Coronary Pneumonia Research Articles

Study on the Changes of Antibody IgM and IgG Based on the Plasma Treatment of COVID-19 in Patients with New Coronary Pneumonia

Objective To apply the convalescent plasma of recovered patients with new coronary pneumonia to treat COVID-19 to explore the changes of antibodies IgM and IgG in their bodies and the therapeutic effect. Methods Plasma treatment was performed for COVID-19, and the values of antibody IgM and IgG ct were observed and statistically analyzed. Results (1) 99 cases of rapid development, severe and critically ill patients were transfused with 107250ml plasma, 1083.33ml per capita. The effective rate of blood transfusion was 100% and the cure rate was 100%. (2) The 229 bags of convalescent plasma of the infused convalescent patients have an IgG ct value of 31.61±23.28 and an IgM of 7.19±11.94. (3) The antibody IgG and IgM ct values (68.70±69.14 and 47.89±64.63) of patients in the blood transfusion group were higher than the pre-transfusion ct values (4.46±13.99 and 4.46±13.99), and the differences were significant (p<0.05); The antibody IgM and IgG ct values at discharge of the non-transfusion group were higher than the antibody ct values at admission (p<0.05); in general, the IgM and IgG antibody ct values of the blood transfusion group were 1-3 times higher than those of the non-transfusion group, and there was no difference ( p>0.05), but the levels of antibody IgG and IgM in severe patients in the blood transfusion group were significantly higher than those in the non-transfusion group (P<0.05). (4) The hospitalization days of the fast-growing and severe patients in the blood transfusion group were significantly shorter than those in the non-transfusion group (P<0.05). Conclusion The application of convalescent plasma from patients recovering from new coronary pneumonia to treat COVID-19 can greatly increase the antibody content in severe and critically ill patients, effectively enhance immune function, shorten the length of hospital stay, and speed up the early improvement of the disease.

Deep learning based model for classification of COVID −19 images for healthcare research progress

As imaging technology plays an important role in the diagnosis and evaluation of the new coronavirus pneumonia (COVID-19), COVID-19 related data sets have been published one after another, but there are relatively few data sets and research progress in related literature. To this end, through COVID-19-related journal papers, reports, and related open-source data set websites, organize and analyze the new coronary pneumonia data set and the deep learning models involved, including computed tomography (CT) image data sets and X-ray (CXR) Image dataset. Analyze the characteristics of the medical images presented in these data sets; focus on open-source data sets, as well as classification and segmentation models that perform well on related data sets. Finally, the future development trend of lung imaging technology is discussed.

Criminal law regulation of criminal acts involving COVID-19 epidemic

On the eve of the 2020 Spring Festival, the new coronavirus pneumonia broke out, normal social order was affected, and crimes involving the epidemic increased. On February 6, 2020, the Supreme People’s Court, the Supreme People’s Procuratorate, the Ministry of Public Security, and the Ministry of Justice jointly issued “Opinions on Punishment of Crimes Obstructing the Prevention and Control of the Novel Coronavirus Infection Pneumonia Epidemic According to Law”, which clearly stipulates nine types of crimes that hinder the prevention and control of the epidemic. In order to clarify and solve some outstanding problems of inconsistent understanding of the application of laws in the practice of epidemic prevention and control, the Supreme People’s Procuratorate selected cases that accounted for a large portion of cases, reflected many problems, and urgently required practical practices; they then issued ten batches of “Typical Cases of Crimes Obstructing the Prevention and Control of the New Coronary Pneumonia Epidemic by Procuratorial Organs across the Country”. This article is mainly based on the “Prosecutors across the country handle typical cases of crimes obstructing the prevention and control of the new crown pneumonia epidemic in accordance with the law”, analyzing economic and property crimes, crimes that disrupt the order of social management, and crimes that endanger public safety.

Small Slogans, Big Effect: the Mobilization Function of Epidemic Prevention Slogans

As a political communication vehicle, slogans played a unique mobilizing role in the early practice of the Chinese Communist Party. In the prevention and control of this new coronary pneumonia epidemic, a large number of epidemic prevention slogans emerged, mobilizing the general public to participate in the prevention and control of the epidemic through its simplicity, vividness and warning characteristics, which had a positive effect on the control of the epidemic. The mobilizing function of the anti-epidemic slogans of orientation, unity, promotion and identification is exercised.

Application of Imaging Examination Based on Deep Learning in the Diagnosis of Viral Senile Pneumonia.

Medical image classification technology, preferably which is based on the deep learning, is not only a key auxiliary diagnosis and treatment method in clinical medicine but also an important direction of scientific research. With the intensification of social aging, the incidence of viral elderly pneumonia has been on the rise and needs dedication from the research community. Doctors rely on personal theories and experience to use traditional methods to check the computed tomography (CT) images of the lungs of elderly patients one by one, which is likely to cause diagnosis errors. The accuracy of the traditional method certainly meets the clinical needs, but it has higher requirements on the theory and experience of medical staff, and the classification efficiency is low. Constructing an accurate and fast auxiliary system can effectively save medical resources. In response to the above problems, we have proposed a viral pneumonia diagnosis method for lung CT images, which is based on the convolutional neural networks. The main research work is carried out around the following aspects: First, in the lung CT image classification task, the traditional methods are inefficient and effective for doctors. The basic quality requirements of the model are high, or, in the model training, the effective training data are small, and so forth, causing problems such as model overfitting. A lung CT classification model based on the improved Inception-ResNet is proposed. In this model, first the overall architecture of the network model is designed, and then the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm is used to perform the same image enhancement processing on the dataset and data needed in this article, and then the pictures pass through three different network models. A binary classification study was carried out on viral pneumonia and normal lung images, and finally the accuracy, sensitivity, and specificity of the three models were compared. The experimental results show that the accuracy of the three models for the judgment of viral pneumonia is more than 95%. Among these, the model proposed in this article has better classification effect and fit, the highest accuracy rate, and less parameters and can be used for rapid screening of viral senile pneumonia. Objective. To complete the classification of lung CT images of the elderly with viral pneumonia based on the improved Inception-ResNet network architecture. Methods. (1) Find and study domestic and foreign medical literature, understand the diagnosis and treatment methods of viral pneumonia, and study lung CT imaging; compare the pattern classifications of deep learning in lung imaging at home and abroad, and further study the application of convolutional neural networks in the medical field application. (2) Study various models and technologies of convolutional neural networks, summarize them separately, and have in-depth understanding of convolutional neural networks, including architecture, methods, and related system frameworks, experimental environments, and so forth. Results. This paper proposes an optimized Inception-ResNet network architecture for image classification. The control experimental model uses two network models, GoogLeNet and ResNet, and selects the viral pneumonia dataset for training and testing. The experimental results are as follows: the sensitivity and specificity are superior to those in the other two models, which can be used for actual medical screening and diagnosis. Conclusion. The improved Inception-ResNet network model method in this paper performs better in terms of accuracy, sensitivity, and specificity. Every metric is higher than those in the ResNet model and the GoogLeNet model, improving the classification effect. In addition, it can be seen from the experimental results that the model used in this paper has a very good classification effect in the classification of new coronary pneumonia CT image data.

Research on Information Visualization Design for Reducing Cognitive Anxiety during the Epidemic

Purpose: To reduce people’s cognitive anxiety during the special period of the epidemic through the use of information visualization methods, and to build a psychological construction for correct understanding of the epidemic. Methods: Analyze the causes of cognitive anxiety, and propose to reduce cognitive anxiety through data screening and display and visualization design in information visualization design, to reduce people’s cognitive anxiety in emergencies. Conclusion: Through the design of the new coronary pneumonia epidemic data visualization network platform, it provides a valuable model for the research on how to reduce cognitive anxiety through information visualization design.

Implementation of Conflict Resolution Strategies for Hospital Nurses during the Novel Coronavirus Pneumonia Pandemic and Its Impact on Nurses’ Stress Levels

The sudden and large-scale outbreak of the new coronavirus virus requires a large number of medical staff to treat and care for patients with new coronary pneumonia. Nurses not only have to undertake a large amount of nursing work, but also need to face the risk of infection, need special protection, and be affected by work. Forced isolation and away from the family and some patients are critically ill. Understand the implementation of conflict resolution strategies for nursing staff in hospitals during the new coronavirus pneumonia pandemic and its impact on nurses’ stress levels, providing references for improving nursing staff’s physical and mental health and work efficiency, as well as nursing staff in future public health emergencies Take various response strategies to provide a basis.

Heart rhythm and autonomic regulation disorders in chronic coronary syndrome patients with community-aсquired pneumonia

Pneumonia presents a considerable challenge in patients with cardiovascular disease due to an increase in the incidence, difficulties of diagnosis and treatment, high mortality. Aim: to study the characteristics of cardiac arrhythmias and heart rhythm autonomic regulation in patients with chronic coronary syndrome (CCS) and community-acquired pneumonia (CAP), and to define their relationship with the clinical features of the disease. Materials and methods. A monocenter cross-sectional study analyzed 90 patients with CCS in parallel groups. The main group included 60 CCS patients with CAP; the control group consisted of 30 patients without concomitant CAP. A complex clinical examination of patients was performed on 1–3 days of hospital stay in accordance with the National Recommendations. Holter ECG monitoring was performed using a CARDIOSENS K device (XAI-MEDICA, Ukraine). Results. Based on the results of 24-hour ECG monitoring, heart rhythm disorders, increased duration of myocardial ischemia and ST-segment depression depth with an increase in the total duration of tachycardia episodes within 24 hours were more common in the main group patients. The severity of CAP on the PSI/PORT scale was correlated with the 24-hour mean heart rate (r = +0.31, P &lt; 0.05), the number of ventricular extrasystoles – with respiratory rate (r = +0.29, P &lt; 0.05), supraventricular extrasystoles – with the duration of ST-segment depression (r = +0.57, P &lt; 0.05). In patients with CCS and CAP, there was a decrease in the total heart rate variability (HRV), mainly in the passive period, combined with an increase in the LF/HF ratio and stress index (SI), which was directly correlated with the severity of CAP and intoxication syndrome. Conclusions. Patients with CCS and CAP are characterized by the increased 24-hour heart rate, duration of ST-segment depression, frequency of supraventricular and ventricular arrhythmias on 24-hour Holter monitoring, paroxysms of atrial fibrillation with the decreased total HRV combined with significantly increased activity of the sympathetic autonomic nervous system.

Prediction of asymptomatic COVID-19 infections based on complex network.

Novel coronavirus pneumonia (COVID‐19) epidemic outbreak at the end of 2019 and threaten global public health, social stability, and economic development, which is characterized by highly contagious and asymptomatic infections. At present, governments around the world are taking decisive action to limit the human and economic impact of COVID‐19, but very few interventions have been made to target the transmission of asymptomatic infected individuals. Thus, it is a quite crucial and complex problem to make accurate forecasts of epidemic trends, which many types of research dedicated to deal with it. In this article, we set up a novel COVID‐19 transmission model by introducing traditional SEIR (susceptible‐exposed‐infected‐removed) disease transmission models into complex network and propose an effective prediction algorithm based on the traditional machine learning algorithm TrustRank, which can predict asymptomatic infected individuals in a population contact network. Our simulation results show that our method largely outperforms the graph neural network algorithm for new coronary pneumonia prediction and our method is also robust and gives good results even if the network information is incomplete.

Research on the Fluctuation Trend of the Spread of New Coronary Pneumonia Epidemic under Non-Strict Prevention and Control Conditions: Analysis Based on the Data of India’s Epidemic

After a long time of joint efforts, the Covid-19 pandemic in some countries and regions has been effectively controlled, but since April 2021, the outbreak of the epidemic in India has posed new challenges for the prevention and control of the global epidemic. How to understand and interpret the rebound trend of the epidemic in India is an important reference for other regions to prevent and control the epidemic. Based on time-series analysis, this paper uses the system dynamics epidemic model to treat the population in the region as a whole and decomposes the spread of the Covid-19 in India into 4 fluctuating transmission processes. These processes show that the spread of the Covid-19 may have annual periodic characteristics and long-term trends. There is a critical period of about 45 days before the outbreak of the epidemic. The global prevention and control requires the joint efforts of all countries to end as soon as possible.

The impact of COVID-19 pandemic on the treatment of acute appendicitis in China

ObjectiveThis research aims to analyze the impact of the novel coronavirus pandemic on the hospital visits of patients with acute appendicitis.MethodsThe retrospective analysis was designed to look at the treatment of acute appendicitis in the Department of General Surgery in Beijing Jishuitan Hospital before and during the COVID-19 pandemic (2019–2020). Data was analyzed by the numbers of patients, sex, age, onset time, fever or not, laboratory examination, imaging test, and treatment. And we analyzed the differences between the “pre-COVID group” and “during-COVID group”.ResultsCompared with the year 2019, the number of acute appendicitis patients has diminished substantially during the COVID-19 pandemic (2020), but the number elevated with the control of the pandemic. Even if we did not find the differences of the treatment before and during the pandemic (P = 0.932), the onset time to emergency was significantly longer (P < 0.001), and more patients had showed fever (P < 0.001) during the COVID-19 pandemic. And the total number of white blood cells and C reactive protein level were significantly higher in 2020 than those in 2019 (P = 0.006, 0.003). And the same result was found in patients with appendiceal fecalith (P = 0.047).ConclusionDuring the pandemic of the new coronavirus pneumonia, the number of patients with acute appendix treatment dropped significantly, mainly because it took longer than before, and the condition was more severe. It can be seen that the new coronary pneumonia has a great impact on the patients’ medical treatment behavior, and the active prevention and treatment of the new coronavirus pneumonia is currently an important and urgent issue.

Design and engineering application of medical oxygen supply system in novel coronavirus pneumonia treatment hospital

Respiratory supporting, as an important medical treatment for new coronavirus pneumonia patients, must be effectively guaranteed by medical oxygen supply. However, the medical oxygen system designed and configured by the existing hospitals according to the current specifications cannot meet the oxygen needs for patients with new coronavirus pneumonia. This paper aimed to study the design of medical oxygen system in new coronavirus pneumonia emergency hospital. By investigating the oxygen treatment plan for the novel coronavirus pneumonia patients in the health emergency hospital, the oxygen treatment characteristics of different patients were studied. The oxygen characteristics of different respiratory support terminals were explored to study the oxygen demands of new coronavirus pneumonia emergency hospitals. Through calculating flow rates of medical gas system air source referring to ‘technical code for medical gases engineering’, the proportion coefficient of severe patients converted into respiratory distress patients was introduced, and the model of calculating flow rates of medical oxygen system air source in emergency hospital was proposed. The cases were verified in a typical health emergency hospital that the developed calculation flow model of medical oxygen source met the demands of hospital oxygen. The outcomes provide a reference for the design and construction of medical oxygen in such health emergency hospitals.

A review and update for registered clinical studies of stem cells for non-tumorous and non-hematological diseases.

ObjectiveTo provide consultative information on selecting potential indications of stem cells in the treatment of non-tumorous and non-hematopoietic system conditions, we screened the clinical trials on website: http://www.clinicaltrials.gov.MethodsA literature search was conducted using the National Institute of Health (NIH) website (http://www.clinicaltrials.gov) from May 10th, 2012 to Oct 13th, 2020 with stem cells as an intervention in human trials for non-tumorous and non-hematological conditions. There was no restriction for language, research location, and research race.Results and conclusionSearch terms initially found a total of 3576 articles. Firstly, 138 terminated or suspended studies were excluded and further 24 repeated studies were excluded. Secondly, 987 tumorous and hematopoietic conditions-related studies were excluded. Lastly, 1218 studies were excluded without stem cell therapy as their primary purpose. A total of 1209 research studies were entered into the analysis and reviewed. The top 4 diseases were about motor system diseases 229 (19.28%). In addition, 206 (17.34%) studies were related to central nervous system (CNS) diseases. 140 (11.78%) were autoimmune diseases or graft-versus-host disease (GVHD) after organ transplantation. 129 (10.86%) were about respiratory system diseases, among them, 44.19% projects were about new coronary pneumonia (NCP). The main cell types used in various diseases are Mesenchymal Stem Cells (MSCs), bone marrow stem cells (BM-SCs), peripheral blood stem cells (PBSCs), hematopoietic stem cells (HSCs), adipose stem cell (ASCs) and precursor cells.

Forecast of the COVID-19 Epidemic Based on RF-BOA-LightGBM.

In this paper, we utilize the Internet big data tool, namely Baidu Index, to predict the development trend of the new coronavirus pneumonia epidemic to obtain further data. By selecting appropriate keywords, we can collect the data of COVID-19 cases in China between 1 January 2020 and 1 April 2020. After preprocessing the data set, the optimal sub-data set can be obtained by using random forest feature selection method. The optimization results of the seven hyperparameters of the LightGBM model by grid search, random search and Bayesian optimization algorithms are compared. The experimental results show that applying the data set obtained from the Baidu Index to the Bayesian-optimized LightGBM model can better predict the growth of the number of patients with new coronary pneumonias, and also help people to make accurate judgments to the development trend of the new coronary pneumonia.

Computer Simulation and Epidemic Preventive Countermeasures Using SEIR Model and Big Data Technology

Object: Evaluation of various new type of coronavirus pneumonia (COVID-19) normalization prevention measures and methods: Based on the actual data and transmission characteristics of the COVID-19 epidemic, this paper constructs an SEIR model for the transmission of new coronary pneumonia, which focuses on parameters such as asymptomatic infection. Result: Simulate the implementation of different types of public health preventive policies, and explore normalized preventive countermeasures.

Discovery of novel inhibitors of SARS-CoV-2 main protease

Corona Virus Disease 2019 (COVID-19), referred to as ‘New Coronary Pneumonia’, is a type of acute infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Mpro is one of the main targets for treating COVID-19. The current research on Mpro mainly focuses on the repurposing of old drugs, and there are only a few novel ligands that inhibit Mpro. In this research, we used computational free energy calculation to screen a compound library against Mpro, and discovered four novel compounds with the two best compounds (AG-690/13507628 and AG-690/13507724) having experimental measured IC50 of just under 3 μM and low cell toxicity. Detailed decomposition of the interactions between the inhibitors and Mpro reveals key interacting residues and interactions that determine the activity. The results from this study should provide a basis for further development of anti-SARS-CoV-2 drugs. Communicated by Ramaswamy H. Sarma

ANALYSIS ON COVID-19 WITH MATHEMATICAL MODELLING

Background: An infectious disease caused by a novel coronavirus called COVID-19 has raged across the world since December 2019. The novel coronavirus first appeared in Wuhan, China, and quickly spread to Asia and now many countries around the world are affected by the epidemic. The deaths of many patients, including medical staff, caused social panic, media attention, and high attention from governments and world organizations. Today, with the joint efforts of the government, the doctors and all walks of life, the epidemic in Hubei Province has been brought under control, preventing its spread from affecting the lives of the people. Because of its rapid spread and serious consequences, this sudden novel coronary pneumonia epidemic has become an important social hot spot event. Through the analysis of the novel coronary pneumonia epidemic situation, we can also have a better understanding of sudden infectious diseases in the future, so that we can take more effective response measures, establish a truly predictable and provide reliable and sufficient information for prevention and control model. Methods: We establish different models according to the different developments of the epidemic situation, different time points, and different response measures taken by the government. To be specific, during the period of 2020.1.23-2020.2.7, the traditional SIR model is adopted; during the period of 2020.2.8- 2020.3.30, according to the scientific research results, it was considered that the novel coronary pneumonia has a latent period, so in the later phase of epidemic development, the government has effectively isolated patients, thus we adopt the SEIQR model accordingly. During the period of 2020.3.31-2020.5.16, because more asymptomatic infected people were found, we use the SEIQLR model to fit. Finally, through a SEIR simulator, considering the susceptible number, the latent number, the infected number, the cured number, death number and other factors, we simulate the change of various numbers of people from the beginning to the next 180 days of novel coronary pneumonia. Findings: The results based on the analysis of differential equations and kinetic models show that through the prediction of the model established in the first phase, the epidemic situation of novel coronary pneumonia in Hubei Province was controlled at the end of March, which is in line with the actual situation. The rest of Hubei province, except for Wuhan, lifted control of the departure channel from 0:00 am on March 25, and Wuhan was also unblocked on April 8. Through the establishment of the second-phase model, it is found that the epidemic situation will reach its peak in mid-February. For example, the quarantine admission of the hospital declined after mid-February, which is inseparable from the measures to build square cabin hospitals in early February so that more and more patients can be admitted. The model established in the third phase shows that the epidemic had been completely controlled by the end of May, which is also in line with the reality. Because in mid-May, the Wuhan government conducted a nucleic acid test on all the citizens to screen for asymptomatic infected persons to fundamentally control the spread of novel coronary pneumonia. Interpretation: Hubei Province, as the center of the initial outbreak of novel coronary pneumonia, people were forced to be isolated at home during the Spring Festival, the most important Chinese holiday, and the whole society was in a state of suspension of work and study. The Chinese government had taken many measures in response to the epidemic, such as shutting down the city, vigorously building square cabin hospitals, and prohibiting people from gathering. At the beginning of May this year, the epidemic in Hubei Province was finally effectively controlled. For ordinary citizens, we should not cause unnecessary panic about the unknown novel coronavirus. Instead, we should fully understand and be familiar with this virus. In addition to the relevant medical knowledge, we should also understand the spread of infectious diseases through appropriate mathematical models. By mathematical models, we can understand the degree of harm of infectious diseases, when to control it, how to stop it, and use scientific views to reveal the original face of the novel coronavirus to the public without causing social panic.

Immune Response and Safety to Inactivated COVID-19 Vaccine: A Comparison Between People Living with HIV and HIV-naïve Individuals

Background: Multi-types COVID-19 vaccines have shown safety and efficacy against COVID-19 in healthy adults. Although current guidelines encourage people living with HIV(PLWH) to take COVID-19 vaccines, whether their immune response to COVID-19 vaccines is distinct from HIV-free individuals is still unclear.Methods: Between March to June 2021, 48 PLWH and 40 HNC, aged 18 to 59 years, were enrolled in the study. All of them received inactivated COVID-19 vaccine at day 0 and the second dose at day 28. The primary safety outcome was the combined adverse reactions within 7days after each injection. The primary immunogenicity outcomes were neutralizing antibodies (nAbs) responses by chemiluminescence and total specific IgM and IgG antibodies responses by ELISA and colloidal gold at baseline (day 0), day 14, day 28, day 42, and day 70.Findings: In total, the study included 46 PLWH and 38 HNC who finished 70 days’ follow-up. The frequency of adverse reactions to the first and second dose was not different between PLWH (30% and 11%) vs HNC (32% and 24%). NAbs responses among PLWH peaked at day 70, while among HNC peaked at day 42. At day 42, the geometric mean concentration (GMC) and seroconversion rate of nAbs among PLWH were 4.46 binding antibody units (BAU)/mL (95% CI, 3.18-5.87) and 26% (95% CI, 14-41), which were lower than that among HNC [GMC (18.28 BAU/mL, 95% CI, 10.33-32.33), seroconversion rate (63%, 95% CI, 44-79)]. IgG responses among both PLWH and HNC peaked at day 70. At day 70, the geometric mean ELISA units (GMEU) and seroconversion rate of IgG among PLWH were 0.193 ELISA units (EU)/mL (95% CI, 0.119-0.313) and 51% (95% CI, 34-69), which was lower than that among HNC [GMEU (0.379 BAU/mL, 95% CI, 0.224-0.653), seroconversion rate (86%, 95% CI, 64-97)].Interpretation: Early humoral immune response to the inactivated COVID-19 vaccine was weaker and delayed among the PLWH population than that among HNC. This observation remained consistent regardless of a high CD4 count and a low HIV viral load suppressed by ART.Funding: This work was supported by the National Key Research and Development Program of China (2017YFE0103800), the National Nature Science Foundation of China (81903371), NIMH (R34MH119963), the National Science and Technology Major Project (2018ZX10101-001-001-003), and Special Found on Prevention and Control of New Coronary Pneumonia in Guangdong Universities (2020KZDZX1047), Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital, Wuhan University (PTXM2020008), Science and Technology Innovation Cultivation Fund of Zhongnan Hospital, Wuhan University (cxpy2017043). Medical Science Advancement Program (Basic Medical Sciences) of Wuhan University (TFJC2018004).Declaration of Interest: The authors declare no conflict of interest.Ethical Approval: The study was approved by the Research and Ethics Committee of Zhongnan Hospital, Wuhan University, P. R. China (2020079K-1). Informed consent was obtained from all individuals enrolled in this study.

COVID-19 Epidemic Prediction Model and Prevention and Control Analysis

COVID-19 infection was found in Wuhan area in early 2020. Because of the Data backlog in the initial period of Hubei province, it was not conducive to accurate establishment of the model. This article selected the number of diagnosed people in 40 districts and counties of Chongqing City near Hubei province for analysis and modeling. And the prediction model is divided into three stages. The first stage is the initial stage of prevention and control, and the virus transmission shows an exponential model growth. The second stage is the development period of prevention and control, the virus transmission shows a fluctuating state. In the third stage, which is in the mature stage of prevention and control, the virus spreads as a linear function. At the same time, the data curve of three stages and the reasons for its formation are analyzed. The laws revealed by this model have a certain theoretical basis for the prevention and control of new infectious diseases such as coronary pneumonia, which will have a certain guiding effect on the prevention and control of viruses in newly discovered countries and regions of the epidemic.

Morphological Group Theory of Soft Matter Structure - A New Mathematics of Natural Image Numbers

The accurate understanding and interpretation of the SARS-CoV-2 electron microscope photos (EMP) is of great significance for the scientific treatment of coronary pneumonia and precise strategy. Here I study the image mathematics and physics description of micro-images of soft matter system by the morphology group theory of material structure, and show that there is a natural image numbers equation, which can be used to describe not only the common feature of soft matter structure, but also to correct classify the states into different classes, so that to study the morphology physics for soft matter science and biology, some important basic concepts of biophysics, such as the central dogma of molecular biology, Gene sequence analysis, heuristic recognition of SARS-CoV-2 can also be newly explained and understood by the equation.