Last, we implemented a within-host computational model based on ordinary differential equations aiming to evaluate different scenarios regarding treatment efficacy during the first days of the acute infection before adaptive immunity is developed. We formulated a basic frequency-dependent model which allows evaluation of antivirals and has been shown to mimic viral dynamics in the human host for a wide range of viruses [23]. In this model, the viral population is controlled by depletion of target (susceptible) cells. For simplicity the main model does not assume a significant effect of multiplicity of infection on the viral dynamics [24], nor includes proliferation and death of target cells given the time scale of the analysis. We focus on evaluating the viral population until an efficacious adaptive response is developed, which we assumed to happen around 10 days after infection [25].  Using this model, we evaluated the impact of SARS-CoV-2 infection by estimating the proportion of “tissue” directly damaged by viral infection (as proportion of infected cells from total targeted) while assuming a correlation with disease severity, in the following scenarios: a) infection under preexposure prophylaxis with an antiviral with high inhibition and optimal tissue penetration b)  a) infection under preexposure prophylaxis with an antiviral with high inhibition and moderate penetration versus moderate inhibition and high penetration, c) infection under preexposure prophylaxis with an antiviral with high inhibition and existing innate immunity, and d) natural infection and consequent treatment with moderate and high antiviral inhibition and full tissue penetration beginning 24h and 48h before viral load peak. Details on the model formulation and inclusion of immune response can be found in the Supplementary Material Section S5.