Research on epidemic spread model based on cold chain input.

In recent years, the new type of coronary pneumonia (COVID-19) has become a highly contagious disease worldwide, posing a serious threat to the public health. This paper is based on the SEIR model of the new coronavirus pneumonia, considering the impact of cold chain input and re-positive on the spread of the virus in the COVID-19. In the process of model design, the food cold chain and re-positive are used as parameters, and its stability is analyzed and simulated. The experimental results show that taking into account the cold chain input and re-positive can effectively simulate the spread of the epidemic. The research results have important research value and practical significance for the prevention and control of the COVID-19 and the prediction of important time nodes.

Research on epidemic spread model based on cold chain input

In recent years, the new type of coronary pneumonia (COVID-19) has become a highly contagious disease worldwide, posing a serious threat to the public health. This paper is based on the SEIR model of the new coronavirus pneumonia, considering the impact of cold chain input and re-positive on the spread of the virus in the COVID-19. In the process of model design, the food cold chain and re-positive are used as parameters, and its stability is analyzed and simulated. The experimental results show that taking into account the cold chain input and re-positive can effectively simulate the spread of the epidemic. The research results have important research value and practical significance for the prevention and control of the COVID-19 and the prediction of important time nodes.

Protocol for Alberta Societal Health Integration Program (ABSHIP): Community- based Complementary & Alternative Medicine (CAM) Services Model for Social Recovery and Improve Economic Participation (preprint)

Background Mental health problems and chronic health conditions cause significant productivity loss in the workplace. Chronic pain and mental health care needs in Canada that existed before COVID-19 remain unmet and have been exacerbated by the pandemic. The Alberta Societal Health Integration Program (ABSHIP) proposes an innovative community-based model in which Complementary and Alternative Medicine (CAM) services are provided to help achieve social recovery and improve economic participation. Methods Participants will receive CAM treatment in two modes: a minimum of two acupuncture treatment sessions per week for up to a total of 12 treatments, and voluntary Qigong exercise. The study will recruit 150 participants between the ages of 14 and 55 who are suffering from pain or mental health issues that are causing severe productivity loss. Primary outcome indicators are productivity (WPAI), pain (BPI), quality of sleep (PSQI), depression (PHQ-9), anxiety (GAD-7), anger (DAR-5), quality of life (EQ-5D-5L) and substance use (DAST-10 & CAGE). Secondary outcome indicators include general health care utilization, which will be measured by patients’ self-reported inpatient, outpatient, emergency department, and prescription drug utilization. Data will be collected at baseline (before treatment) and after the sixth and twelfth session (post-treatment) to measure short-term outcomes of the study. To understand the long-term impact of ABSHIP, participants will be invited to take the same survey three and six months after completing the program. Discussion ABSHIP is a first-of-its-kind study that provides CAM integrated interventions to enhance pain management, as well as protecting and fostering psychosocial well-being and resilience in children, adolescents, and seniors. The successful completion of the project will result in cost savings as well as significant evidence to aid instrument policy in the short and long run. Trial Registration This interventional study involving human participants, has been approved by the University of Calgary (UofC) Conjoint Health Research Ethics Board (CHREB) (ethics ID: REB 21-2050).

An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19.

At present, the Corona Virus Disease 2019 (COVID-19) is ravaging the world, bringing great impact on people's life safety and health as well as the healthy development of economy and society, so the research on the prediction of the development trend of the epidemic is crucial. In this paper, we focus on the prevention and control of epidemic using the relevant technologies in the field of artificial intelligence and signal analysis. With the unknown principle of epidemic transmission, we first smooth out the complex and variable epidemic data through the empirical mode decomposition model to obtain the change trends of epidemic data at different time scales. On this basis, the change trends under different time scales are trained using an extreme learning machine to obtain the corresponding prediction values, and finally the epidemic prediction results are obtained by fitting through Adaptive Network-based Fuzzy Inference System. The experimental results show that the algorithm has good learning ability, especially in the prediction of time-series sequences can guarantee the accuracy rate while having low time complexity. Therefore, this paper not only plays a theoretical support for epidemic prevention and control, but also plays an important role in the construction of public emergency health system in the long run.

Safety of protease inhibitors and Arbidol for SARS-CoV-2 pneumonia in Zhejiang Province, China.

The aim of this study was to evaluate the safety of an antiviral regimen of protease inhibitors combined with Arbidol (umifenovir) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia patients. The genomic sequence of SARS-CoV-2 is highly homologous to that of SARS-CoV (Zhou et al., 2020). Previously published basic and clinical research on anti-SARS-CoV treatment found that lopinavir/ritonavir (LPV/r) could improve the prognosis of SARS patients (Chan et al., 2003; Chu et al., 2004). Darunavir (DRV) is another protease inhibitor that blocks the binding of SARS-CoV-2 to human angiotensin-converting enzyme 2 (Omotuyi et al., 2020). The broad-spectrum antiviral drug Arbidol (umifenovir) also shows in vitro anti-SARS-CoV activity (Khamitov et al., 2008).

Retrospective analysis of the clinical features of outpatients with novel coronavirus in areas outside Wuhan, China (preprint)

Objective: The novel coronavirus pneumonia (COVID-19) has spread rapidly across the globe with the movement of people. How to diagnose COVID-19 quickly and accurately is a concern for all. We retrospectively assessed the clinical characteristics of patients with COVID-19 detected by outpatient screening in areas outside Wuhan, China, to guide early screening outside the epidemic area, to isolate and treat COVID-19-positive patients, and to control the spread of this virus in the region.Results: Among the 213 patients treated in the fever clinic of our hospital, 41 tested positive for novel coronavirus (2019-nCoV) and 172 were negative. Among the positive patients, 13 (31.7%) of the patients had been to Wuhan, while 28 (68.3%) had not been to Wuhan. There were 4 cases of clustering occurrence. The main symptoms exhibited by COVID-19-positive patients were fever (87.8%), cough (68.3%), and expectoration (34.1%). The C-reactive protein (CRP) levels were increased in 35 (85.3%) positive patients; the hydroxybutyrate dehydrogenase in the myocardial zymogram was increased in 22 positive patients (53.6%) and 38 negative patients (22.1%); computed tomography (CT) findings revealed lung lesions in all 41 positive patients (100%).Conclusion: We classified the patient population and analyzed the data to understand the early clinical performance of COVID-19. Our research illustrate that screening for COVID-19 outside Wuhan should focus on early symptoms such as fever and cough, in combination with lung CT findings, epidemiological history, and sputum pathogen detection to determine whether patients need further isolation.

Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms.

OBJECTIVE: The SARS-CoV-2-infected disease (COVID-19) outbreak is a major threat to human beings. Previous studies mainly focused on Wuhan and typical symptoms. We analysed 74 confirmed COVID-19 cases with GI symptoms in the Zhejiang province to determine epidemiological, clinical and virological characteristics. DESIGN: COVID-19 hospital patients were admitted in the Zhejiang province from 17 January 2020 to 8 February 2020. Epidemiological, demographic, clinical, laboratory, management and outcome data of patients with GI symptoms were analysed using multivariate analysis for risk of severe/critical type. Bioinformatics were used to analyse features of SARS-CoV-2 from Zhejiang province. RESULTS: Among enrolled 651 patients, 74 (11.4%) presented with at least one GI symptom (nausea, vomiting or diarrhoea), average age of 46.14 years, 4-day incubation period and 10.8% had pre-existing liver disease. Of patients with COVID-19 with GI symptoms, 17 (22.97%) and 23 (31.08%) had severe/critical types and family clustering, respectively, significantly higher than those without GI symptoms, 47 (8.14%) and 118 (20.45%). Of patients with COVID-19 with GI symptoms, 29 (39.19%), 23 (31.08%), 8 (10.81%) and 16 (21.62%) had significantly higher rates of fever >38.5°C, fatigue, shortness of breath and headache, respectively. Low-dose glucocorticoids and antibiotics were administered to 14.86% and 41.89% of patients, respectively. Sputum production and increased lactate dehydrogenase/glucose levels were risk factors for severe/critical type. Bioinformatics showed sequence mutation of SARS-CoV-2 with m6A methylation and changed binding capacity with ACE2. CONCLUSION: We report COVID-19 cases with GI symptoms with novel features outside Wuhan. Attention to patients with COVID-19 with non-classic symptoms should increase to protect health providers.

Maximum entropy method for estimating the reproduction number: An investigation for COVID-19 in China (preprint)

The key parameter that characterizes the transmissibility of a disease is the reproduction number R. If it exceeds 1, the number of incident cases will inevitably grow over time, and a large epidemic is possible. To prevent the expansion of an epidemic, R must be reduced to a level below 1. To estimate the reproduction number, the probability distribution function of the generation interval of an infectious disease is required to be available; however, this distribution is often unknown. In this letter, given the incomplete information for the generation interval, we propose a maximum entropy method to estimate the reproduction number. Based on this method, given the mean value and variance of the generation interval, we first determine its probability distribution function and in turn estimate the real-time values of reproduction number of COVID-19 in China. By applying these estimated reproduction numbers into the susceptible-infectious-removed epidemic model, we simulate the evolutionary track of the epidemic in China, which is well in accordance with that of the real incident cases. The simulation results predict that China's epidemic will gradually tend to disappear by May 2020 if the quarantine measures can continue to be executed.