Besides vaccine certificates, research suggests leaders also need to trigger society’s intrinsic motivation to help in order to achieve lasting and equitable solutions.

The vacuum-exhausted isolation locker (VEIL) provides a safety barrier during the care of COVID-19 patients. The VEIL is a 175-L enclosure with exhaust ports to continuously extract air through viral particle filters connected to hospital suction. Our experiments show that the VEIL contains and exhausts exhaled aerosols and droplets.

This study applied causal criteria in directed acyclic graphs for handling covariates in associations for prognosis of severe coronavirus disease 2019 (COVID-19) cases. To identify non-specific blood tests and risk factors as predictors of hospitalisation due to COVID-19, one has to exclude noisy predictors by comparing the concordance statistics (area under the curve − AUC) for positive and negative cases of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Predictors with significant AUC at negative stratum should be either controlled for their confounders or eliminated (when confounders are unavailable). Models were classified according to the difference of AUC between strata. The framework was applied to an open database with 5644 patients from Hospital Israelita Albert Einstein in Brazil with SARS-CoV-2 reverse transcription – polymerase chain reaction (RT-PCR) exam. C-reactive protein (CRP) was a noisy predictor: hospitalisation could have happened due to causes other than COVID-19 even when SARS-CoV-2 RT-PCR is positive and CRP is reactive, as most cases are asymptomatic to mild. Candidates of characteristic response from moderate-to-severe inflammation of COVID-19 were: combinations of eosinophils, monocytes and neutrophils, with age as risk factor; and creatinine, as risk factor, sharpens the odds ratio of the model with monocytes, neutrophils and age.

In December 2019, the first confirmed case of pneumonia caused by a novel coronavirus was reported. Coronavirus disease 2019 (COVID-19) is currently spreading around the world. The relationships among the pandemic and its associated travel restrictions, social distancing measures, contact tracing, mask-wearing habits and medical consultation efficiency have not yet been extensively assessed. Based on the epidemic data reported by the Health Commission of Wenzhou, we analysed the developmental characteristics of the epidemic and modified the Susceptible-Exposed-Infectious-Removed (SEIR) model in three discrete ways. (1) According to the implemented preventive measures, the epidemic was divided into three stages: initial, outbreak and controlled. (2) We added many factors, such as health protections, travel restrictions and social distancing, close-contact tracing and the time from symptom onset to hospitalisation (TSOH), to the model. (3) Exposed and infected people were subdivided into isolated and free-moving populations. For the parameter estimation of the model, the average TSOH and daily cured cases, deaths and imported cases can be obtained through individual data from epidemiological investigations. The changes in daily contacts are simulated using the intracity travel intensity (ICTI) from the Baidu Migration Big Data platform. The optimal values of the remaining parameters are calculated by the grid search method. With this model, we calculated the sensitivity of the control measures with regard to the prevention of the spread of the epidemic by simulating the number of infected people in various hypothetical situations. Simultaneously, through a simulation of a second epidemic, the challenges from the rebound of the epidemic were analysed, and prevention and control recommendations were made. The results show that the modified SEIR model can effectively simulate the spread of COVID-19 in Wenzhou. The policy of the lockdown of Wuhan, the launch of the first-level Public Health Emergency Preparedness measures on 23 January 2020 and the implementation of resident travel control measures on 31 January 2020 were crucial to COVID-19 control.

The coronavirus disease 2019 (COVID-19) pandemic is currently the most critical challenge in public health. An understanding of the factors that affect severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection will help fight the COVID-19 pandemic. This study sought to investigate the association between SARS-CoV-2 infection and blood type distribution. The big data provided by the World Health Organization (WHO) and Johns Hopkins University were used to assess the dynamics of the COVID-19 epidemic. The infection data in the early phase of the pandemic from six countries in each of six geographic zones divided according to the WHO were used, representing approximately 5.4 billion people around the globe. We calculated the infection growth factor, doubling times of infection and death cases, reproductive number and infection and death cases in relation to the blood type distribution. The growth factor of infection and death cases significantly and positively correlated with the proportion of the population with blood type A and negatively correlated with the proportion of the population with blood type B. Compared with the lower blood type A population (<30%), the higher blood type A population (⩾30%) showed more infection and death cases, higher growth factors and shorter case doubling times for infections and deaths and thus higher epidemic dynamics. Thus, an association exists between SARS-CoV-2 and the ABO blood group distribution, which might be useful for fighting the COVID-19 pandemic.

A 12-year-old girl presented with fever and signs of systemic inflammation, and was found to have junctional tachycardia. She was subsequently diagnosed with Multisystem Inflammatory Syndrome in Children and treated with intravenous immunoglobulin and steroids, which led to resolution of the arrhythmia.

Health care workers (HCWs) are vulnerable to the risk of infections and could become vectors of onward transmission of coronavirus disease 2019 (COVID-19). Little is known about the factors which could contribute to increased COVID-19 infection among HCWs in Nigeria. We aimed at assessing the causes of COVID-19 infection among HCWs. We used a qualitative study design to conduct in-depth interview among 16 frontline HCWs participating in the COVID-19 response in Kwara State, Nigeria. Colaizzi's phenomenological method was used in the qualitative analysis of data. We found that HCWs were aware of their vulnerability to the COVID-19 infection, and the reasons attributed included poor knowledge of IPC measures for COVID-19, inadequate supply of personal protective equipment (PPE), poor political will and inadequate health facilities (HFs) management support. Improved political will and better involvement of HFs management teams in infection prevention and control (IPC) systems are needed to reduce the risk for COVID-19 infection among HCWs. We recommend scale-up training on IPC measures particularly hand washing and use of PPE as well as the development of effective points of care risk assessment with a high index of suspicion in HFs.

The COVID-19 pandemic has had a huge influence in almost all areas of life, affecting societies, economics, and health care systems worldwide. The paediatric cardiology community is no exception. As the challenging battle with COVID-19 continues, professionals from the Association for the European Paediatric and Congenital Cardiology receive many questions regarding COVID-19 in a Paediatric and Congenital Cardiology setting. The aim of this paper is to present the AEPC position on frequently asked questions based on the most recent scientific data, as well as to frame a discussion on how to take care of our patients during this unprecedented crisis. As the times are changing quickly and information regarding COVID-19 is very dynamic, continuous collection of evidence will help guide constructive decision-making.

Coronavirus disease-2019 (COVID-19) has caused the recent pandemic worldwide. Research studies are focused on various factors affecting the pandemic to find effective vaccine or therapeutics against COVID-19. Environmental factors are the important regulators of COVID-19 pandemic. This study aims to determine the impact of weather on the COVID-19 cases, fatalities and frequency of mutations in Bangladesh. The impacts were determined on 1, 7 and 14 days of the case. The study was conducted based on Spearman's correlation coefficients. The highest correlation was found between population density and cases (rs = 0.712). Among metrological parameters, average temperature had the strongest correlation (rs = −0.675) with the cases. About 82% of Bangladeshi isolates had D614G at spike protein. Both temperature and UV index had strong effects on the frequency of mutations. Among host factors, coinfection is highly associated with frequency of different mutations. This study will give a complete picture of the effects of metrological parameters on COVID-19 cases, fatalities and mutation frequency that will help the authorities to take proper decisions.

We report the use of aqueous microdroplets to accelerate deoxyribonucleic acid (DNA) fragmentation by deoxyribonuclease I (DNase I), and we present a simple, ultrafast approach named DNA fragment mass fingerprinting to discriminate different DNA sequences by comparing their fragment mass patterns. DNA fragmentation in tiny microdroplets, which was produced by electrosonically spraying (+3 kV) a room temperature aqueous solution containing 10 μM DNA and 10 μg ml−1 DNase I from a homemade setup, takes less than 1 ms. High differentiation/identification fidelity could be obtained by applying a cosine correlation measure for similarity assessment between two fragment mass patterns, which compares both mass-to-charge ratios (m/z) with an error tolerance of 5 ppm and the peaks’ relative intensities. A single-nucleotide mutation in the sequence of bases, as exemplified by the sickle cell anemia mutation, is differentiated by setting a cutoff value of similarity at 90%. The order change of two adjacent bases in the sequence could still be well discriminated with a similarity of only 62% between the fragment mass patterns of the two similar sequences, which have the same molecular weights and thus cannot be differentiated by gel electrophoresis or direct mass detection by mass spectrometry. Compared to traditional genotyping methods, such as quantitative real-time polymerase chain reaction, the identification process with our approach could be completed within several minutes without any other expensive and complicated reagents or experimental steps. The potential of our approach for convenient and fast microbe genetic discrimination or identification is further demonstrated by differentiating the Orf1ab gene fragments of two similar coronaviruses with a very high sequence homologous rate of 96%, SARS-CoV-2 and bat-SL-CoVZC45, with a similarity of 0% between their fragment mass patterns.

In recent months, COVID-19 has become a global pandemic and had a huge impact on the world. People under different conditions have very different attitudes toward the epidemic. Due to the real-time and large-scale nature of social media, we can continuously obtain a massive amount of public opinion information related to the epidemic from social media. In particular, researchers may ask questions such as “how is the public reacting to COVID-19 in China during different stages of the pandemic?”, “what factors affect the public opinion orientation in China?”, and so on. To answer such questions, we analyze the pandemic-related public opinion information on Weibo, China's largest social media platform. Specifically, we have first collected a large amount of COVID-19-related public opinion microblogs. We then use a sentiment classifier to recognize and analyze different groups of users’ opinions. In the collected sentiment-orientated microblogs, we try to track the public opinion through different stages of the COVID-19 pandemic. Furthermore, we analyze more key factors that might have an impact on the public opinion of COVID-19 (e.g. users in different provinces or users with different education levels). Empirical results show that the public opinions vary along with the key factors of COVID-19. Furthermore, we analyze the public attitudes on different public-concerning topics, such as staying at home and quarantine. In summary, we uncover interesting patterns of users and events as an insight into the world through the lens of a major crisis.