Influenza viruses annually kill 290,000-650,000 people worldwide. Antivirals can reduce death tolls. Baloxavir, the recently approved influenza antiviral, inhibits initiation of viral mRNA synthesis, whereas oseltamivir, an older drug, inhibits release of virus progeny. Baloxavir blocks virus replication more rapidly and completely than oseltamivir, reducing the duration of infectiousness. Hence, early baloxavir treatment may indirectly prevent transmission. Here, we estimate impacts of ramping up and accelerating baloxavir treatment on population-level incidence using a new model that links viral load dynamics from clinical trial data to between-host transmission. We estimate that~22 million infections and >6,000 deaths would have been averted in the 2017-2018 epidemic season by administering baloxavir to 30% of infected cases within 48 h after symptom onset. Treatment within 24 h would almost double the impact. Consequently, scaling up early baloxavir treatment would substantially reduce influenza morbidity and mortality every year. The development of antivirals against the SARS-CoV2 virus that function like baloxavir might similarly curtail transmission and save lives.

Background Pandemic SARS-CoV-2 was first reported in Wuhan, China on December 31, 2019. Twenty-one days later, the US identified its first case––a man who had traveled from Wuhan to the state of Washington. Recent studies in the Wuhan and Seattle metropolitan areas retrospectively tested samples taken from patients with COVID-like symptoms. In the Wuhan study, there were 4 SARS-CoV-2 positives and 7 influenza positives out of 26 adults outpatients who sought care for influenza-like-illness at two central hospitals prior to January 12, 2020. The Seattle study reported 25 SARS-CoV-2 positives and 442 influenza positives out of 2353 children and adults who reported acute respiratory illness prior to March 9, 2020. Here, we use these findings to extrapolate the early prevalence of symptomatic COVID-19 in Wuhan and Seattle. Methods For each city, we estimate the ratio of COVID-19 to influenza infections from the retrospective testing data and estimate the age-specific prevalence of influenza from surveillance reports during the same time period. Combining these, we approximate the total number of symptomatic COVID-19 infections. Findings In Wuhan, there were an estimated 1386 [95% CrI: 420-3793] symptomatic cases over 30 of COVID-19 between December 30, 2019 and January 12, 2020. In Seattle, we estimate that 2268 [95% CrI: 498, 6069] children under 18 and 4367 [95% CrI: 2776, 6526] adults were symptomatically infected between February 24 and March 9, 2020. We also find that the initial pandemic wave in Wuhan likely originated with a single infected case who developed symptoms sometime between October 26 and December 13, 2019; in Seattle, the seeding likely occurred between December 25, 2019 and January 15, 2020. Interpretation The spread of COVID-19 in Wuhan and Seattle was far more extensive than initially reported. The virus likely spread for months in Wuhan before the lockdown. Given that COVID-19 appears to be overwhelmingly mild in children, our high estimate for symptomatic pediatric cases in Seattle suggests that there may have been thousands more mild cases at the time.

Before January 22, 2020, only one pediatric case of COVID-19 was reported in mainland China. However, a retrospective surveillance study identified six children who had been hospitalized for COVID-19 in one of three central Wuhan hospitals between January 7th and January 15th. Given that Wuhan has over 395 other hospitals, there may have been far more severe pediatric cases than reported. There were six and 43 children out of 336 who tested positive for COVID-19 and influenza, respectively among all pediatric admissions during the 9-day period. By using this ratio in a detailed analysis of influenza surveillance data and COVID-19 epidemic dynamics (see Appendix), we estimate that there were 313 [95% CI: 171-520] children hospitalized for COVID-19 in Wuhan during January 7-15, 2020 (Figure). Under an epidemic doubling time of 7.31 days4, we estimate that there were 1105 [95% CI: 592, 1829] cumulative pediatric COVID-19 hospitalizations prior to the January 23rd lockdown, which far surpasses the 425 confirmed cases reported across all age groups, none of which were children under age 15. Children are strikingly absent from COVID-19 reports and limited data suggest that pediatric infections are overwhelmingly mild5. Thus, our estimates for hundreds of severe pediatric cases likely translates to thousands or even tens of thousands of mildly infected children, suggesting that the force of infection from children may be grossly underestimated and the infection fatality rate overestimated from confirmed case counts alone. This highlights the urgent need for more robust surveillance to gauge the true extent and severity of COVID-19 in all ages.

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about subpopulations with significantly distinct treatment effects. The discussion mainly focuses on inference for a benefiting subpopulation, that is, a characterization of a group of patients who benefit from the treatment under consideration more than the overall population. We introduce alternative approaches and demonstrate them with a small simulation study. Then, we turn to clinical trial designs. When the selection of the interesting subpopulation is carried out as the trial proceeds, the design becomes an adaptive clinical trial design, using subgroup analysis to inform the randomization and assignment of treatments to patients. We briefly review some related designs. There are a variety of approaches to Bayesian subgroup analysis. Practitioners should consider the type of subpopulations in which they are interested and choose their methods accordingly. We demonstrate how subgroup analysis can be carried out by different Bayesian methods and discuss how they identify slightly different subpopulations.

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