ABSTRACT
As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.
ABSTRACT
As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.
ABSTRACT
Background: The outbreak of SARS-CoV-2 lead to a worldwide pandemic which poses substantial challenges to public health. Methods: We enrolled 102 consecutive recovered patients with laboratory-confirmed SARS-CoV-2 infection. Epidemiological and demographic characteristics, temporal dynamic profiles of laboratory tests and findings on chest CT radiography, and clinical outcomes were collected and analyzed. Results: Independent risk factors for prolonged fever, viral RNA shedding or radiologic recovery included age of more than 44 years, female gender, having symptoms of cough and fever, a delay from the symptom onset to hospitalization of more than 3 days, a lower CD4 count of less than 500/mu L on admission, and severe or critical illness in hospitalization. The estimated median time from symptom onset was 6.4 (5.5 -7.4) days to peak viral load, 9.1 (7.9 -10.4) days to afebrile, 8 (6.7 -9.4) days to worst radiologic finding, 12.7 (11.2 -14.3) days to viral RNA negativity, and 26.7 (23.8 -29.9) days to radiologic resolution. This study included the entire cross-section of patients seen in our clinical practice and reflected the real-world situation. Conclusions: These findings provide the rationale for strategies of active symptom monitoring, timing of quarantine and antiviral interventions, and duration of radiologic follow-up in patients with COVID-19.
ABSTRACT
Under the background that Covid-19 is spreading across the world, the lifestyle of people has to confront a series of changes and challenges. This also presents new problems and requirements to automation facilities. For example, nowadays masks have almost become necessities for people in public places. However, most access control systems (ACS) cannot recognize people wearing masks and authenticate their identities to deal with increasingly serious epidemic pressure. Consequently, many public entries have turned to an attendant mode that brings low efficiency, infection potential, and high possibility of negligence. In this paper, a new security classification framework based on face recognition is proposed. This framework uses mask detection algorithm and face authentication algorithm with anti-spoofing function. In order to evaluate the performance of the framework, this paper employs the Chinese Academy of Science Institute of Automation-Face Anti-spoofing Datasets (CASIA-FASD) and Reply-Attack datasets as benchmarks. Performance evaluation indicates that the Half Total Error Rate (HTER) is 9.7%, the Equal Error Rate (EER) is 5.5%. The average process time of a single frame is 0.12 seconds. The results demonstrate that this framework has a high anti-spoofing capability and can be employed on the embedded system to complete the mask detection and face authentication task in real-time.
ABSTRACT
Objective: To systematically evaluate the incidence of adverse reactions to coronavirus disease 2019 (COVID-19) vaccination. Methods: We systematically searched PubMed, Embase, The Cochrane Library, Web of Science, CNKI, WanFang Data, and VIP Database from the inception of each database to August 31, 2021. Randomized controlled clinical trials (RCTs) on the safety of different types of COVID-19 vaccines were retrieved and analyzed. A random or fixed-effects model was used with an odds ratio as the effect size. The quality of each reference was evaluated. The incidence of the adverse reactions of the placebo group and the vaccination group was compared. Heterogeneity and publication bias were taken care of by meta-regression and sub-group analyses. Results: A total of 13 articles were included, with 81 287 subjects. Compared with the placebo group, the vaccination group showed a higher combined risk ratio (RR) of total adverse reactions (RR=1.67, 95% CI: 1.46-1.91, P<0.01), local adverse reactions (RR=2.86, 95% CI: 2.11-3.87, P<0.01), systemic adverse reactions (RR=1.25, 95% CI: 0.92-1.72, P=0.16), pain (RR=2.55, 95% CI: 1.75-3.70, P<0.01), swelling (RR=4.16, 95% CI: 1.71-10.17, P=0.002, fever (RR=2.34, 95% CI: 1.84-2.97, P<0.01), fatigue (RR=1.36, 95% CI: 1.32-1.41, P<0.01) and headache (RR=1.22, 95% CI: 1.18-1.26, P<0.01). The subgroup analysis showed the incidence of adverse reactions of the vaccination group after injection of the three COVID-19 vaccines (inactivated viral vaccines, mRNA vaccines and adenovirus vector vaccines) was higher than that of the placebo group, and the difference between the placebo group and the vaccination group in the mRNA vaccine subgroup and the adenovirus vector vaccine subgroup was statistically significant (P<0.01). The incidence of adverse reactions after injection of COVID-19 vaccine in subgroups of different ages was significantly higher than that in the placebo group (P<0.01). Conclusions: COVID-19 vaccines have a good safety, among which adenovirus vector vaccine has the highest incidence of adverse reactions. Both adolescents and adults vaccinated with novel coronavirus vaccine have a certain proportion of adverse reactions, but the symptoms are mild and can be relieved by themselves. Our meta-analysis can help boost global awareness of vaccine safety, promote mass vaccination, help build regional and global immune barriers and effectively curb the recurrency of COVID-19.