Since the outbreak of 2019 novel coronavirus (2019-nCoV) infection in Wuhan City, China, pediatric cases have gradually increased. It is very important to prevent cross-infection in pediatric fever clinics, to identify children with fever in pediatric fever clinics, and to strengthen the management of pediatric fever clinics. According to prevention and control programs, we propose the guidance on the management of pediatric fever clinics during the nCoV pneumonia epidemic period, which outlines in detail how to optimize processes, prevent cross-infection, provide health protection, and prevent disinfection of medical staff. The present consideration statement summarizes current strategies on the pre-diagnosis, triage, diagnosis, treatment, and prevention of 2019-nCoV infection, which provides practical suggestions on strengthening the management of pediatric fever clinics during the nCoV pneumonia epidemic period.

Responding to the extreme scarcity of medical resources during the early outbreak of the coronavirus disease (COVID-19) in Wuhan, China, an emergency specialist hospital of Leishenshan started to construct on January 26, 2020, and accommodate patients on February 6, 2020. The clinical laboratory center of Leishenshan Hospital (CLCLH) was constructed at the same time within 11 days to support the treatment of inpatients in Leishenshan Hospital and the testing of suspected patients from different fever clinics in Wuhan. The CLCLH could perform a total of 320 clinic, 299 biochemistry, 31 microorganism, and 47 infection and immunity examinations per day. It could also complete an average of 239 nucleic acid tests and 118 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody examinations per day. No suspected cases were documented among the health care workers during the operation of the CLCLH. The construction and operation experiences of the CLCLH is provided in this study and might be used by other countries as reference. The content of this study is divided into 4 parts: (1) the establishment of the CLCLH, including its layout and medical resource allocation; (2) the major testing items; (3) the specific procedure of COVID-19 indicator examination; and (4) the standardized personal protection measures.

On coronavirus disease 2019 (COVID-19) wards, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid was frequently detected on high-touch surfaces, floors, and socks inside patient rooms. Contamination of floors and shoes was common outside patient rooms on the COVID-19 wards but decreased after improvements in floor cleaning and disinfection were implemented.

Since its recent onset, the COVID-19 pandemic has altered the daily lives of millions around the world. One area particularly affected is our diets, with food supply chain disruptions, media coverage of food safety issues and restaurant closures all influencing consumer dietary behaviour. Given this situation, we pose a timely question – what is the impact of the current pandemic on longer-term meat consumption patterns? This issue is pertinent given accumulating evidence that overconsumption of meat, particularly red meat, is associated with negative environmental and health outcomes. Here, we discuss how the current pandemic has already begun to shift public awareness of illnesses linked to animals and has resulted in short-term changes in patterns of meat consumption. Past zoonotic outbreaks, such as SARS and swine flu, are also referred to, and we find that these led to similar short-term reductions in meat intake, a shift in the type of meat chosen and longer-lasting impacts on consumer perceptions of the health risks associated with meat. We conclude that, if immediate changes in eating patterns as a result of COVID-19 are retained in the longer term, one possible opportunity to emerge from the current pandemic may be a shift away from overconsumption of meat, leading to potential health and environmental benefits in the longer term.

Our study aimed to systematically analyse the risk factors of coronavirus disease 2019 (COVID-19) patients with severe disease. An electronic search in eight databases to identify studies describing severe or critically ill COVID-19 patients from 1 January 2020 to 3 April 2020. In the end, we meta-analysed 40 studies involving 5872 COVID-19 patients. The average age was higher in severe COVID-19 patients (weighted mean difference; WMD = 10.69, 95%CI 7.83–13.54). Patients with severe disease showed significantly lower platelet count (WMD = −18.63, 95%CI −30.86 to −6.40) and lymphocyte count (WMD = −0.35, 95%CI −0.41 to −0.30) but higher C-reactive protein (CRP; WMD = 42.7, 95%CI 31.12–54.28), lactate dehydrogenase (LDH; WMD = 137.4, 95%CI 105.5–169.3), white blood cell count（WBC), procalcitonin（PCT）, D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and creatinine（Cr）. Similarly, patients who died showed significantly higher WBC, D-dimer, ALT, AST and Cr but similar platelet count and LDH as patients who survived. These results indicate that older age, low platelet count, lymphopenia, elevated levels of LDH, ALT, AST, PCT, Cr and D-dimer are associated with severity of COVID-19 and thus could be used as early identification or even prediction of disease progression.

This article reports the establishment of an isolated, fully functional field intensive care unit (FICU) unit equipped with all necessary critical care facilities as a part of the national pre-emptive preparedness to treat an unexpected surge outbreak of coronavirus disease 2019 (COVID-19) patients in Bahrain. One floor of an existing car parking structure was converted into a 130-bed FICU set-up by the in-house project implementation team comprised of multidisciplinary departments. The setting was a military hospital in the Kingdom of Bahrain, and the car park was on the hospital premises. The FICU contained a 112-bed fully equipped ICU and an 18-bed step-down ICU, and was built in 7 d to cater to the intensive care of COVID-19 patients in Bahrain.

The spread of COVID-19 is recent in India, which has within 4 months caused over 190 000 infections, as of 1 June 2020, despite four stringent lockdowns. With the current rate of the disease transmission in India, which is home to over 1.35 billion people, the infection spread is predicted to be worse than the USA in the upcoming months. To date, there is a major lack of understanding of the transmission dynamics and epidemiological characteristics of the disease in India, inhibiting effective measures to control the pandemic. We collected all the available data of the individual patients, cases and a range of parameters such as population distribution, testing and healthcare facilities, and weather, across all Indian states till May 2020. Numerical analysis was conducted to determine the effect of each parameter on the COVID-19 situation in India. A significant amount of local transmission in India initiated with travellers returning from abroad. Maharashtra, Tamil Nadu and Delhi are currently the top three infected states in India with doubling time of 14.5 days. The average recovery rate across Indian states is 42%, with a mortality rate below 3%. The rest 55% are currently active cases. In total, 88% of the patients experienced symptoms of high fever, 68% suffered from dry cough and 7.1% patients were asymptomatic. In total, 66.8% patients were males, 73% were in the age group of 20–59 years and over 83% recovered in 11–25 days. Approximately 3.4 million people were tested between 1 April and 25 May 2020, out of which 4% were detected COVID-19-positive. Given the current doubling time of infections, several states may face a major shortage of public beds and healthcare facilities soon. Weather has minimal effect on the infection spread in most Indian states. The study results will help policymakers to predict the trends of the disease spread in the upcoming months and devise better control measures.

Coronavirus disease 2019 (COVID-19) is a global health threat. A hospital in Zhuhai adopted several measures in Fever Clinic Management (FCM) to respond to the outbreak of COVID-19. FCM has been proved to be effective in preventing nosocomial cross infection. Faced with the emergency, the hospital undertook creative operational steps in relation to the control and spread of COVID-19, with special focuses on physical and administrative layout of buildings, staff training and preventative procedures. The first operational step was to set up triaging stations at all entrances and then complete a standard and qualified fever clinic, which was isolated from the other buildings within our hospital complex. Secondly, the hospital established its human resource reservation for emergency response and the allocation of human resources to ensure strict and standardised training methods through the hospital for all medical staff and ancillary employees. Thirdly, the hospital divided the fever clinic into partitioned areas and adapted a three-level triaging system. The experiences shared in this paper would be of practical help for the facilities that are encountering or will encounter the challenges of COVID-19, i.e. to prevent nosocomial cross infection among patients and physicians.

Diagnostic testing for the novel coronavirus is an important tool to fight the coronavirus disease (Covid-19) pandemic. However, testing capacities are limited. A modified testing protocol, whereby a number of probes are ‘pooled’ (i.e. grouped), is known to increase the capacity for testing. Here, we model pooled testing with a double-average model, which we think to be close to reality for Covid-19 testing. The optimal pool size and the effect of test errors are considered. The results show that the best pool size is three to five, under reasonable assumptions. Pool testing even reduces the number of false positives in the absence of dilution effects.

This paper presents my thinking and concerns about formation of COVID-19 policy. Policy formation must cope with substantial uncertainties about the nature of the disease, the dynamics of transmission, and behavioral responses. Data uncertainties limit our knowledge of the past trajectory and current state of the pandemic. Data and modeling uncertainties limit our ability to predict the impacts of alternative policies. I explain why current epidemiological and macroeconomic modeling cannot deliver realistically optimal policy. I describe my recent work quantifying basic data uncertainties that make policy analysis difficult. I discuss approaches for policy choice under uncertainty and suggest adaptive policy diversification.

Despite denials of politicians and other advisors, trade-offs have already been apparent in many policy decisions addressing the coronavirus disease 2019 pandemic and its social and economic consequences. Here, we illustrate why it is important, from a wellbeing perspective, to recognise such trade-offs, and provide a framework, based on the economic concept of ‘marginal analysis’, for doing so. We illustrate its potential through consideration of optimising the balance between reducing the reproductive rate (R) of the virus and further opening of the economy. The framework accommodates both perspectives in the health-vs-economy debate whereby, depending on where we are within the marginal analysis framework, either health issues are allowed to dominate or, below some threshold of R and/or background level of infection, health and economic considerations can be traded off against each other. Given the inevitability of such trade-offs, the framework exposes crucial questions to be addressed, such as: the critical value of R and/or background infection, above which health considerations predominate, and which may vary from jurisdiction to jurisdiction; and the value of lives forgone resulting from the small increases in R and/or background infection levels that may have to be tolerated as the economy is gradually opened.