Lessons from China's Experience: Legal Analysis of Health Risks and Medical Obstacles of Personnel in COVID-19 Control Areas.

Purpose: Individuals in controlled areas often face restrictions on their personal freedom, and if they are unable to receive medical treatment when needed, it can significantly increase their health risks. However, current epidemic prevention and control policies do not provide clear guidelines on how to ensure individuals in controlled areas to seek medical attention when faced with health problems. By implementing specific measures that local governments must take in order to protect the health of those in controlled areas, the risks to their health can be greatly reduced. Patients and Methods: Our research utilizes a comparative approach to analyze the measures adopted by various regions for safeguarding the health of individuals in control areas, and the diverse outcomes they produce. We conduct empirical analysis and present examples of severe health risks that individuals in control areas face due to inadequate health protection measures. Furthermore, we conduct a critical evaluation of China's legal management of control areas, identifying both its principles and shortcomings. Results: The lack of unified legal regulations has led some local governments to make some shortcomings in making decisions on epidemic prevention and control. Specifically, some governments have neglected to provide adequate medical protection for individuals in controlled areas, limited the authority of specific implementers of prevention policies, and failed to establish fair punishment mechanisms. These shortcomings have a direct impact on the health of those in controlled areas and can even lead to tragic outcomes. Conclusion: Effective management of individuals in control areas during public health emergencies is crucial in reducing health risks. To achieve this, China needs to establish unified regulations and requirements, particularly with regards to medical protection, for individuals in control areas. Such measures can be achieved through the improvement of legislation, which can significantly reduce health risks faced by individuals in control areas during public health emergencies.

Analysis of COVID-19 Severity from the Perspective of Coagulation Index using Evolutionary Machine Learning with Enhanced Brain Storm Optimization

Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting in ineffective therapeutic therapy. This study designs an effective intelligence framework to early recognition and discrimination of COVID-19 severity from the perspective of coagulation indexes. The framework is proposed by integrating an enhanced new stochastic optimizer, a brain storm optimizing algorithm (EBSO), with an evolutionary machine learning algorithm called EBSO-SVM. Fast convergence and low risk of the local stagnant can be guaranteed for EBSO with added by Harris hawks optimization (HHO), and its property is verified on 23 benchmarks. Then, the EBSO is utilized to perform parameter optimization and feature selection simultaneously for support vector machine (SVM), and the presented EBSO-SVM early recognition and discrimination of COVID-19 severity in terms of coagulation indexes using COVID-19 clinical data. The classification performance of the EBSO-SVM is very promising, reaching 91.9195% accuracy, 90.529% Matthews correlation coefficient, 90.9912% Sensitivity and 88.5705% Specificity on COVID-19. Compared with other existing state-of-the-art methods, the EBSO-SVM in this paper still shows obvious advantages in multiple metrics. The statistical results demonstrate that the proposed EBSO-SVM shows predictive properties for all metrics and higher stability, which can be treated as a computer-aided technique for analysis of COVID-19 severity from the perspective of coagulation.

Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine.

Coronavirus Disease 2019 (COVID-19) was distributed globally at the end of December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early diagnosis and successful COVID-19 assessment are missing, clinical care is ineffective, and deaths are high. In this study, we investigate whether the level of biochemical indicators helps to discriminate and classify the severity of the COVID-19 using the machine learning method. This research creates an efficient intelligence method for the diagnosis of COVID-19 from the perspective of biochemical indexes. The framework is proposed by integrating an enhanced new stochastic called the colony predation algorithm (CPA) with a kernel extreme learning machine (KELM), abbreviated as ECPA-KELM. The core feature of the approach is the ECPA algorithm which incorporates the two main operators that have been abstained from the grey wolf optimizer and moth-flame optimizer to improve and restore the CPA research functions and are simultaneously used to optimize the parameters and to select features for KELM. The ECPA output is checked thoroughly using IEEE CEC2017 benchmark to verify the capacity of the proposed methodology. Finally, in the diagnosis of COVID-19 using biochemical indexes, the designed ECPA-KELM model and other competing KELM models based on other optimization are used. Checking statistical results will display improved predictive properties for all metrics and higher stability. ECPA-KELM can also be used to discriminate and classify the severity of the COVID-19 as a possible computer-aided method and provide effective early warning for the treatment and diagnosis of COVID-19.

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods.

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.

Twelve out of 117 recovered COVID-19 patients retest positive in a single-center study of China.

BACKGROUND: It has been reported that a fraction of recovered coronavirus disease 2019(COVID-19) patients have retested positive for SARS-CoV-2. Clinical characteristics and risk factors for retesting positive have not been studied extensively. METHODS: In this retrospective, single-center cohort study, we included adult patients (≥ 18 years old) diagnosed as COVID-19 in Affiliated Yueqing Hospital, Wenzhou Medical University, Zhejiang, China. All the patients were discharged before March 31, 2020, and were re-tested for SARS-CoV-2 RNA by real-time reverse-transcriptase polymerase-chain-reaction (RT-PCR) after meeting the discharge criteria. We retrospectively analyzed this cohort of 117 discharged patients and analyzed the differences between retest positive and negative patients in terms of demographics, clinical characteristics, laboratory findings, chest computed tomography (CT) features and treatment procedures. FINDINGS: Compared with the negative group, the positive group had a higher proportion of patients with comorbidities (Odds Ratio(OR) =2·12, 95% Confidence Interval(CI) 0·48-9·46; p = 0·029), longer hospital stay (OR=1·21, 95% CI 1·07-1·36; p = 0·008), a higher proportion of patients with lymphocytopenia (p = 0·036), a higher proportion of antibiotics treatment (p = 0·008) and glucocorticoids treatment (p = 0·003). Multivariable regression showed increasing odds of positive SARS-CoV-2 retest after discharge associated with longer hospital stay (OR=1·22, 95% CI 1·08-1·38; p = 0·001), and lymphocytopenia (OR=7·74, 95% CI 1·70-35·21; p = 0·008) on admission. INTERPRETATION: Patients with COVID-19 who met discharge criteria could still test positive for SARS-CoV-2 RNA. Longer hospital stay and lymphopenia could be potential risk factors for positive SARS-CoV-2 retest in COVID-19 patients after hospital discharge. FUNDING: Natural Science Foundation of Zhejiang Province, Medical Scientific Research Fund of Zhejiang Province, Wenzhou science and technology project.