具有跟踪隔离措施的新冠肺炎传播模型分析及应用

报道于2019年12月底的新型冠状病毒肺炎(COVID-19)疫情, 由于2020年春运期间人口的大规模流动, 使得其迅速蔓延.自2020年1月23日起, 我国采取了各种措施使得疫情得到了有效的控制, 例如武汉封城、确诊病例的密切接触者跟踪隔离、湖北人员的居家隔离等.该文基于COVID-19在山西省的实际传播情况, 建立了具有输入病例和确诊病例密切接触者跟踪隔离的动力学模型.在不考虑输入病例的情况下, 分析了模型的动力学行为.利用山西省COVID-19病例数据, 计算了实时再生数, 发现山西省2020 年1月25日全省封村封街道有效控制了COVID-19疫情的传播, 即实时再生数小于1, 从宏观角度验证了防控措施的有效性.进一步通过模型的数值拟合得到: 早期染病者隔离14天的防控策略是合理有效的; 武汉封城时间越早, 染病者的规模越小; 跟踪隔离到大量确诊病例的接触者时, 染病者的规模越小.     

Mathematical study of a risk-structured two-group model for Chlamydia transmission dynamics

A new two-group deterministic model for Chlamydia trachomatis, which stratifies the entire population based on risk of acquiring or transmitting infection, is designed and analyzed to gain insight into its transmission dynamics. The model is shown to exhibit the phenomenon of backward bifurcation, where a stable disease-free equilibrium (DFE) co-exists with one or more stable endemic equilibria when the associated reproduction number is less than unity. Unlike in some of the earlier modeling studies on Chlamydia transmission dynamics in a population, this study shows that the backward bifurcation phenomenon persists even if individuals who recovered from Chlamydia infection do not get re-infected. However, it is shown that the phenomenon can be removed if all the susceptible individuals are equally likely to acquire infection (i.e., for the case where the susceptible male and female populations are not stratified according to risk of acquiring infection). In such a case, the DFE of the resulting (reduced) model is globally-asymptotically stable when the associated reproduction number is less than unity and no re-infection of recovered individuals occurs. Thus, this study shows that stratifying the two-sex Chlamydia transmission model, presented in [1], according to the risk of acquiring or transmitting infection induces the phenomenon of backward bifurcation regardless of whether or not the re-infection of recovered individuals occurs.     

新冠疫情对我国信贷风险冲击的定量测算

文章建立了宏观经济传导模型和信贷风险传导模型,根据对GDP的估计,对新型冠状病毒感染肺炎疫情下的2020年全国和若干疫情严重省市的不良贷款率进行了定量测算.假设一季度内疫情得以控制,全年GDP增速下降至5.7％的情景下,预计全国年末不良贷款率约3％,不良贷款余额比2019年增加逾80％,接近2019年国内商业银行贷款损...     

Re-infection-induced backward bifurcation in the transmission dynamics of Chlamydia trachomatis

A new two-group deterministic model for Chlamydia trachomatis is designed and analyzed to gain insights into its transmission dynamics. The model is shown to exhibit the phenomenon of backward bifurcation, where a stable disease-free equilibrium (DFE) co-exists with one or more stable endemic equilibria when the associated reproduction number is less than unity. It is further shown that the backward bifurcation dynamic is caused by the re-infection of individuals who recovered from the disease. The epidemiological implication of this result is that the classical requirement of the reproduction number being less than unity becomes only a necessary, but not sufficient, condition for disease elimination. The basic model is extended to incorporate the use of treatment for infectious individuals (including those who show disease symptoms and those who do not). Rigorous analysis of the treatment model reveals that the use of treatment could have positive or negative population-level impact, depending on the sign of a certain epidemiological threshold. The treatment model is used to evaluate various treatment strategies, namely treating every infected individual showing symptoms of Chlamydia (universal strategy), treating only infectious males showing Chlamydia symptoms (male-only strategy) and treating only infectious females showing symptoms of Chlamydia (female-only strategy). Numerical simulations show that the implementation of the male-only or female-only strategy can induce an indirect benefit of saving new cases of Chlamydia infection in the opposite sex. Further, the universal strategy gives the highest reduction in the cumulative number of new cases of infection.     

Mathematical model for the novel coronavirus (2019-nCOV) with clinical data using fractional operator

Coronavirus infection (COVID-19) is a considerably dangerous disease with a high demise rate around the world. There is no known vaccination or medicine until our time because the unknown aspects of the virus are more significant than our theoretical and experimental knowledge. One of the most effective strategies for comprehending and controlling the spread of this epidemic is to model it using a powerful mathematical model. However, mathematical modeling with a fractional operator can provide explanations for the disease's possibility and severity. Accordingly, basic information will be provided to identify the kind of measure and intrusion that will be required to control the disease's progress. In this study, we propose using a fractional-order SEIARPQ model with the Caputo sense to model the coronavirus (COVID-19) pandemic, which has never been done before in the literature. The stability analysis, existence, uniqueness theorems, and numerical solutions of such a model are displayed. All results were numerically simulated using MATLAB programming. The current study supports the applicability and influence of fractional operators on real-world problems.     

COVID-19 Epidemic Prediction Based on Deep Learning

In this paper, a multi-layer gated recurrent unit neural network (multi-head GRU) model is proposed to predict the confirmed cases of the new crown epidemic (COVID-19). We extract the time series relationship in the data, and the rolling prediction method is adopted to ensure the simple structure of the model and achieve higher precision and interpretability. The prediction results of this model are compared with the LSTM model, the Transformer model and the infectious disease model (SIR). The results show that the proposed model has higher prediction accuracy. The mean absolute error (MAE) of epidemic prediction in most countries (the United States, Brazil, India, the United Kingdom and Russia) is respectively 197.52, 68.02, 200.67, 24.78 and 123.50, which is much smaller than the prediction error of the SIR model, LSTM model and Transformer model. For the spread of the COVID-19 epidemic, traditional infectious disease models and machine learning models cannot achieve more accurate predictions. In this paper, we use a GRU model to predict the real-time spread of COVID-19, which has fewer parameters and reduces the risk of overfitting to train faster. Meanwhile, it can make up for the shortcoming of the transformer model to capture local features.     

The Impact of Multilateral Imported Cases of COVID-19 on the Epidemic Control in China

While the spread of COVID-19 in China is under control, the pandemic is developing rapidly around the world. Due to the normal migration of population, China is facing the high risk from imported cases. The potential specific medicine and vaccine are still in the process of clinical trials. Currently,controlling the impact of imported cases is the key to prevent new outbreak of COVID-19 in China. In this paper, we propose two impulsive systems to describe the impact of multilateral imported cases of COVID-19. Based on the published data, we simulate and analyze the epidemic trends under different control strategies. In particular, we compare four different scenarios and show the corresponding medical burden. The results can be useful in designing appropriate control strategy for imported cases in practice.     

Effects of information-dependent vaccination behavior on coronavirus outbreak: insights from a SIRI model

A mathematical model is proposed to assess the effects of a vaccine on the time evolution of a coronavirus outbreak. The model has the basic structure of SIRI compartments (susceptible–infectious–recovered–infectious) and is implemented by taking into account of the behavioral changes of individuals in response to the available information on the status of the disease in the community. We found that the cumulative incidence may be significantly reduced when the information coverage is high enough and/or the information delay is short, especially when the reinfection rate is high enough to sustain the presence of the disease in the community. This analysis is inspired by the ongoing outbreak of a respiratory illness caused by the novel coronavirus COVID-19.

     

投资者情绪对科创板市场收益率的影响——基于文本数据挖掘视角

本文以科创板市场为主要研究对象,基于文本数据挖掘方法探究了新冠疫情发生前和疫情期间投资者情绪对市场收益率的影响及其作用机制。利用东方财富股吧2019年7月至2020年3月的日度科创板股票评论数据,基于Bi-LSTM深度学习技术对文本数据情感倾向进行分类,建立投资者情绪指数。通过构建双向固定效应的联立方程模型,采用2SLS方法估计投资者情绪对科创板市场收益率的作用,并检验在经济平稳运行和受新冠疫情冲击期间该作用的差异性。实证分析及稳健性检验的结果均表明,投资者情绪通过影响交易量进而影响科创板股票市场收益率,这种正向作用在1%的置信水平下显著。此外,投资者情绪对科创板收益率的影响在经济平稳运行和受新冠疫情冲击期间均保持稳健,且在新冠疫情期间作用更强。本研究成果对于新冠疫情期间我国证券市场监管层完善科创板交易机制,以及对中小投资者优化投资战略具有重要意义。     

Dynamical analysis of a sex-structured Chlamydia trachomatis transmission model with time delay

The effect of using time delay to model the latency period of Chlamydia trachomatis infection is explored, by designing a deterministic two-sex model for Chlamydia transmission dynamics in a population. The resulting delay differential equation model is shown to undergo the phenomenon of backward bifurcation, where a stable disease-free equilibrium co-exists with one or more stable endemic equilibria when the associated reproduction threshold is less than unity. This phenomenon arises due to the re-infection of individuals who recovered from the disease. Using permanence theory, it is shown that Chlamydia will persist in the population whenever the associated reproduction threshold exceeds unity. It is further shown that long latency period could induce positive (decrease disease burden) or negative (increase disease burden) population-level impact depending on the sign of a certain epidemiological threshold quantity and some other conditions. Furthermore, this study shows that adding a time delay (to model the latency period) does not alter the main equilibrium dynamics (with respect to the effective control or persistence of the disease in the community) of the corresponding non-delayed Chlamydia transmission model considered in our earlier study Sharomi and Gumel (2009) [7].     

一类潜伏期有传染性的传染病模型动力学分析

建立了一类潜伏期具备传染性的传染病传播模型,根据疾病传播规律求解了疾病消失和持续生存的阈值——基本再生数.对系统的稳定性进行了讨论,得到了系统稳定性条件.最后,以COVID-19为例,解释了各种举措在疾病控制中的作用,并对疫情传播扩散做了探讨和预测.     

Optimal control theory of an age-structured coronavirus disease model and the dynamical analysis of the underlying ordinary differential equation model having constant parameters

This paper addresses the dynamics of COVID-19 using the approach of age-structured modeling. A particular case of the model is presented by taking into account age-free parameters. The sub-model consisting of ordinary differential equations (ODEs) is investigated for possible equilibria, and qualitative aspects of the model are rigorously presented. In order to control the spread of the disease, we considered two age- and time-dependent non-pharmaceutical control measures in the age-structured model, and an optimal control problem using a general maximum principle of Pontryagin type is achieved. Finally, sample simulations are plotted which support our theoretical work.     

基于时滞动力学模型对钻石公主号邮轮疫情的分析

2019年末以来,新型冠状病毒肺炎迅速蔓延的疫情引发了全球关注.文献[5-6]提出了一类时滞动力学系统的新冠肺炎传播模型用以描述疫情的发展趋势.文献[7]在此基础上,结合CCDC统计数据,提出了一类基于CCDC统计数据的随机时滞动力学模型.本文将使用以上两类模型研究分析"钻石公主号"邮轮的疫情发展.基于日本厚生劳动省公布的数据,本文准确反演出模型参数,进而有效模拟当前疫情的发展,并预测疫情未来的趋势,发现在疫情爆发初期基本再生数R0(t)较大,而后随着防控措施加强而逐渐减小;约在2月下旬,累计确诊人数增长速度放缓,在3月上旬,累计确诊人数趋于稳定,即无新增确诊人数,疫情得到有效控制;最终累计确诊人数对隔离率变化敏感,隔离率升高,最终累计确诊人数将有显著下降.针对传染率较高、隔离率较低的问题,本文建议日本政府进一步加强防控措施,抑制疫情的大规模爆发.     

基于SEIR模型的COVID-19疫情防控效果评估和预测

通过对COVID-19疫情在中国的传播情况进行分析,建立了一个SEIR流行病模型,模型中将确诊人群分成已收治和未收治两类.先从理论上分析了模型的无病平衡点及其稳定性、基本再生数等关键问题;再结合实际数据,对武汉封城前和封城后两个阶段疫情的发展趋势进行数值模拟和比较分析,讨论了模型中一些重要参数对确诊人数的影响;最后,针...     

Regional Prediction of COVID-19 in the United States Based on the Difference Equation Model

The novel coronavirus pneumonia 2019 (COVID-19) has swept the globe in just a few months with negative social and psychological consequences for public health. So far, the United States has been one of the countries most affected by the epidemic. In this study, 51 states in the United States are divided into 10 state clusters according to relevant factors, and a difference equation model with spatio-temporal dynamic characteristics is established to predict the transmission dynamics of COVID-19 in the 10 state clusters and obtain data on regional aggregation levels (the United States). The study showed that the Pearson Correlation Coefficient between the actual data and the predicted data in the 10 state clusters is between 0.6 and 0.96 (mean R$^{2}$=0.8448), and the mean absolute error (MAE) of the newly confirmed cases in each cluster is between 300 and 1650 (mean MAE=878) and the average forecasting error rate (AFER) of the total confirmed cases in each cluster is between 0.9$\%$ and 3$\%$ (mean AFER=1.57$\%$). These results show that the difference equation model can well predict the changes in the recent confirmed cases of infectious diseases such as COVID-19.     

Forecasting a Cyclical Downturn (Recession) in the US Economy Using a Mathematical Model of Hyman Minsky’s Theory of Financial Instability

Doklady Mathematics - By using the US economy as an example, the paper shows how the COVID-19 pandemic has changed its short-term dynamics, causing a deep crisis recession in 2020 rather than the...     

基于灰色预测模型的广州港货运发展研究

新冠肺炎疫情对广州港的货运发展产生了一定程度的影响,但目前仍然缺少相关的定量研究.基于灰色预测模型,利用港口货物吞吐量、外贸货物吞吐量、集装箱吞吐量三个货运指标,在新冠肺炎疫情没发生的这一假设下,推测2020年1-8月广州港的各货运发展指标值.基于预测值与实际值的比较,定量研究广州港的货运发展受新冠肺炎的影响值,反映新...     

非线性组合动态传播率模型与我国COVID-19疫情分析和预测

针对传统的流行性传染病学中基本传染数R0难以准确估计以及单一模型预测精度低的缺陷,利用组合动态传播率替换基本传染数R0,提出基于支持向量回归的非线性时变传播率模型并对我国COVID-19疫情进行分析和预测.首先,计算动态传播率的离散值;其次,使用多项式函数、指数函数、双曲函数和幂函数分别对动态传播率的离散值进行拟合并基...     

Modelling and analysis of the spread of AIDS epidemic with immigration of HIV infectives

We propose and analyze, a nonlinear mathematical model of the spread of HIV/AIDS in a population of varying size with immigration of infectives. It is assumed that susceptibles become infected via sexual contacts with infectives (also assumed to be infectious) and all infectives ultimately develop AIDS. The model is studied using stability theory of differential equations and computer simulation. Model dynamics is also discussed under two particular cases when there is no direct inflow of infectives. On analyzing these situations, it is found that the disease is always persistent if the direct immigration of infectives is allowed in the community. Further, in the absence of inflow of infectives, the endemicity of the disease is found to be higher if pre-AIDS individuals also interact sexually in comparison to the case when they do not take part in sexual interactions. Thus, if the direct immigration of infectives is restricted, the spread of infection can be slowed down. A numerical study of the model is also carried out to investigate the influence of certain key parameters on the spread of the disease.     

SIR类型新型冠状病毒肺炎多阶段最优控制模型

新型冠状病毒肺炎(COVID-19)疫情在全球范围传播,给人们的健康带来了严重的威胁。面对疫情发展预期数据,我们需要在有限医疗资源的情况下确定疫情传播参数,以指导主要防疫措施的实施力度。本文采用SIR类型的模型描述新冠肺炎疫情发展,并建立多阶段最优控制模型确定疫情传播参数。为了高效确定参数取值,我们建立多项式时间可计算的半定规划近似模型。基于世界卫生组织发布的数据,我们求解近似模型,得到描述给定时段内美国新冠肺炎疫情发展态势的疫情传播参数,并分析疫情防控策略。