Favipiravir for Treatment of Outpatients With Asymptomatic or Uncomplicated Coronavirus Disease 2019: A Double-Blind, Randomized, Placebo-Controlled, Phase 2 Trial.

BACKGROUND: Favipiravir, an oral, RNA-dependent RNA polymerase inhibitor, has in vitro activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite limited data, favipiravir is administered to patients with coronavirus disease 2019 (COVID-19) in several countries. METHODS: We conducted a phase 2, double-blind, randomized controlled outpatient trial of favipiravir in asymptomatic or mildly symptomatic adults with a positive SARS-CoV-2 reverse-transcription polymerase chain reaction assay (RT-PCR) within 72 hours of enrollment. Participants were randomized to receive placebo or favipiravir (1800 mg twice daily [BID] day 1, 800 mg BID days 2-10). The primary outcome was SARS-CoV-2 shedding cessation in a modified intention-to-treat (mITT) cohort of participants with positive enrollment RT-PCRs. Using SARS-CoV-2 amplicon-based sequencing, we assessed favipiravir's impact on mutagenesis. RESULTS: We randomized 149 participants with 116 included in the mITT cohort. The participants' mean age was 43 years (standard deviation, 12.5 years) and 57 (49%) were women. We found no difference in time to shedding cessation overall (hazard ratio [HR], 0.76 favoring placebo [95% confidence interval {CI}, .48-1.20]) or in subgroups (age, sex, high-risk comorbidities, seropositivity, or symptom duration at enrollment). We detected no difference in time to symptom resolution (initial: HR, 0.84 [95% CI, .54-1.29]; sustained: HR, 0.87 [95% CI, .52-1.45]) and no difference in transition mutation accumulation in the viral genome during treatment. CONCLUSIONS: Our data do not support favipiravir at commonly used doses in outpatients with uncomplicated COVID-19. Further research is needed to ascertain if higher favipiravir doses are effective and safe for patients with COVID-19. CLINICAL TRIALS REGISTRATION: NCT04346628.

June: open-source individual-based epidemiology simulation.

We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.