Analysis of the Curative Effect of Cold-Dampness Epidemic Prescription(寒湿疫方)on the Main Symptoms of Quarantined People in Community

Objective:To evaluate the curative effect of"Cold-Dampness Epidemic Prescription(寒湿疫方)"on the main symptoms of quarantined people in community based on real-world research data,and to provide scientific basis for Chinese medicine to fight Coronavirus disease 2019 under the"Wuchang Model".Methods:From February 4,2020 to March 5,2020,the data of community quarantined people who only took"Cold-Dampness Epidemic Prescription(寒湿疫方)"under the management of"Wuchang Model"were collected,and the demographic characteristics,basic diseases,distribution of sick people,treatment process,disappearance time of eight clinical symptoms,adverse reactions and clinical outcomes of patients were statistically analyzed.Results:A total of 951 quarantined people in COVID-19 community were included,including 440 males(46.27%)and 511 females(53.73%),the oldest was 89 years old,the youngest was 2 years old,and the average age was 43.34 years old;Among the quarantined population in the community,there were 141 cases diagnosed by CDC(14.83%),CDC excluded 810 cases(85.17%);106 cases of CDC were diagnosed as COVID-19,accounting for 47.96%of the self-reported cases,and 311 cases(32.70%)had previous medical history;696(73.19%)had no obvious symptoms,and 255(26.81%)developed fever,fatigue,cough,sputum,shortness of breath,loss of appetite,diarrhea and mood,of which 187(19.66%)had cough symptoms,followed by sputum coughing(14.30%)and fever(13.56%).Conclusion:"Cold-Dampness Epidemic Prescription(寒湿疫方)"is effective in treating the clinical symptoms of COVID-19 without obvious adverse reactions.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Individual dynamics and local heterogeneity provide a microscopic view of the epidemic spreading

The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the widely used deterministic compartmental models have qualitatively presented continuous “analytical” insight and captured some transmission features,their treatment usually lacks spatiotemporal variation.Here,we propose a stochastic individual dynamical(SID)model to mimic the random and heterogeneous nature of epidemic propagation.The SID model provides a unifying framework for representing the spatiotemporal variations of epidemic development by tracking the movements of each individual.Using this model,we reproduce the infection curves for COVID-19 cases in different areas globally and find the local dynamics and heterogeneity at the individual level that affect the disease outbreak.The macroscopic trend of virus spreading is clearly illustrated from the microscopic perspective,enabling a quantitative assessment of different interventions.Seemingly,this model is also applicable to studying stochastic processes at the “meter scale”,e.g.,human society’s collective dynamics.

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.

Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome:Modeling the Epidemic in Sichuan Province,China

Objective Our study aimed to provide a comprehensive overview of the current status and dynamic trends of the human immunodeficiency virus(HIV)prevalence in Sichuan,the second most heavily affected province in China,and to explore future interventions.Methods The epidemiological,behavioral,and population census data from multiple sources were analyzed to extract inputs for an acquired immunodeficiency syndrome(AIDS)epidemic model(AEM).Baseline curves,derived from historical trends in HIV prevalence,were used,and the AEM was employed to examine future intervention scenarios.Results In 2015,the modeled data suggested an adult HIV prevalence of 0.191%in Sichuan,with an estimated 128,766 people living with HIV/AIDS and 16,983 individuals with newly diagnosed infections.Considering current high-risk behaviors,the model predicts an increase in the adult prevalence to 0.306%by 2025,projecting an estimated 212,168 people living with HIV/AIDS and 16,555 individuals with newly diagnosed infections.Conclusion Heterosexual transmission will likely emerge as the primary mode of AIDS transmission in Sichuan.Furthermore,we anticipate a stabilization in the incidence of AIDS with a concurrent increase in prevalence.Implementing comprehensive intervention measures aimed at high-risk groups could effectively alleviate the spread of AIDS in Sichuan.

Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments

The control of highly contagious disease spreading in campuses is a critical challenge.In residential universities,students attend classes according to a curriculum schedule,and mainly pack into classrooms,dining halls and dorms.They move from one place to another.To simulate such environments,we propose an agent-based susceptible–infected–recovered model with time-varying heterogeneous contact networks.In close environments,maintaining physical distancing is the most widely recommended and encouraged non-pharmaceutical intervention.It can be easily realized by using larger classrooms,adopting staggered dining hours,decreasing the number of students per dorm and so on.Their real-world influence remains uncertain.With numerical simulations,we obtain epidemic thresholds.The effect of such countermeasures on reducing the number of disease cases is also quantitatively evaluated.

Epidemic Characteristics and Spatio-Temporal Patterns of HFRS in Qingdao City,China,2010-2022

Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.

Vaccination and Measles Epidemic in the Republic of Congo

Measles is a highly infectious, vaccine-preventable viral disease. Often forgotten, it is potentially fatal. Its main symptoms are: fever, cough, conjunctivitis, rhinitis and skin rash. Serological diagnoses by ELISA test based on the detection of anti-measles virus immunoglobulins M and G (IgM and IgG), are used respectively for the confirmation of suspected cases notified by means of clinical signs of the disease in health structures of the twelve departments of the Republic of Congo involved in the epidemiological surveillance of the disease, and for the evaluation of the immunity conferred by vaccination. During 2021, 459 suspected cases of measles were reported and sampled throughout the country, despite the administration under the Expanded Routine Immunization Program (EPI), of 2 doses of the combined measles-rubella vaccine (RR1 and RR2) in 2020. The notification rate was higher in the south of the country, more precisely in the health structures of the departments of Pointe-Noire (42%) and Brazzaville (11%). During the year 2021, 459 suspected cases of measles were notified and sampled throughout the country with a high notification rate in the south of the country, more precisely in the health structures of the Pointe-Noire departments (42%) and Brazzaville (11%). The samples consisting of human blood (serum) were sent to the National Public Health Laboratory and analyzed by various ELISA tests for the detection of anti-measles immunog-lobulins M and G. The analysis of the results obtained shows that the measles virus circulated in all departments of the country. 154 cases (33.55%) were confirmed positive by IgM ELISA and 98 positive cases (63.63%) were patients vaccinated against measles. Vaccination coverage in RR1 of [50%-95% [(first dose) as well as the lowest RR2 < 50% (second dose) undoubtedly because the COVID-19 pandemic could be the cause of the high frequency of cases positive vaccinated. The non-detection of IgG immunoglobulins in vaccinated patients observed by IgG ELISA tests revealed that 63.26% of vaccinated patients were not immunized against the measles virus. These results confirm those obtained during the Elisa IgM analysis and make it possible to deduce that the quality, the number of doses not properly administered, the individual characteristics of the people as well as the poor conservation of the administered vaccine (non-compliance with the cold chain) would explain the high proportion of positive cases of vaccinated measles observed.

Relationship Between Individuals’Epidemic Risk Perception Within Living Space and Subjective Well-Being:Empirical Evidence from China after the First Wave of COVID-19

It is common to observe the epidemic risk perception(ERP)and a decline in subjective well-being(SWB)in the context of public health events,such as Corona Virus Disease 2019(COVID-19).However,there have been few studies exploring the impact of individuals’ERP within living space on their SWB,especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the pandemic.In this paper,we conducted a study with 789 participants in urban China,measuring their ERP within living space and examining its influence on their SWB using path analysis.The results indicated that individuals’ERP within their living space had a significant negative effect on their SWB.The density of certain types of facilities within their living space,such as bus stops,subway stations,restaurants,fast food shops,convenience shops,hospitals,and public toilets,had a significantly negative impact on their SWB,mediated by their ERP within living space.Additionally,participation in out-of-home work and other activities not only increased individuals’ERP within living space,but also strengthened its negative effect on their SWB.

Research on the Control Cover against Solenopsis invicta and Rapid Extinguishment of Its Epidemic

Based on the biological characteristics of Solenopsis invicta and the structural characteristics of its ant nest,a fast and efficient closed treatment device was developed.Compared with the simple chemical treatment commonly used at present,the developed treatment device(the ant nest control cover)is a fast and efficient method to exterminate S.invicta in 7 d,featured by short course,quick results and good effect.

Stochastic Bifurcation of an SIS Epidemic Model with Treatment and Immigration

In this paper, we investigate an SIS model with treatment and immigration. Firstly, the two-dimensional model is simplified by using the stochastic averaging method. Then, we derive the local stability of the stochastic system by computing the Lyapunov exponent of the linearized system. Further, the global stability of the stochastic model is analyzed based on the singular boundary theory. Moreover, we prove that the model undergoes a Hopf bifurcation and a pitchfork bifurcation. Finally, several numerical examples are provided to illustrate the theoretical results. .

Stability of a Delayed Stochastic Epidemic COVID-19 Model with Vaccination and with Differential Susceptibility

In this paper, we treat the spread of COVID-19 using a delayed stochastic SVIRS (Susceptible, Infected, Recovered, Susceptible) epidemic model with a general incidence rate and differential susceptibility. We start with a deterministic model, then add random perturbations on the contact rate using white noise to obtain a stochastic model. We first show that the delayed stochastic differential equation that describes the model has a unique global positive solution for any positive initial value. Under the condition R0 ≤ 1, we prove the almost sure asymptotic stability of the disease-free equilibrium of the model.

Historical review and reflections on the participation of acupuncture and moxibustion in the treatment of epidemics in the People’s Republic of China (from 1950 until now)

Since the establishment of the People’s Republic of China, both the Communist Party and the government have placed great emphasis on the advancement of traditional Chinese medicine. Acupuncture and moxibustion have been actively involved in combating major epidemics such as malaria, schistosomiasis, and COVID-19. This article conducts a historical review of these three significant cases to elucidate how the acupuncture community has effectively utilized its unique advantages and characteristics through theoretical discussions, clinical practices, experimental research, as well as receiving administrative leadership and political support from the Communist Party of China and government. We provide an objective evaluation of their effectiveness while summarizing historical experiences to serve as a reference for future utilization of acupuncture and moxibustion therapy in epidemic relief efforts. Additionally, propose four suggestions: strengthening Party leadership and enhancing political support;timely summarization of experiences to establish programs and systems;deepening scientific research by integrating experimental findings with clinical practice;focusing on public awareness campaigns and education to solidify grassroots foundations.

Impact of individual behavior adoption heterogeneity on epidemic transmission in multiplex networks

In recent years, the impact of information diffusion and individual behavior adoption patterns on epidemic transmission in complex networks has received significant attention. In the immunization behavior adoption process, different individuals often make behavioral decisions in different ways, and it is of good practical importance to study the influence of individual heterogeneity on the behavior adoption process. In this paper, we propose a three-layer coupled model to analyze the process of co-evolution of official information diffusion, immunization behavior adoption and epidemic transmission in multiplex networks, focusing on individual heterogeneity in behavior adoption patterns. Specifically, we investigate the impact of the credibility of social media and the risk sensitivity of the population on behavior adoption in further study of the effect of heterogeneity of behavior adoption on epidemic transmission. Then we use the microscopic Markov chain approach to describe the dynamic process and capture the evolution of the epidemic threshold. Finally, we conduct extensive simulations to prove our findings. Our results suggest that enhancing the credibility of social media can raise the epidemic transmission threshold, making it effective at controlling epidemic transmission during the dynamic process. In addition, improving an individuals' risk sensitivity, and thus their taking effective protective measures, can also reduce the number of infected individuals and delay the epidemic outbreak. Our study explores the role of individual heterogeneity in behavior adoption in real networks, more clearly models the effect of the credibility of social media and risk sensitivity of the population on the epidemic transmission dynamic, and provides a useful reference for managers to formulate epidemic control and prevention policies.

Dynamic Modeling and Analysis of Occult Transmission of Omicron SARS-CoV-2 Epidemic

At present, the Omicron variant is still the dominant strain in the global novel coronavirus pneumonia pandemic, and has the characteristics of concealed transmission, which brings heavy pressure to the health systems of different countries. Omicron infections were first found in Chinese Mainland in Tianjin in December 2021, and Omicron epidemic broke out in many parts of China in 2022. In order to enable the country and government to make scientific and accurate decisions in the face of the epidemic, it is particularly important to predict and analyze the relevant factors of Omicron’s covert transmission. In this paper, based on the official data of Jilin City and the improved SEIR dynamic model, through parameter estimation, the contact infection probability of symptomatic infected persons in Omicron infected patients is 0.4265, and the attenuation factor is 0.1440. Secondly, the influence of infectious duration in different incubation periods, asymptomatic infected persons and other factors on the epidemic situation in this area was compared. Finally, the scale of epidemic development was predicted and analyzed.

Epidemic to Different Strains of SERRATIA: Experience of Neonatal Intensive Care Unit: About 30 Cases

Serratia is an opportunistic pathogenic bacterium of the Enterobacteriaceae family, occasionally responsible for epidemics of nosocomial infections in critical departments;in particular in neonatal intensive care units Enterobacteriaceae form a large family of Gram-negative bacteria, which cause diseases of highly variable severity, due to distinct pathogenic mechanisms. This family is heterogeneous as it consists of about 30 genera of bacteria and more than 100 species. However, all these germs have in common their preferential location in the digestive system, some being part of the normal flora although they are also present in the environment. Several metabolic processes characterize this bacterial family. These include the ability to reduce nitrate to nitrite (for energy generation), ferment glucose, lack cytochrome oxidase, be aerobic or anaerobic, motile or immobile, … Enterobacteriaceae constitute more than 80% of the germs isolated in the laboratory: Escherichia, Salmonella, Shigella, Klebsiella, Enterobacter, Serratia, Proteus, Morganella and Yersinia are the rods most often found. Regarding nosocomial infection to Serratia, this bacterium colonizes the respiratory, digestive and urinary systems of patients, mainly responsible for bacteremia, infections of the lower respiratory tract, urinary and skin infections. Through our work, we report the epidemic experienced in the neonatal intensive care unit MOHAMMED VI university hospital, mother-child hospital MARRAKECH MOROCCO for three months from December at February 2023 interesting 30 newborns whose clinical presentation was different, the positive diagnosis was based on blood and geographical samples taken by the bacteriology department to isolate the offending germs, the therapeutic management of our patients consisted essentially of various hygiene measures in association with dual antibiotic therapy based on meropenem and amikacin. The evolution was marked by the death of 21 patients (70%) and the recovery of 9 patients (30%). The prognosis of infection by serratia remains bleak with high morbidity interest in prevention by respecting the rules of hygiene, which begins with hand disinfection.

A Neural Study of the Fractional Heroin Epidemic Model

This works intends to provide numerical solutions based on the nonlinear fractional order derivatives of the classical White and Comiskey model(NFD-WCM).The fractional order derivatives have provided authentic and accurate solutions for the NDF-WCM.The solutions of the fractional NFD-WCM are provided using the stochastic computing supervised algorithm named Levenberg-Marquard Backpropagation(LMB)based on neural networks(NNs).This regression approach combines gradient descent and Gauss-Newton iterative methods,which means finding a solution through the sequences of different calculations.WCM is used to demonstrate the heroin epidemics.Heroin has been on-growth world wide,mainly in Asia,Europe,and the USA.It is the fourth foremost cause of death due to taking an overdose in the USA.The nonlinear mathematical system NFD-WCM discusses the overall circumstance of different drug users,such as suspected groups,drug users without treatment,and drug users with treatment.The numerical results of NFD-WCM via LMB-NNs have been substantiated through training,testing,and validation measures.The stability and accuracy are then checked through the statistical tool,such asmean square error(MSE),error histogram,and fitness curves.The suggested methodology’s strength is demonstrated by the high convergence between the reference solutions and the solutions generated by adding the efficacy of a constructed solver LMB-NNs,with accuracy levels ranging from 10?9 to 10?10.

Study on Urban Resilience Based on the Analysis of Supply Guarantee Facilities during Epidemic Closure—A Case Study of Shanghai in 2022

In the case of major public emergencies, the ability of cities to resist shocks becomes very important and reflects urban resilience. This paper selected the supply guarantee facilities during the city-wide lockdown of Shanghai during March-May 2022 due to COVID-19 pandemic as the research object. In this research, the per capita supply guarantee capacity and supply guarantee overlay intensity were analyzed based on the official list of facilities, and the actual role of the supply guarantee facilities was investigated through a public research questionnaire. The thermodynamic diagram map that was generated based on phone visits reflected the actual distribution of the population. Then the level of “supply guarantee resilience” was evaluated and graded for each district of Shanghai. It was concluded that the overall level of supply guarantee resilience was high in the central city, divergence in the near suburban districts, and generally low in the remote suburban districts. Based on this, the paper summarized the problems of supply guarantee revealed in Shanghai during the epidemic lockdown as well as proposed strategies to improve the resilience of similar scenarios in the future. With the help of big data and public research tools, this paper aimed to provide ways and methods to evaluate the resilience of cities under major public emergencies.

Sustainable Community Epidemic Prevention Countermeasures against Public Health Emergencies

How to deal with public health emergencies is an important issue related to the normal life of community residents. However, after the corresponding understanding, it is found that a series of unsustainable epidemic prevention factors have been exposed in the community in the COVID-19, such as blocked access to basic materials, unsafe living environment, and lack of social platforms. In view of these problems, based on Maslow’s hierarchy of needs, the changes in the hierarchy of needs of residents during the epidemic were analyzed, and sustainable community epidemic prevention strategies were proposed from five aspects of material storage, safe environment, social platform, respect opportunity and self-worth realization, so as to provide effective reference for future response to public health emergencies.

AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent world.When an outbreak occurs,each country must have a well-coordinated and preventative plan to address the situation.Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living.Although intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern society.Here,we used an artificial intelligence AI-based system for disease prediction using an Artificial Neural Network(ANN).The ANN improved the regularization of the classification model,hence increasing its accuracy.The unconstrained opti-mization model reduced the classifier’s cost function to obtain the lowest possible cost.To verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed models.The proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.