We modeled the viral dynamics of 13 untreated patients infected with severe acute respiratory syndrome‐coronavirus 2 to infer viral growth parameters and predict the effects of antiviral treatments. In order to reduce peak viral load by more than two logs, drug efficacy needs to be > 90% if treatment is administered after symptom onset; an efficacy of 60% could be sufficient if treatment is initiated before symptom onset. Given their pharmacokinetic/pharmacodynamic properties, current investigated drugs may be in a range of 6–87% efficacy. They may help control virus if administered very early, but may not have a major effect in severely ill patients.

word count: 100/100) 38We modeled the viral dynamics of 13 untreated patients infected with SARS-CoV-2 to infer 39 viral growth parameters and predict the effects of antiviral treatments. In order to reduce peak 40 viral load by more than 2 logs, drug efficacy needs to be greater than 80% if treatment is 41 administered after symptom onset; an efficacy of 50% could be sufficient if treatment is 42 initiated before symptom onset. Given their pharmacokinetic/pharmacodynamic properties, 43 current investigated drugs may be in a range of 20-70% efficacy. They may help control virus 44 if administered very early, but may not have a major effect in severe patients. 45 46 All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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.