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We think government lockdowns cause substantial collateral health damage. For example, hospital admissions in the USA for emergency treatment of acute ischaemic strokes have been substantially lower in February-March, 2020, than in February-March, 2019, resulting in delayed treatment.1 Compared with a historical baseline, UK nursing homes and hospices saw an increase in the number of deaths between February and June, 2020, associated with acute coronary syndrome (a 41% increase), stroke (a 39% increase), and heart failure (a 25% increase).2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection platforms currently report qualitative results. However, technology based on RT-PCR allows for calculation of viral load, which is associated with transmission risk and disease severity in other viral illnesses.1 Viral load in COVID-19 might correlate with infectivity, disease phenotype, morbidity, and mortality. To date, no studies have assessed the association between viral load and mortality in a large patient cohort.2, 3, 4 To our knowledge, we are the first to report on SARS-CoV-2 viral load at diagnosis as an independent predictor of mortality in a large hospitalised cohort (n=1145).

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

The emergence of three lethal coronaviruses in <20 years and the urgency of the COVID-19 pandemic have prompted efforts to develop new therapeutic strategies, including by repurposing existing agents. After performing a comparative analysis of the three pathogenic human coronaviruses severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), SARS-CoV-2, and Middle East respiratory syndrome coronavirus (MERS-CoV), we identified shared biology and host-directed drug targets to prioritize therapeutics with potential for rapid deployment against current and future coronavirus outbreaks.