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Rationale: The COVID‐19 pandemic affects diverse groups of women and men differently. The risks and consequences are disproportionately felt by certain groups, especially those living in situations of vulnerability and those who experience discrimination. It is vital that country responses to COVID-19 consider equity, gender, ethnicity, and human rights perspectives to: • prevent the expansion of inequalities; • account for the everyday lived realities of different groups that may affect the success of measures. Objectives: • To function as a “first port of call” for national health policy makers to support their efforts to integrate and enhance equity, gender, ethnicity and human rights considerations and approaches into measures responding to COVID-19; • To provide links to sources of related information and guidelines, where available.

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.

oronavirus Disease 2019 (COVID-19) has recently become a public emergency and a worldwide pandemic. The clinical symptoms of severe and non-severe patients vary, and the case-fatality rate (CFR) in severe COVID-19 patients is very high. However, the information on the risk factors associated with the severity of COVID-19 and of their prognostic potential is limited. METHODS: In this retrospective study, the clinical characteristics, laboratory findings, treatment and outcome data were collected and analyzed from 223 COVID-19 patients stratified into 125 non-severe patients and 98 severe patients. In addition, a pooled large-scale meta-analysis of 1646 cases was performed. RESULTS: We found that the age, gender and comorbidities are the common risk factors associated with the severity of COVID-19. For the diagnosis markers, we found that the levels of D-dimer, C-reactive protein (CRP), lactate dehydrogenase (LDH), procalcitonin (PCT) were significantly higher in severe group compared with the non-severe group on admission (D-Dimer: 87.3% vs. 35.3%, P<0.001; CRP, 65.1% vs. 13.5%, P<0.001; LDH: 83.9% vs. 22.2%, P<0.001; PCT: 35.1% vs. 2.2%, P<0.001), while the levels of aspartate aminotransferase (ASP) and creatinine kinase (CK) were only mildly increased. We also made a large scale meta-analysis of 1646 cases combined with 4 related literatures, and further confirmed the relationship between the COVID-19 severity and these risk factors. Moreover, we tracked dynamic changes during the process of COVID-19, and found CRP, D-dimer, LDH, PCT kept in high levels in severe patient. Among all these markers, D-dimer increased remarkably in severe patients and mostly related with the case-fatality rate (CFR). We found adjuvant antithrombotic treatment in some severe patients achieved good therapeutic effect in the cohort. CONCLUSIONS: The diagnosis markers CRP, D-dimer, LDH and PCT are associated with severity of COVID-19. Among these markers, D-dimer is sensitive for both severity and CFR of COVID-19. Treatment with heparin or other anticoagulants may be beneficial for COVID-19 patients.

In response to the growing COVID-19 pandemic and shortages of laboratory-based molecular testing capacity and reagents, multiple diagnostic test manufacturers have developed and begun selling rapid and easy-to-use devices to facilitate testing outside of laboratory settings. These simple test kits are based either on detection of proteins from the COVID-19 virus in respiratory samples (e.g. sputum, throat swab) or detection, in blood or serum, of human antibodies generated in response to infection. WHO applauds the efforts of test developers to innovate and respond to the needs of the population. However, before these tests can be recommended, they must be validated in the appropriate populations and settings. Inadequate tests may miss patients with active infection or falsely categorize patients as having the disease when they do not, further hampering disease control efforts. At present, based on current evidence, WHO recommends the use of these new point-of-care immunodiagnostic tests only in research settings. They should not be used in any other setting, including for clinical decision-making, until evidence supporting use for specific indications is available. WHO continues to evaluate available immunodiagnostics tests for COVID-19 and will update this scientific brief when necessary...

Mast cells are present in mucosal and connective tissues throughout the body. They participate in the innate and adaptive immune process and have an important role in inflammation and allergy. Mast cells located in the submucosa of the respiratory tract are known to be activated by the SARS-CoV-2 virus. Once activated, degranulation occurs releasing histamine and proteases. Late mast-cell activation leads to the release of pro-inflammatory cytokines such as IL-1, IL-6 and TNF-α. Vitamin D is required to maintain the stability of mast cells, and Vitamin D deficiency results in mast cell activation.

This page summarises important considerations for the care of people with co-existing medical conditions during the COVID-19 pandemic. Key points from guidance and position statements are summarised for each condition, and there is a link to the main BMJ Best Practice topic. This overview topic is continually reviewed and updated, and more conditions will be added to this list. Our full topic on Coronavirus disease 2019 (COVID-2019) includes information on diagnosis and management, as well as prevention, differential diagnosis, epidemiology, aetiology, prognosis, and complications.

The COVID-19 epidemic started in the Hubei province in China in December 2019 and then spread around the world reaching the pandemic stage at the beginning of March 2020. Since then, several countries went into lockdown. We estimate the effect of the lockdown in France on the contact rate and the effective reproduction number Re of the COVID-19. We obtain a reduction by a factor 7 (Re=0.47, 95%-CI: 0.45-0.50), compared to the estimates carried out in France at the early stage of the epidemic. We also estimate the fraction of the population that would be infected by the beginning of May, at the official date at which the lockdown should be relaxed. We find a fraction of 3.7% (95%-CI: 3.0-4.8%) of the total French population, without taking into account the number of recovered individuals before April 1st, which is not known. This proportion is seemingly too low to reach herd immunity. Thus, even if the lockdown strongly mitigated the first epidemic wave, keeping a low value of Re is crucial to avoid an uncontrolled second wave (initiated with much more infectious cases than the first wave) and to hence avoid the saturation of hospital facilities. Our approach is based on the mechanistic-statistical formalism, which uses a probabilistic model to connect the data collection process and the latent epidemiological process, which is described by a SIR-type differential equation model.

[Abstract]. Background: Awareness and attentiveness have implications for the acceptance and adoption of disease prevention and control measures. Social media posts provide a record of the public’s attention to an outbreak. To measure the attention of Chinese netizens to COVID-19, a pre-established nationally representative cohort of Weibo users was searched for COVID-19-related keywords in their posts. Methods: COVID-19-related posts (N=1101) were retrieved from a longitudinal cohort of 52,268 randomly sampled Weibo accounts (December 31, 2019 – February 12, 2020). Results: Attention to COVID-19 was limited prior to China openly acknowledging human-to-human transmission on January 20. Following this date, attention quickly increased and has remained high over time. Particularly high levels of social media traffic appeared around when Wuhan was first placed in quarantine (January 23-24, 8-9% of the overall posts), when a scandal associated with the Red Cross Society of China occurred (February 1, 8%), and following the death of Dr. Li Wenliang (February 6-7, 11%), one of the whistleblowers reprimanded by the Chinese police in early January for discussing this outbreak online. Discussion: Limited early warnings represent missed opportunities to engage citizens earlier in the outbreak. Governments should more proactively communicate early warnings to the public in a transparent manner.

Background: Mounting evidence suggests that there is an undetected pool of COVID-19 asymptomatic but infectious cases. Estimating the number of asymptomatic infections has been crucial to understand the virus and contain its spread, which is, however, hard to be accurately counted. Methods: We propose an approach of machine learning based fine-grained simulator (MLSim), which integrates multiple practical factors including disease progress in the incubation period, cross-region population movement, undetected asymptomatic patients, and prevention and containment strength. The interactions among these factors are modeled by virtual transmission dynamics with several undetermined parameters, which are determined from epidemic data by machine learning techniques. When MLSim learns to match the real data closely, it also models the number of asymptomatic patients. MLSim is learned from the open Chinese global epidemic data. Findings: MLSim showed better forecast accuracy than the SEIR and LSTM-based prediction models. The MLSim learned from the data of China's mainland reveals that there could have been 150,408 (142,178-157,417) asymptomatic and had self-healed patients, which is 65% (64% - 65%) of the inferred total infections including undetected ones. The numbers of asymptomatic but infectious patients on April 15, 2020, were inferred as, Italy: 41,387 (29,037 - 57,151), Germany: 21,118 (11,484 - 41,646), USA: 354,657 (277,641 - 495,128), France: 40,379 (10,807 - 186,878), and UK: 144,424 (127,215 - 171,930). To control the virus transmission, the containment measures taken by the government were crucial. The learned MLSim also reveals that if the date of containment measures in China's mainland was postponed for 1, 3, 5, and 7 days later than Jan. 23, there would be 109,039 (129%), 183,930 (218%), 313,342 (371%), 537,555 (637%) confirmed cases on June 12. Conclusions: Machine learning based fine-grained simulators can better model the complex real-world disease transmission process, and thus can help decision-making of balanced containment measures. The simulator also revealed the potential great number of undetected asymptomatic infections, which poses a great risk to the virus containment.

Background: Interleukin-6 (IL-6) is known to be detrimental in coronavirus disease 2019 (COVID-19) because of its involvement in driving cytokine storm. This systematic review and meta-analysis aimed to assess the safety and efficacy of anti-IL-6 signaling (anti-IL6/IL-6R/JAK) agents on COVID-19 based on the current evidence. Methods: Studies were identified through systematic searches of PubMed, EMBASE, ISI Web of Science, Cochrane library, ongoing clinical trial registries (clinicaltrials.gov), and preprint servers (medRxiv, ChinaXiv) on August 10, 2020, as well as eligibility checks according to predefined selection criteria. Statistical analysis was performed using Review Manager (version 5.3) and STATA 12.0. Results: Thirty-one studies were included in the pooled analysis of mortality, and 12 studies were identified for the analysis of risk of secondary infections. For mortality analysis, 5630 COVID-19 cases including 2,132 treated patients and 3,498 controls were analyzed. Anti-IL-6 signaling agents plus standard of care (SOC) significantly decreased the mortality rate compared to SOC alone (pooled OR = 0.61, 95% CI 0.45–0.84, p = 0.002). For the analysis of secondary infection risk, 1,624 patients with COVID-19 including 639 treated patients and 985 controls were included, showing that anti-IL-6 signaling agents did not increase the rate of secondary infections (pooled OR = 1.21, 95% CI 0.70–2.08, p = 0.50). By contrast, for patients with critical COVID-19 disease, anti-IL-6 signaling agents failed to reduce mortality compared to SOC alone (pooled OR = 0.75, 95% CI 0.42–1.33, p = 0.33), but they tended to increase the risk of secondary infections (pooled OR = 1.85, 95% CI 0.95–3.61, p = 0.07). A blockade of IL-6 signaling failed to reduce the mechanical ventilation rate, ICU admission rate, or elevate the clinical improvement rate. Conclusion: IL-6 signaling inhibitors reduced the mortality rate without increasing secondary infections in patients with COVID-19 based on current studies. For patients with critical disease, IL-6 signaling inhibitors did not exhibit any benefit.