; Cowling, BJ

doi:10.1038/s41591-021-01545-w

Cited by 33 publications

(30 citation statements)

References 10 publications

(6 reference statements)

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“… Data One of the most time-consuming aspects of developing and applying the analytical framework involved locating and processing relevant data sources on mobility, policy response, epidemiology, and so on. As has been recognised elsewhere ( Kraemer et al, 2021 ), streamlining the consolidation, standardising and sharing of epidemiological data will play a vital role on supporting rapid-response analysis in the event of future pandemics. Timeliness Decision-making in the early stages of a pandemic is time-critical.…”

Section: Discussionmentioning

confidence: 98%

Rapid assessment of the risk of SARS-CoV-2 importation: case study and lessons learned

et al. 2022

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Section: Discussionmentioning

confidence: 98%

Rapid assessment of the risk of SARS-CoV-2 importation: case study and lessons learned

et al. 2022

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“…Since the end of 2019, COVID-19 continuously posed threat to public health globally [ 19 ]. The novel genetic mutations of SARS-CoV-2 have continually challenged the control system for the COVID-19 pandemic, making it critical to monitor key epidemiological parameters for understanding the transmission and clinical characteristics of emerging variants [ 6 , 10 ]. The incubation period is defined as the time interval between exposure and onset of illness for symptomatic infections [ 13 ], which is important to inform quarantine policies, to study transmission dynamics of an infectious disease, and to assess the effectiveness of entry screening [ 11 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating the incubation period of SARS-CoV-2 Omicron BA.1 variant in comparison with that during the Delta variant dominance in South Korea

et al. 2022

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“…Monitoring changes in key epidemiological parameters is critical to understanding the evolution of SARS-CoV-2 and predicting its future dynamics [38]. Our study synthesizes a previously developed theoretical framework on serial- and generation-interval distributions and presents methodological advances in monitoring epidemiological parameters.…”

Section: Discussionmentioning

confidence: 99%

Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2

Park

Sun

Abbott

et al. 2022

Preprint

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Estimating the differences in the incubation-period, serial-interval, and generation-interval distributions of SARS-CoV-2 variants is critical to understanding their transmission and control. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection and transmission—for example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we re-analyze incubation-period and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same data set reported shorter mean observed incubation period (3.2 days vs 4.4 days) and serial interval (3.5 days vs 4.1 days) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased. When we account for growth-rate differences of two variants during the study period, we estimate similar mean incubation periods (3.8–4.5 days) for both variants but a shorter mean generation interval for the Omicron variant (3.0 days; 95% CI: 2.7–3.2 days) than for the Delta variant (3.8 days; 95% CI: 3.7–4.0 days). We further note that the differences in estimated generation intervals may be driven by the “network effect”—higher effective transmissibility of the Omicron variant can cause faster susceptible depletion among contact networks, which in turn prevents late transmission (therefore shortening realized generation intervals). Using up-to-date generation-interval distributions is critical to accurately estimating the reproduction advantage of the Omicron variant.SignificanceRecent studies suggest that individuals infected with the Omicron variant develop symptoms earlier (shorter incubation period) and transmit faster (shorter generation interval) than those infected with the Delta variant. However, these studies typically neglect population-level effects: when an epidemic is growing, a greater proportion of current cases were infected recently, biasing us toward observing faster transmission events. Accounting for this dynamical bias, we find that Omicron infections from the Netherlands at the end of December 2021 had similar incubation periods, but shorter generation intervals, compared to Delta infections from the same period. Shorter generation intervals of the Omicron variant might be due to its higher effective reproduction number, which can cause faster local susceptible depletion around the contact network.

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Monitoring key epidemiological parameters of SARS-CoV-2 transmission

Cited by 33 publications

References 10 publications

Rapid assessment of the risk of SARS-CoV-2 importation: case study and lessons learned

Rapid assessment of the risk of SARS-CoV-2 importation: case study and lessons learned

Estimating the incubation period of SARS-CoV-2 Omicron BA.1 variant in comparison with that during the Delta variant dominance in South Korea

Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2

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