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

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.

The unmitigated profile of COVID-19 infectiousness (preprint)

Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of the COVID-19 pandemic and for analyzing the effectiveness of different mitigation strategies. Many studies have tried to use data from the onset of symptoms of infector-infectee pairs to estimate the infectiousness profile of SARS-CoV-2. However, both statistical and epidemiological biases in the data could lead to an underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation steps were still minimal), and find that the infectiousness profile is wider than previously thought. For example, our estimate for the proportion of transmissions occurring 14 days or more after infection is an order of magnitude higher - namely 19% (95% CI 10%-25%). The inferred generation interval distribution is sensitive to the definition of the period of unmitigated transmission, but estimates that rely on later periods are less reliable due to intervention effects. Nonetheless, the results are robust to other factors such as the model, the assumed growth rate and possible bias of the dataset. Knowing the unmitigated infectiousness profile of infected individuals affects estimates of the effectiveness of self-isolation and quarantine of contacts. The framework presented here can help design better quarantine policies in early stages of future epidemics using data from the initial stages of transmission.

The total number and mass of SARS-CoV-2 virions in an infected person (preprint)

Quantitatively describing the time course of the SARS-CoV-2 infection within an infected individual is important for understanding the current global pandemic and possible ways to combat it. Here we integrate the best current knowledge about the abundance of potential SARS-CoV-2 host cells and typical concentrations of virions in bodily fluids to estimate the total number and mass of SARS-CoV-2 virions in an infected person. We estimate that each infected person carries 109-1011 virions during peak infection, with a total mass of about 1 {micro}g-0.1 mg, which curiously implies that all SARS-CoV-2 virions currently in the world have a mass of only 0.1-1 kg. Knowledge of the absolute number of virions in an infected individual can put into perspective parameters of the immune system response, minimal infectious doses and limits of detection in testing.

Quantitative clarification of key questions about COVID-19 epidemiology (preprint)

Modeling the spread of COVID-19 is crucial for informing public health policy. All models for COVID-19 epidemiology rely on parameters describing the dynamics of the infection process. The meanings of epidemiological parameters like R_0, R_t, the "serial interval" and "generation interval" can be challenging to understand, especially as these and other parameters are conceptually overlapping and sometimes confusingly named. Moreover, the procedures used to estimate these parameters make various assumptions and use different mathematical approaches that should be understood and accounted for when relying on parameter values and reporting them to the public. Here, we offer several insights regarding the derivation of commonly-reported epidemiological parameters, and describe how mitigation measures like lockdown are expected to affect their values. We aim to present these quantitative relationships in a manner that is accessible to the widest audience possible. We hope that better communicating the intricacies of epidemiological models will improve our collective understanding of their strengths and weaknesses, and will help avoid possible pitfalls when using them.

A quantitative compendium of COVID-19 epidemiology (preprint)

Accurate numbers are needed to understand and predict viral dynamics. Curation of high-quality literature values for the infectious period duration or household secondary attack rate, for example, is especially pressing currently because these numbers inform decisions about how and when to lockdown or reopen societies. We aim to provide a curated source for the key numbers that help us understand the virus driving our current global crisis. This compendium focuses solely on COVID-19 epidemiology. The numbers reported in summary format are substantiated by annotated references. For each property, we provide a concise definition, description of measurement and inference methods, and associated caveats. We hope this compendium will make essential numbers more accessible and avoid common sources of confusion for the many newcomers to the field such as using the incubation period to denote and quantify the latent period or using the hospitalization duration for the infectiousness period duration. This document will be repeatedly updated and the community is invited to participate in improving it.