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. 2021 Sep 27:10:e69302.
doi: 10.7554/eLife.69302.

Quantifying the relationship between SARS-CoV-2 viral load and infectiousness

Aurélien Marc  1 , Marion Kerioui  1 , François Blanquart  1   2 , Julie Bertrand  1 , Oriol Mitjà  3   4 , Marc Corbacho-Monné  3   5   6 , Michael Marks  7   8   9 , Jeremie Guedj  1
Affiliations

Quantifying the relationship between SARS-CoV-2 viral load and infectiousness

Aurélien Marc et al. Elife. .

Abstract

The relationship between SARS-CoV-2 viral load and infectiousness is poorly known. Using data from a cohort of cases and high-risk contacts, we reconstructed viral load at the time of contact and inferred the probability of infection. The effect of viral load was larger in household contacts than in non-household contacts, with a transmission probability as large as 48% when the viral load was greater than 1010 copies per mL. The transmission probability peaked at symptom onset, with a mean probability of transmission of 29%, with large individual variations. The model also projects the effects of variants on disease transmission. Based on the current knowledge that viral load is increased by two- to eightfold with variants of concern and assuming no changes in the pattern of contacts across variants, the model predicts that larger viral load levels could lead to a relative increase in the probability of transmission of 24% to 58% in household contacts, and of 15% to 39% in non-household contacts.

Keywords: SARS-CoV-2; computational biology; epidemiology; human; infectious disease; infectious diseases; microbiology.

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Conflict of interest statement

AM, MK, FB, JB, OM, MC, MM, JG No competing interests declared

Figures

Figure 1.
Figure 1.. Individual fits of viral dynamics in index cases and occurrence of high-risk contacts.
Black dots represent the measured viral load. Squares indicate documented high-risk contacts, with empty squares representing contacts without transmission, and red squares representing contacts with a subsequent infection. Results obtained in the 41 index cases having three viral load measurements.
Figure 1—figure supplement 1.
Figure 1—figure supplement 1.. Flow chart of data selection.
Figure 1—figure supplement 2.
Figure 1—figure supplement 2.. Distribution of contacts.
Distribution of the times at which a high-risk contact occurred between an index and a contact, with reference to the time of symptom onset of the index case. This indicates that most contacts reported in the study occurred at the time of symptom onset of the index, and that many contacts occurred during the pre-symptomatic period of the index. All contacts (Left). Household contacts (Middle). Non-household contacts (Right).
Figure 2.
Figure 2.. Model-based predictions of the effect of viral load on the risk of transmission and comparison to observed data.
Bars represent the mean predicted probability of transmission obtained from 1000 simulations of the model M2 and stratified by viral load level at the time of contact. Black dots are the proportion of transmission events observed in the data stratified by the predicted viral load of the index cases at the time of contact (along with their 95% confidence intervals). Household contacts (Left). Non-household contacts (Right).
Figure 3.
Figure 3.. Model-based predictions of the dynamics of viral load and infectiousness over time.
Prediction interval of the viral load (black) and the probability of transmission over time after a high-risk contact obtained from 1000 simulations of the model. The shaded area represents the 90% inter quantile range. Household contacts (Left). Non-household contacts (Right).
Figure 4.
Figure 4.. Effects of variants of concern and vaccination on transmission for different distributions of contacts.
(A) We considered a rate of contacts that could either decline after 5 days (top) or remain constant for the whole considered period (bottom). (B) Distribution of the generation interval using model M2 under each scenario. (C) Impact of changes in viral production on the average probability of transmission. (D) Impact of changes in viral production on the relative change from the baseline scenario in model M2.
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. Effect of changes in the viral production on the viral load and transmission probabilities trajectories.
Effect of variants of concern (Top). Effect of vaccination rollout (Bottom). We only displayed the median trajectories over the simulated individuals. Viral load (Black). Transmission probability for household contacts (Blue). Transmission probability for non-household contacts (Orange).
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. Impact of changes in the viral production rate on the average probability of transmission with model M3.
Using a time-varying distribution of contacts (Top) or a constant distribution of contacts (Bottom).
See this image and copyright information in PMC

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