Simulation Analysis of COVID-19 Epidemic Model in Wuhan Based on SIR Model

Based on SIR model, combined with the mode of COVID-19 epidemic spread in Wuhan, the SIR model of COVID-19 epidemic spread is constructed, which mainly includes three aspects: simulation of the average number of infected people in COVID-19, simulation of cross-infection in COVID-19 and simulation of contact infection in COVID-19. Using the results of these three simulations, we can predict the spread of COVID-19 epidemic in the region, and find out the methods to prevent and control the outbreak or spread of the epidemic. © 2023 SPIE.

Traffic-driven SIR epidemic spread dynamics on scale-free networks

Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.

Entropy Based Clustering of Viral Sequences

Clustering viral sequences allows us to characterize the composition and structure of intrahost and interhost viral populations, which play a crucial role in disease progression and epidemic spread. In this paper we propose and validate a new entropy based method for clustering aligned viral sequences considered as categorical data. The method finds a homogeneous clustering by minimizing information entropy rather than distance between sequences in the same cluster. We have applied our entropy based clustering method to SARS-CoV-2 viral sequencing data. We report the information content extracted from the sequences by entropy based clustering. Our method converges to similar minimum-entropy clusterings across different runs and limited permutations of data. We also show that a parallelized version of our tool is scalable to very large SARS-CoV-2 datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Epidemic Spread Modeling for COVID-19 Using Cross-fertilization of Mobility Data

We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020, to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals at the beginning of the pandemic. While the KCDC data offer a wealth of information, they are also by their nature limited. To compensate for their limitations, we use detailed mobility data from Berlin, Germany after observing that mobility of individuals is surprisingly similar in both Berlin and Seoul. Using information from the Berlin mobility data, we cross-fertilize the KCDC Seoul data set and use it to parameterize an agent-based simulation that models the spread of the disease in an urban environment. After validating the simulation predictions with ground truth infection spread in Seoul, we study the importance of each input parameter on the prediction accuracy, compare the performance of our model to state-of-the-art approaches, and show how to use the proposed model to evaluate different what-if counter-measure scenarios. IEEE

Image Recognition Based on Deep Learning with Thermal Camera Sensing

As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost. © 2023 CRL Publishing. All rights reserved.

Strategies for the sustainable development of China in the post-epidemic era

Countries around the world are facing enormous challenges in their economic and social development as COVID-19 continues to spread, resulting in slower economic recovery in the post-pandemic era. Considering the impact of economic growth on future sustainable development in this new era, green economic recovery (GER) can achieve a win-win situation between economic recovery and environmental improvement and bring forth environmentally sustainable economic growth. This research first lists related COVID-19 literature surveys and GER policies in the post-pandemic era in China. Based on a comparative study of the international experience of GER policy practices, this paper then analyzes the opportunities and challenges China faces for GER and puts forward countermeasures and suggestions on how to promote its sustainable development in the post-epidemic era. We believe our research presents useful enlightenments for sustainable economic and social development in the post-epidemic era. © 2022 ERP Environment and John Wiley & Sons Ltd.

Design of an Intelligent Robot for Back-Slap Sputum Excretion Based on Back Feature Recognition

Under the circumstance of COVID-19 epidemic spread, global medical resources are in serious shortage. As a common way of care for respiratory diseases, although back-slap sputum excretion can be used for the care of lung diseases, but it requires the cooperation of multiple medical staff, and lead to inefficient care. This paper designed a method of the human' s back feature recognition based on YOLOv5, and built a new type of intelligent robot for back-slap sputum excretion on this basis, which can assist care staff to complete the back-slap sputum excretion care for patients, and reduce the labor intensity of staff and the risk of cross infection. © 2022 IEEE.

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.

Optimal exit strategy design for COVID-19

In this paper an optimal control design approach is used to compute optimal strategies of intervention in terms of the combined actions of vaccination and social distancing. The aim is to define times and modalities of reduction of the individual limitations compatibly with a cost associated to the epidemic effects on the population healthy, to the social and economic consequences of the containment measures and to the vaccination campaign. A mathematical model, obtained adapting and improving a previously proposed and validated one, is introduced to this control design aim. Simulation results obtained for different choices of costs and constraints are also reported and discussed.

Modelling of spatial infection spread through heterogeneous population: from lattice to partial differential equation models.

We present a simple model for the spread of an infection that incorporates spatial variability in population density. Starting from first-principle considerations, we explore how a novel partial differential equation with state-dependent diffusion can be obtained. This model exhibits higher infection rates in the areas of higher population density-a feature that we argue to be consistent with epidemiological observations. The model also exhibits an infection wave, the speed of which varies with population density. In addition, we demonstrate the possibility that an infection can 'jump' (i.e. tunnel) across areas of low population density towards areas of high population density. We briefly touch upon the data reported for coronavirus spread in the Canadian province of Nova Scotia as a case example with a number of qualitatively similar features as our model. Lastly, we propose a number of generalizations of the model towards future studies.

GeoSpread: An Epidemic Spread Modeling Tool for COVID-19 Using Mobility Data

We present an individual-centric agent-based model and a flexible tool, GeoSpread, for studying and predicting the spread of viruses and diseases in urban settings. Using COVID-19 data collected by the Korean Center for Disease Control & Prevention (KCDC), we analyze patient and route data of infected people from January 20, 2020, to May 31, 2020, and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of population mobility and is used to parameterize GeoSpread to capture the spread of the disease. We validate simulation predictions from GeoSpread with ground truth and we evaluate different what-if counter-measure scenarios to illustrate the usefulness and flexibility of the tool for epidemic modeling. © 2022 Owner/Author.

Strategies for the sustainable development of China in the post‐epidemic era

Countries around the world are facing enormous challenges in their economic and social development as COVID-19 continues to spread, resulting in slower economic recovery in the post-pandemic era. Considering the impact of economic growth on future sustainable development in this new era, green economic recovery (GER) can achieve a win-win situation between economic recovery and environmental improvement and bring forth environmentally sustainable economic growth. This research first lists related COVID-19 literature surveys and GER policies in the post-pandemic era in China. Based on a comparative study of the international experience of GER policy practices, this paper then analyzes the opportunities and challenges China faces for GER and puts forward countermeasures and suggestions on how to promote its sustainable development in the post-epidemic era. We believe our research presents useful enlightenments for sustainable economic and social development in the post-epidemic era.

COVID-19 Spread Simulation in a Crowd Intelligence Network

In this paper, the Crowd Intelligence Network Model is applied to the simulation of epidemic spread. This model combines the multi-layer coupling network model and the two-stage feedback member model to study the epidemic spread mechanisms under multiple-scene intervention. First, this paper establishes a multi-layer coupled network structure based on the characteristic of Social Network, Information Network, and Monitor Network, namely, the Crowd Intelligence Network structure. Then, based on this structure, the digital-self model, which has a multiple-scene effect and two-stage feedback structure, is designed. It has an emotional state and infection state quantified by using attitude and self-protection levels. This paper uses the attitude level and self-protection level to quantify individual emotions and immune levels, and discusses the impact of individual emotions on epidemic prevention and control. Finally, the availability of the Crowd Intelligence Network Model on the epidemic spread is verified by comparing the simulation trend with the actual spread trend of COVID-19. © The author(s) 2022.

Simulation Research on COVID-19 Transmission and Control Measures Based on SEIiRD Model

With the spread of the novel coronavirus pneumonia around the world, the data and transmission mechanism are analyzed. The SEIiRD model is constructed based on the existing SEIRD model, and the infected population is divided into asymptomatic infections, mild infections, severe infections and critical infections. The impact of the transmission rate of different infected people on the development of the epidemic was analyzed. Simulation experiments were carried out on the basis of fitting real data, and it was found that the main infected populations that affected the discovery of the epidemic were asymptomatic and mildly infected. On this basis, the transmission rate of different asymptomatic and mildly infected people was further analyzed. The impact of different intervention times on the number of infections and deaths was simulated. Results show that the model can effectively simulate the spread of COVID-19 and provide decision-making support to the departments to implement corresponding epidemic prevention and control strategies. © 2022 Acta Simulata Systematica Sinica. All rights reserved.

Epidemic Spread Optimization for Disease Containment with NPIs and Vaccination

The potential impact of epidemics, e.g., COVID-19, H1N1, and SARS, is severe on public health, the economy, education, and society. Before effective treatments are available and vaccines are fully deployed, combining Non-Pharmaceutical Interventions (NPIs) and vaccination strategies is the main approaches to contain the epidemic or live with the virus. Therefore, research for deciding the best containment operations to contain the epidemic based on various objectives and concerns is much needed. In this paper, we formulate the problem of Containment Operation Optimization Design (COOD) that optimizes the epidemic containment by carefully analyzing contacts between individuals. We prove the hardness of COOD and propose an approximation algorithm, named Multi-Type Action Scheduling (MTAS), with the ideas of Infected Ratio, Contact Risk, and Severity Score to select and schedule appropriate actions that implement NPIs and allocate vaccines for different groups of people. We evaluate MTAS on real epidemic data of a population with real contacts and compare it against existing approaches in epidemic and misinformation containment. Experimental results demonstrate that MTAS improves at least 200% over the baselines in the test case of sustaining public health and the economy. Moreover, the applicability of MTAS to various epidemics of different dynamics is demonstrated, i.e., MTAS can effectively slow down the peak and reduce the number of infected individuals at the peak. © 2022 IEEE.

Research on Microblog Sentiment Analysis Based on Multimodal and Multiscale Feature Fusion

The pneumonia epidemic spread by the 2019 new coronavirus(2019-nCoV) has affected people's lives in any aspects, and has aroused widespread concern in global public opinion. In order to better grasp the real public opinion situation on the Internet and ensure the progress of epidemic prevention and public opinion analysis, this paper conducts research on netizen sentiment analysis for epidemic-related topics in the Internet community, and proposes a multimodal feature fusion solution. For the fusion of image and text modalities, Bi-LSTM and Bi-GRU are used to further learn the intrinsic correlation between modalities on the basis of bidirectional transformer feature fusion, and an image-based multi-scale feature fusion method is proposed, which can better solve the problem in this task. Experiments show that the method proposed in this paper is better than the current mainstream multimodal sentiment analysis methods. © 2022 IEEE.

Predicting the Dynamics of Covid-19 Propagation in Azerbaijan based on Time Series Models

The study is dedicated to developing an econometric model that can be used to make medium-term forecasts about the dynamics of the spread of the coronavirus in different countries, including Azerbaijan. We examine the number of COVID-19 cases and deaths worldwide to understand the data's intricacies better and make reliable predictions. Though it’s essential to quickly obtain an acceptable (although not perfect) prediction that shows the critical trends based on incomplete and inaccurate data, it is practically impossible to use standard SIR models of the epidemic spread. At the same time the similarity of the dynamics in different countries, including those which were several weeks ahead of Azerbaijan in the epidemic situation, and the possibility of including the heterogeneity factors into the model allowed as early as March 2020 to develop the extrapolation working relatively well on the medium-term horizon. The SARS-CoV-2 virus, which causes COVID-19, has affected societies worldwide, but the experiences have been vastly different. Countries' health-care and economic systems differ significantly, making policy responses such as testing, intermittent lockdowns, quarantine, contact tracing, mask-wearing, and social distancing. The study presented in this paper is based on the Exponential Growth Model method, which is used in statistical analysis, forecasting, and decision-making in public health and epidemiology. This model was created to forecast coronavirus spread dynamics under uncertainty over the medium term. The model predicts future values of the percentage increase in new cases for 1–2 months. Data from previous periods in the United States, Italy, Spain, France, Germany, and Azerbaijan were used. The simulation results confirmed that the proposed approach could be used to create medium-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Azerbaijan, the deviation of the predicted total number of confirmed cases from the actual number was within 3-10 percent. Based on March statistics on the spread of the coronavirus in the US, 4 European countries: Italy, Spain, France, Germany (most susceptible to the epidemic), and Azerbaijan, it was shown how the trajectory would deviate exponentially from a shape;a trial was carried out to identify and assess the key factors that characterize countries. One of the unexpected results was the impact of quarantine restrictions on the number of people infected. We also used the medium-term forecast set by the local government to assess the adequacy of health systems. © 2022, World Scientific and Engineering Academy and Society. All rights reserved.

PolSIRD: Modeling Epidemic Spread Under Intervention Policies: Analyzing the First Wave of COVID-19 in the USA.

Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in the absence of any intervention policies. In addition, these models assume full observability of disease cases and do not account for under-reporting. We present a mathematical model, namely PolSIRD, which accounts for the under-reporting by introducing an observation mechanism. It also captures the effects of intervention policies on the disease spread parameters by leveraging intervention policy data along with the reported disease cases. Furthermore, we allow our recurrent model to learn the initial hidden state of all compartments end-to-end along with other parameters via gradient-based training. We apply our model to the spread of the recent global outbreak of COVID-19 in the USA, where our model outperforms the methods employed by the CDC in predicting the spread. We also provide counterfactual simulations from our model to analyze the effect of lifting the intervention policies prematurely and our model correctly predicts the second wave of the epidemic.

Stochastic Modeling of Dynamics of the Spread of COVID-19 Infection Taking Into Account the Heterogeneity of Population According To Immunological, Clinical and Epidemiological Criteria

Here we present a stochastic model of the spread of Covid-19 infection in a certain region. The model is a continuous-discrete random process that takes into account a number of parallel processes, such as the non-stationary influx of latently infected individuals into the region, the passage by individuals of various stages of an infectious disease, the vaccination of the population, and the re-infection of some of the recovered and vaccinated individuals. The duration of stay of individuals in various stages of an infectious disease is described using distributions other than exponential. An algorithm for numerical statistical modeling of the dynamics of the spread of infection among the population of the region based on the Monte Carlo method has been developed. To calibrate the model, we used data describing the level of seroprevalence of the population of the Novosibirsk Region in the first wave of the Covid-19 epidemic in 2020. The results of computational experiments with the model are presented for studying the dynamics of the spread of infection under vaccination of the population of the region © 2022. Mathematical Biology and Bioinformatics.All Rights Reserved.

A Non-autonom SIR Model in Epidemiology

With the outbreak of COVID-19, the modelling of epidemic spread has once again become highly important. This paper introduces an epidemic spreading model with a changing infection rate. This model extends the traditional SIR (Susceptible – Infected – Removed) model. The SIR model is a dynamic model which divides individuals into 3 groups: susceptible, infected, and removed (including recovered and died). Individuals in each group have a constant proportion to change to the next group. This paper assumes the infection rate is dependent on the development cycle of the virus, which can vary in the different periods since being infected, instead of constants. This makes the differential equations a non-autonomous model. This paper works on how to fit the function of the infection rate and solve the equations. This paper uses Burr distribution which has 3 unknown parameters as the function of infection rate, and then discusses about two different methods to get these parameters—the least-squares method and the maximum likelihood estimation. As a numerical experiment of this model, this paper uses the data of COVID-19 in Ireland to make predictions and compare with the traditional SIR model. The non-autonomous model in this paper shows better performance than the traditionary SIR model. This new model might be potential in further epidemic simulation, and it is not hard to be combined with other extensions of the SIR model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.