Background: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.

Background: Coronavirus disease 2019 (Covid-19) has resulted in a global outbreak. Few existing targeted medications are available. Lianhuaqingwen (LH) capsule, a repurposed marketed Chinese herb product, has been proven effective for influenza. Purpose: To determine the safety and efficacy of LH capsule in patients with Covid-19. Methods: We did a prospective multicenter open-label randomized controlled trial on LH capsule in confirmed cases with Covid-19. Patients were randomized to receive usual treatment alone or in combination with LH capsules (4 capsules, thrice daily) for 14 days. The primary endpoint was the rate of symptom (fever, fatigue, coughing) recovery. Results: We included 284 patients (142 each in treatment and control group) in the full-analysis set. The recovery rate was significantly higher in treatment group as compared with control group (91.5% vs. 82.4%, p = 0.022). The median time to symptom recovery was markedly shorter in treatment group (median: 7 vs. 10 days, p < 0.001). Time to recovery of fever (2 vs. 3 days), fatigue (3 vs. 6 days) and coughing (7 vs. 10 days) was also significantly shorter in treatment group (all p < 0.001). The rate of improvement in chest computed tomographic manifestations (83.8% vs. 64.1%, p < 0.001) and clinical cure (78.9% vs. 66.2%, p = 0.017) was also higher in treatment group. However, both groups did not differ in the rate of conversion to severe cases or viral assay findings (both p > 0.05). No serious adverse events were reported.

Clinical studies and case reports have identified a number of herb-drug interactions potentiated by the concurrent use of herbal medicines with prescription drugs. The purpose of this paper is to discuss the mechanisms and clinical implications of such herb-drug interactions by reviewing published human studies. Both pharmacokinetic and pharmacodynamic components may be involved in herbdrug interactions, although metabolic induction or inhibition is a common underlying mechanism for many herb-drug interactions. Drugs that have a high potential to interact with herbal medicines usually have a narrow therapeutic index, including warfarin, digoxin, cyclosporine, tacrolimus, amitriptyline, midazolam, indinavir, and irinotecan. Many of them are substrates of cytochrome P450s (CYPs) and/or P-glycoprotein (P-gp). Herbal medicines that are reported to interact with drugs include garlic (Allium sativum), ginger (Zingiber officinale), ginkgo (Ginkgo biloba), ginseng (Panax ginseng), and St. John's wort (Hypericum perforatum). For example, garlic has been shown to increase the clotting time and international normalized ratio (INR) of warfarin, cause hypoglycaemia when taken with chlorpropamide, and reduce the area under the plasma concentration-time curve (AUC) and maximum concentration of saquinavir in humans. Similarly, case reports have demonstrated that ginkgo may potentiate bleeding when combined with warfarin or aspirin, increases blood pressure when combined with thiazide diuretics, and has even led to a coma when combined with trazodone, a serotonin antagonist and reuptake inhibitor used to treat depression. Furthermore, ginseng reduced the blood levels of warfarin and alcohol as well as induced mania if taken concomitantly with phenelzine, a non-selective and irreversible monoamine oxidase inhibitor used as an antidepressant and anxiolytic agent. Lastly, multiple herb-drug interactions have been identified with St. John's wort that involve significantly reduced AUC and blood concentrations of warfarin, digoxin, indinavir, theophylline, cyclosporine, tacrolimus, amitriptyline, midazolam, and phenprocoumon. The clinical consequence of herb-drug interactions varies, from being well-tolerated to moderate or serious adverse reactions, or possibly life-threatening events. Undoubtedly, the early and timely identification of herb-drug interactions is imperative to prevent potentially dangerous clinical outcomes. Further well-designed studies are warranted to address the mechanisms and clinical significance of important herb-drug interactions.

Background: The pandemic of COVID-19 poses a challenge to global healthcare. The mortality rates of severe cases range from 8.1% to 38%, and it is particularly important to identify risk factors that aggravate the disease. Methods: We performed a systematic review of the literature with meta-analysis, using 7 databases to identify studies reporting on clinical characteristics, comorbidities and complications in severe and non-severe patients with COVID-19. All the observational studies were included. We performed a random or fixed effects model meta-analysis to calculate the pooled proportion and 95% confidence interval (CI). Measure of heterogeneity was estimated by Cochran's Q statistic, I 2 index and P value. Results: A total of 4881 cases from 25 studies related to COVID-19 were included. The most prevalent comorbidity was hypertension (severe: 33.4%, 95% CI: 25.4%–41.4%; non-severe 21.6%, 95% CI: 9.9%–33.3%), followed by diabetes (severe: 14.4%, 95% CI: 11.5%–17.3%; non-severe: 8.5%, 95% CI: 6.1%–11.0%). The prevalence of acute respiratory distress syndrome, acute kidney injury and shock were all higher in severe cases, with 41.1% (95% CI: 14.1%–68.2%), 16.4% (95% CI: 3.4%–29.5%) and 19.9% (95% CI: 5.5%–34.4%), rather than 3.0% (95% CI: 0.6%–5.5%), 2.2% (95% CI: 0.1%–4.2%) and 4.1% (95% CI: −4.8%–13.1%) in non-severe patients, respectively. The death rate was higher in severe cases (30.3%, 95% CI: 13.8%–46.8%) than non-severe cases (1.5%, 95% CI: 0.1%–2.8%). Conclusion: Hypertension, diabetes and cardiovascular diseases may be risk factors for severe COVID-19.