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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.

The current boom of safe and renewable energy storage systems is driving the recent renaissance of Zn‐ion batteries. However, the notorious tip‐induced dendrite growth on the Zn anode restricts their further application. Herein, the first demonstration of constructing a flexible 3D carbon nanotube (CNT) framework as a Zn plating/stripping scaffold is constituted to achieve a dendrite‐free robust Zn anode. Compared with the pristine deposited Zn electrode, the as‐fabricated Zn/CNT anode affords lower Zn nucleation overpotential and more homogeneously distributed electric field, thus being more favorable for highly reversible Zn plating/stripping with satisfactory Coulombic efficiency rather than the formation of Zn dendrites or other byproducts. As a consequence, a highly flexible symmetric cell based on the Zn/CNT anode presents appreciably low voltage hysteresis (27 mV) and superior cycling stability (200 h) with dendrite‐free morphology at 2 mA cm−2, accompanied by a high depth of discharge (DOD) of 28%. Such distinct performance overmatches most of recently reported Zn‐based anodes. Additionally, this efficient rechargeability of the Zn/CNT anode also enables a substantially stable Zn//MnO2 battery with 88.7% capacity retention after 1000 cycles and remarkable mechanical flexibility.

BACKGROUND:The novel coronavirus disease 2019 (COVID-19) has become a global health emergency. The cumulative number of new confirmed cases and deaths are still increasing out of China. Independent predicted factors associated with fatal outcomes remain uncertain.RESEARCH QUESTION: The goal of the current study was to investigate the potential risk factors associated with fatal outcomes from COVID-19 through a multivariate Cox regression analysis and a nomogram model. STUDY DESIGN AND METHODS:A retrospective cohort of 1,590 hospitalized patients with COVID-19 throughout China was established. The prognostic effects of variables, including clinical features and laboratory findings, were analyzed by using Kaplan-Meier methods and a Cox proportional hazards model. A prognostic nomogram was formulated to predict the survival of patients with COVID-19. RESULTS:In this nationwide cohort, nonsurvivors included a higher incidence of elderly people and subjects with coexisting chronic illness, dyspnea, and laboratory abnormalities on admission compared with survivors. Multivariate Cox regression analysis showed that age $ 75 years (hazard ratio [HR], 7.86; 95% CI, 2. 44-25.35), age between 65 and 74 years (HR, 3.43; 95% CI, 1.24-9.5), coronary heart disease (HR, 4.28; 95% CI, 1.14-16.13), cerebrovascular disease (HR, 3.1; 95% CI, 1.07-8.94), dyspnea (HR, 3.96; 95% CI,, procalcitonin level > 0.5 ng/mL (HR, 8.72; 95% CI,, and aspartate aminotransferase level > 40 U/L (HR, 2.2; 95% CI, 1.1-6.73) were independent risk factors associated with fatal outcome. A nomogram was established based on the results of multivariate analysis. The internal bootstrap resampling approach suggested the nomogram has sufficient discriminatory power with a C-index of 0.91 (95% CI, 0.85-0.97). The calibration plots also showed good consistency between the prediction and the observation. INTERPRETATION:The proposed nomogram accurately predicted clinical outcomes of patients with COVID-19 based on individual characteristics. Earlier identification, more intensive surveillance, and appropriate therapy should be considered in patients at high risk.