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Background The public health impact of the coronavirus disease 2019 (COVID-19) pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. Methods Using a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care. Results The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R = 1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics. Conclusions Advances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

Background Patients with previous tuberculosis may have residual DNA in sputum that confounds nucleic acid amplification tests such as Xpert MTB/RIF. Little is known about the frequency of Xpert-positive, culture-negative ("false positive") results in retreatment patients, whether these are distinguishable from true positives, and whether Xpert's automated filter-based wash step reduces false positivity by removing residual DNA associated with nonintact cells. Methods Pretreatment patients (n = 2889) with symptoms of tuberculosis from Cape Town, South Africa, underwent a sputum-based liquid culture and Xpert. We also compared Xpert results from dilutions of intact or heat-lysed and mechanically lysed bacilli. Results Retreatment cases were more likely to be Xpert false-positive (45/321 Xpert-positive retreatment cases were false-positive) than new cases (40/461) (14% [95% confidence interval {CI}, 10%-18%] vs 8% [95% CI, 6%-12%];P= .018). Fewer years since treatment completion (adjusted odds ratio [aOR], 0.85 [95% CI, .73-.99]), less mycobacterial DNA (aOR, 1.14 [95% CI, 1.03-1.27] per cycle threshold [CT]), and a chest radiograph not suggestive of active tuberculosis (aOR, 0.22 [95% CI, .06-.82]) were associated with false positivity. CThad suboptimal accuracy for false positivity: 46% of Xpert-positives with CT> 30 would be false positive, although 70% of false positives would be missed. CT's predictive ability (area under the curve, 0.83 [95% CI, .76-.90]) was not improved by additional variables. Xpert detected nonviable, nonintact bacilli without a change in CTvs controls. Conclusions One in 7 Xpert-positive retreatment patients were culture negative and potentially false positive. False positivity was associated with recent previous tuberculosis, high CT, and a chest radiograph not suggestive of active tuberculosis. Clinicians may consider awaiting confirmatory testing in retreatment patients with CT> 30; however, most false positives fall below this cut-point. Xpert can detect DNA from nonviable, nonintact bacilli.