The unmitigated profile of COVID-19 infectiousness

Abstract
Data availability
Article and author information
Metrics

Abstract

Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to 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 was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOC), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics.

Data availability

All study data are included in the article, SI appendix, and Dataset S1.All code is available in Jupyter notebooks found inhttps://gitlab.com/milo-lab-public/the-unmitigated-profile-of-covid-19-infectiousness

The following previously published data sets were used

1. Ali et al
(2020) PDGLin/COVID19_EffSerialInterval_NPI: Serial interval of SARS-CoV-2 was shortened over time by non-pharmaceutical interventions
zenodo.3940300.

http://doi.org/10.5281/zenodo.3940300
1. Ganyani et al
(2020) Estimating the generation interval for COVID-19
github files.

https://github.com/cecilekremer/COVID19
1. He et al
(2020) Supplementary Data 1
Supplementary Data 1.

https://www.nature.com/articles/s41591-020-0869-5#Sec9
1. Liao et al
(2020) Figure 2
Figure 2.

http://dx.doi.org/10.1016/j.xinn.2020.04.001
1. Li et al
(2020) Figure 3. Detailed Information on Exposures and Dates of Illness Onset in Five Clusters Including 16 Cases.
Figure 3.

http://dx.doi.org/10.1056/NEJMoa2001316
1. Zhang et al
(2020) Table S12. Raw data of clusters and cluster cases of COVID-19 used to estimate the serial interval
supplementaryMaterial.

https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30230-9/fulltext#supplementaryMaterial
1. Ren et al
(2021) Transmission events used to infer the occurrence of pre-symptomatic transmission.
Figure 3.

http://dx.doi.org/10.1111/irv.12787
1. Wu et al
(2020) Supplementary Table 8. Dates of symptom onset of infector-infectee pairs
Supplementary Table 8. Dates of symptom onset of infector-infectee pairs.

http://dx.doi.org/10.1038/s41591-020-0822-7
1. Xia et al
(2020) Characteristics of the cases in the 50 clusters
Table 2.

http://dx.doi.org/10.1101/2020.03.06.20031955
1. Yang et al
(2020) COVID19_SHIYAN
zenodo.3898225.

http://doi.org/10.5281/zenodo.3898225.
1. Zhanwei Due et al
(2020) Table S5
Table S5.

https://github.com/MeyersLabUTexas/COVID-19
1. Zhao et al
(2020) Zhao et al. supplementary material 1
Zhao et al. supplementary material 1.

http://dx.doi.org/10.1017/ice.2020.64

Article and author information

Author details

Ron Sender

Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel

Competing interests
The authors declare that no competing interests exist.
Yinon Bar-On

Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel

Competing interests
The authors declare that no competing interests exist.
Sang Woo Park

Department of Ecology and Evolutionary, Princeton University, Princeton, United States

Competing interests
The authors declare that no competing interests exist.
Elad Noor

Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-8776-4799
Jonathan Dushoff

Department of Biology, McMaster University, Hamilton, Canada

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-0506-4794
Ron Milo

Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel

For correspondence
ron.milo@weizmann.ac.il

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-1641-2299

Funding

Weizmann Institute of Science (The Weizmann CoronaVirus Fund)

Ron Milo

Weizmann Institute of Science (Weizmann Data Science Research Center,and by a research grant from the Estate of Tully and Michele)

Ron Sender

Canadian Institute for Health Research

Jonathan Dushoff

Ben B. and Joyce E. Eisenberg Foundation

Ron Milo

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.