In the paper, we study a kind of time-delayed novel coronavirus pneumonia dynamical model with vaccination. This model considers that people are vaccinated, and the human immune system has a series of processes, which...In the paper, we study a kind of time-delayed novel coronavirus pneumonia dynamical model with vaccination. This model considers that people are vaccinated, and the human immune system has a series of processes, which need a certain time. We first obtain the disease-free equilibrium and the basic reproduction number R<sub>0</sub>, and the system has a unique endemic equilibrium when R<sub>0</sub> > 1. Then we discuss the stability of the disease-free equilibrium and the endemic equilibrium with different delays τ. For τ = 0, using the Lyapunov function approach, we obtained the stability of disease-free equilibrium and the endemic equilibrium, respectively. For any delay τ ≠ 0, using the Routh-Hurwitz Criteria, we obtained that the disease-free equilibrium is locally asymptotically stable. We also find the critical value τ<sub>0</sub> at the endemic equilibrium, and obtain the condition that the system has a Hopf bifurcation at the endemic equilibrium. Finally, with the suitable choices of the parameters, some numerical simulations are presented in order to verify the effectiveness of the obtained theoretical results.展开更多
The outbreak and rapid spread of COVID-19 has become a public health emergency of international concern.A number of studies have used modeling techniques and developed dynamic models to estimate the epidemiological pa...The outbreak and rapid spread of COVID-19 has become a public health emergency of international concern.A number of studies have used modeling techniques and developed dynamic models to estimate the epidemiological parameters,explore and project the trends of the COVID-19,and assess the effects of intervention or control measures.We identified 63 studies and summarized the three aspects of these studies:epidemiological parameters estimation,trend prediction,and control measure evaluation.Despite the discrepancy between the predictions and the actuals,the dynamic model has made great contributions in the above three aspects.The most important role of dynamic models is exploring possibilities rather than making strong predictions about longer-term disease dynamics.展开更多
The current epidemic outbreak COVID-19 first took place in the Wuhan city of China and then spread worldwide.This deadly disease affected millions of people and compelled the governments and other concerned institutio...The current epidemic outbreak COVID-19 first took place in the Wuhan city of China and then spread worldwide.This deadly disease affected millions of people and compelled the governments and other concerned institutions to take serious actions.Around 0.28 million people have died from the COVID-19 outbreak as of May 11,2020,05:41 GMT,and the number is still increasing exponentially.The results of any scientific investigation of this phenomenon are still to come.However,now it is urgently needed to evaluate and compare the disease dynamics to improve the quarantine activities and the level of individual protection,to at least speed up the rate of isolation of infected persons.In the domain of big data science and other related areas,it is always of interest to provide the best description of the data under consideration.Therefore,in this article,we compare the COVID-19 pandemic dynamics between two neighboring Asian countries,Iran and Pakistan,to provide a framework to arrange the appropriate quarantine activities.Simple tools for comparing this deadly pandemic dynamic have been presented that can be adopted to produce the bases for inferences.Most importantly,a new statistical model is developed to provide the best description of COVID-19 daily deaths data in Iran and Pakistan.展开更多
Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urg...Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.展开更多
The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest touris...The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.展开更多
The rational and effective implementation of epidemic prevention and control measures is crucial to controlling the spread of COVID-19, and vaccination is a key part to be considered in the development of epidemic pre...The rational and effective implementation of epidemic prevention and control measures is crucial to controlling the spread of COVID-19, and vaccination is a key part to be considered in the development of epidemic prevention and control strategies. In order to give full play to the greater role of vaccination strategies in epidemic prevention and control, more accurate and efficient vaccination strategies should be explored. Based on the classical SEIR dynamic model, this paper established a COVID-19 dynamic model of population age structure in the form of population grouping and combined with the transmission characteristics of the COVID-19 epidemic. An optimization model with the goal of minimizing daily infection was established to analyze the optimization studies on infection-related specificity of vaccination for different age groups under the condition of limited daily vaccine supply at the early stage of the epidemic, and to obtain the priority of vaccination strategies for Chinese age groups. And the effect of the heterogeneity of infection rate and hospitalization rate on the priority of vaccine allocation.展开更多
The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 t...The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 transmission have been reported. Among them, Bayesian probabilistic models of COVID-19 transmission dynamics have been very efficient in the interpretation of early data from the beginning of the pandemic, helping to estimate the impact of non-pharmacological measures in each country, and forecasting the evolution of the pandemic in different potential scenarios. These models use probability distribution curves to describe key dynamic aspects of the transmission, like the probability for every infected person of infecting other individuals, dying or recovering, with parameters obtained from experimental epidemiological data. However, the impact of vaccine-induced immunity, which has been key for controlling the public health emergency caused by the pandemic, has been more challenging to describe in these models, due to the complexity of experimental data. Here we report different probability distribution curves to model the acquisition and decay of immunity after vaccination. We discuss the mathematical background and how these models can be integrated in existing Bayesian probabilistic models to provide a good estimation of the dynamics of COVID-19 transmission during the entire pandemic period.展开更多
The recent pandemic of coronavirus disease 2019(COVID-19)caused by SARS-CoV-2 has raised global health concerns.The viral 3-chymotrypsin-like cysteine protease(3CL^pro)enzyme controls coronavirus replication and is es...The recent pandemic of coronavirus disease 2019(COVID-19)caused by SARS-CoV-2 has raised global health concerns.The viral 3-chymotrypsin-like cysteine protease(3CL^pro)enzyme controls coronavirus replication and is essential for its life cycle.3CL^pro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus(SARS-CoV)and Middle East respiratory syndrome coronavirus(MERS-CoV).Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV.Therefore,herein,we analysed the 3CL^pro sequence,constructed its 3D homology model,and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds.Our analyses revealed that the top nine hits might serve as potential anti-SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19.展开更多
The coronavirus disease 2019(COVID-19)pandemic is a global crisis,and medical systems in many countries are overwhelmed with supply shortages and increasing demands to treat patients due to the surge in cases and seve...The coronavirus disease 2019(COVID-19)pandemic is a global crisis,and medical systems in many countries are overwhelmed with supply shortages and increasing demands to treat patients due to the surge in cases and severe illnesses.This study aimed to assess COVID-19-related essential clinical resource demands in China,based on different scenarios involving COVID-19 spreads and interventions.We used a susceptible–exposed–infectious–hospitalized/isolated–removed(SEIHR)transmission dynamics model to estimate the number of COVID-19 infections and hospitalizations with corresponding essential healthcare resources needed.We found that,under strict non-pharmaceutical interventions(NPIs)or mass vaccination of the population,China would be able to contain community transmission and local outbreaks rapidly.However,under scenarios involving a low intensity of implemented NPIs and a small proportion of the population vaccinated,the use of a peacetime–wartime transition model would be needed for medical source stockpiles and preparations to ensure a normal functioning healthcare system.The implementation of COVID-19 vaccines and NPIs in different periods can influence the transmission of COVID-19 and subsequently affect the demand for clinical diagnosis and treatment.An increased proportion of asymptomatic infections in simulations will not reduce the demand for medical resources;however,attention must be paid to the increasing difficulty in containing COVID-19 transmission due to asymptomatic cases.This study provides evidence for emergency preparations and the adjustment of prevention and control strategies during the COVID-19 pandemic.It also provides guidance for essential healthcare investment and resource allocation.展开更多
This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,t...This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.展开更多
The COVID-19 pandemic,caused by SARS-CoV-2,highlighted heterogeneities in human behavior and attitudes of individuals with respect to adherence or lack thereof to public health-mandated intervention and mitigation mea...The COVID-19 pandemic,caused by SARS-CoV-2,highlighted heterogeneities in human behavior and attitudes of individuals with respect to adherence or lack thereof to public health-mandated intervention and mitigation measures.This study is based on using mathematical modeling approaches,backed by data analytics and computation,to theoretically assess the impact of human behavioral changes on the trajectory,burden,and control of the COVID-19 pandemic during the first two waves in New York City.A novel behavior-epidemiology model,which considers n heterogeneous behavioral groups based on level of risk tolerance and distinguishes behavioral changes by social and disease-related motivations(such as peer-influence and fear of disease-related hospitalizations),is developed.In addition to rigorously analyzing the basic qualitative features of this model,a special case is considered where the total population is stratified into two groups:risk-averse(Group 1)and risk-tolerant(Group 2).The 2-group model was calibrated and validated using daily hospitalization data for New York City during the first wave,and the calibrated model was used to predict the data for the second wave.The 2-group model predicts the daily hospitalizations during the second wave almost perfectly,compared to the version without behavioral considerations,which fails to accurately predict the second wave.This suggests that epidemic models of the COVID-19 pandemic that do not explicitly account for heterogeneities in human behavior may fail to accurately predict the trajectory and burden of the pandemic in a population.Numerical simulations of the calibrated 2-group behavior model showed that while the dynamics of the COVID-19 pandemic during the first wave was largely influenced by the behavior of the risk-tolerant(Group 2)individuals,the dynamics during the second wave was influenced by the behavior of individuals in both groups.It was also shown that disease-motivated behavioral changes(i.e.,behavior changes due to the level of COVID-19 hospitalizations in the community)had greater influence in significantly reducing COVID-19 morbidity and mortality than behavior changes due to the level of peer or social influence or pressure.Finally,it is shown that the initial proportion of members in the community that are risk-averse(i.e.,the proportion of individuals in Group 1 at the beginning of the pandemic)and the early and effective implementation of non-pharmaceutical interventions have major impacts in reducing the size and burden of the pandemic(particularly the total COVID-19 mortality in New York City during the second wave).展开更多
In this paper,we present a mathematical model that incorporates seasonal variations in COVID-19 transmission within South Africa.By fitting the model to real-world data,we estimate its parameters and demonstrate its e...In this paper,we present a mathematical model that incorporates seasonal variations in COVID-19 transmission within South Africa.By fitting the model to real-world data,we estimate its parameters and demonstrate its enhanced accuracy in describing the local infection dynamics.We analyze the basic reproduction number and establish threshold dynamics through theoretical analysis,alongside investigating its numerical relationship with specific parameters.Furthermore,we conduct an optimal control analysis to evaluate the impact of intervention strategies,including quarantine,vaccination and medical treatment,on COVID-19 spread.Our findings emphasize the effectiveness of combining all three interventions in reducing the number of exposed and infected individuals.We identify that implementing these interventions when the infected population is at its lowest yields optimal results.展开更多
The epidemiological characteristics and distributions of two epidemics in Fujian Province of Southeast China were attributed to the complex interactions among variant,host,and non-pharmaceutical interventions(NPIs).Al...The epidemiological characteristics and distributions of two epidemics in Fujian Province of Southeast China were attributed to the complex interactions among variant,host,and non-pharmaceutical interventions(NPIs).All reported cases in the Putian epidemic(September 8–October 2,2021,Delta variant B.1.617.2)and Fuzhou epidemic(October 22–November 18,2022,Omicron variant BA.5.2)were classified by sex,age group,occupation,and location in this study.Using surveillance data from the Fujian Provincial Center for Disease Control and Prevention,we established a virus-oriented SVEIR(Susceptible–Vaccinated–Exposed–Infected–Recovered)model to investigate the dynamic evolution features of these two variants and the effects of NPIs.The optimal simulations were carried out with variants and scenario investigations.The scenario investigations showed that NPIs significantly reduced the transmission risk and infection scales of COVID-19,and that the Omicron variant was more infectious than the Delta variant.Moreover,the dynamic investigations revealed the increasing tendencies from Delta to Omicron,such as the basic reproduction number,infection rate,percentage of high-risk cases,and the growth rate.Decreasing tendencies were also identified,such as the average recovery period,the awareness delay,and the percentage of symptomatic cases.This study highlighted that NPIs played critical roles in successfully containing the two epidemics.Such interventions are strongly recommended to public health policymakers.展开更多
Using the classical Susceptible-Infected-Recovered epidemiological model,an analytical formula is derived for the number of beds occupied by Covid-19 patients.The analytical curve is fitted to data in Belgium,France,N...Using the classical Susceptible-Infected-Recovered epidemiological model,an analytical formula is derived for the number of beds occupied by Covid-19 patients.The analytical curve is fitted to data in Belgium,France,New York City and Switzerland,with a correlation coefficient exceeding 98.8%,suggesting that finer models are unnecessary with such macroscopic data.The fitting is used to extract estimates of the doubling time in the ascending phase of the epidemic,the mean recovery time and,for those who require medical intervention,the mean hospitalization time.Large variations can be observed among different outbreaks.展开更多
Backgrounds Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19.Methods:In this study,we designed a dynamic graph model,not for precisely predicting the number of infec...Backgrounds Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19.Methods:In this study,we designed a dynamic graph model,not for precisely predicting the number of infected cases,but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors・Results^We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city.We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system,and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality.Conclusions Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions・We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS・CoV・2 from spreading globally.展开更多
In this study, we developed a SEIR model, including social interactions and individualhuman mobility in everyday activities. For this purpose, daily mobility of people wasconsidered by using the molecular dynamic meth...In this study, we developed a SEIR model, including social interactions and individualhuman mobility in everyday activities. For this purpose, daily mobility of people wasconsidered by using the molecular dynamic method and the virus spreading was modeledemploying the ordinary SEIR scheme. Utilizing this model, the variation of populationsize, density, and health strategy as well as the effect of busy places such as malls,were considered. The results show that our flexible model is able to consider the effectsof different parameters such as distance between peoples, local population density andhealth strategy in the outbreak.展开更多
The dynamics of COVID-19 contagion investigated in S˜ao Lu′ıs-MA,Brazil,is based on the SIR model.S˜ao Lu′ıs was the first city in Brazil to implement the lockdown to control the disease.The SIR model’s definition ...The dynamics of COVID-19 contagion investigated in S˜ao Lu′ıs-MA,Brazil,is based on the SIR model.S˜ao Lu′ıs was the first city in Brazil to implement the lockdown to control the disease.The SIR model’s definition and properties are presented in addition to the ways of estimating its parameters.The estimation of their values is due to a database released on the Maranh˜ao State Health Department website during the period March 20 to July 2,2020.The analysis of the proliferation of the disease shows the evolution of the COVID-19 in the city.The graphs illustrate that result.展开更多
This study investigates the static and dynamic return and volatility spillovers between non-fungible tokens(NFTs)and conventional currencies using the time-varying parameter vector autoregressions approach.We reveal t...This study investigates the static and dynamic return and volatility spillovers between non-fungible tokens(NFTs)and conventional currencies using the time-varying parameter vector autoregressions approach.We reveal that the total connectedness between these markets is weak,implying that investors may increase the diversification benefits of their multicurrency portfolios by adding NFTs.We also find that NFTs are net transmitters of both return and volatility spillovers;however,in the case of return spillovers,the influence of NFTs on conventional currencies is more pronounced than that of volatility shock transmissions.The dynamic exercise reveals that the returns and volatility spillovers vary over time,largely increasing during the onset of the Covid-19 crisis,which deeply affected the relationship between NFTs and the conventional currencies markets.Our findings are useful for currency traders and NFT investors seeking to build effective cross-currency and cross-asset hedge strategies during systemic crises.展开更多
Background:The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019(COVID-19)pandemic complicated to predict and posed a severe challenge to the Beijing 2022Wint...Background:The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019(COVID-19)pandemic complicated to predict and posed a severe challenge to the Beijing 2022Winter Olympics and Winter Paralympics held in February and March 2022.Methods:During the preparations for the Beijing 2022 Winter Olympics,we established a dynamic model with pulsedetection and isolation efect to evaluate the efect of epidemic prevention and control measures such as entry policies,contact reduction,nucleic acid testing,tracking,isolation,and health monitoring in a closed-loop managementenvironment,by simulating the transmission dynamics in assumed scenarios.We also compared the importance ofeach parameter in the combination of intervention measures through sensitivity analysis.Results:At the assumed baseline levels,the peak of the epidemic reached on the 57th day.During the simulationperiod(100 days),13,382 people infected COVID-19.The mean and peak values of hospitalized cases were 2650and 6746,respectively.The simulation and sensitivity analysis showed that:(1)the most important measures to stopCOVID-19 transmission during the event were daily nucleic acid testing,reducing contact among people,and dailyhealth monitoring,with cumulative infections at 0.04%,0.14%,and 14.92%of baseline levels,respectively(2)strictlyimplementing the entry policy and reducing the number of cases entering the closed-loop system could delay thepeak of the epidemic by 9 days and provide time for medical resources to be mobilized;(3)the risk of environmentaltransmission was low.Conclusions:Comprehensive measures under certain scenarios such as reducing contact,nucleic acid testing,health monitoring,and timely tracking and isolation could efectively prevent virus transmission.Our research resultsprovided an important reference for formulating prevention and control measures during the Winter Olympics,andno epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results.展开更多
It’s urgently needed to assess the COVID-19 epidemic under the“dynamic zero-COVID policy”in China,which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control.Here,we deve...It’s urgently needed to assess the COVID-19 epidemic under the“dynamic zero-COVID policy”in China,which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control.Here,we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-remov ed(SEAIQR)model with stage-specific interventions based on recent Shanghai epidemic data,considering a large number of asymptomatic infectious,the changing parameters,and control procedures.The data collected from March 1st,2022 to April 15th,2022 were used to fit the model,and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting.We then calculated the effective regeneration number(Rt)and analyzed the sensitivity of different measures scenarios.Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai,and Pudong is the district with the most positive cases.The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th,2022,with 1963 and 28,502 cases,respectively,and zero community transmission may be achieved in early to mid-May.The prediction errors for newly confirmed cases were considered to be reasonable,and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th.The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477~47,749 and 402,254~730,176,respectively.At the beginning of the outbreak,Rt was 6.69.Since the implementation of comprehensive control,Rt showed a gradual downward trend,dropping to below 1.0 on April 15th,2022.With the early implementation of control measures and the improvement of quarantine rate,recovery rate,and immunity threshold,the peak number of infections will continue to decrease,whereas the earlier the control is implemented,the earlier the turning point of the epidemic will arrive.The proposed time-dependent SEAIQR dynamic model fits and forecasts the epidemic well,which can provide a reference for decision making of the“dynamic zero-COVID policy”.展开更多
文摘In the paper, we study a kind of time-delayed novel coronavirus pneumonia dynamical model with vaccination. This model considers that people are vaccinated, and the human immune system has a series of processes, which need a certain time. We first obtain the disease-free equilibrium and the basic reproduction number R<sub>0</sub>, and the system has a unique endemic equilibrium when R<sub>0</sub> > 1. Then we discuss the stability of the disease-free equilibrium and the endemic equilibrium with different delays τ. For τ = 0, using the Lyapunov function approach, we obtained the stability of disease-free equilibrium and the endemic equilibrium, respectively. For any delay τ ≠ 0, using the Routh-Hurwitz Criteria, we obtained that the disease-free equilibrium is locally asymptotically stable. We also find the critical value τ<sub>0</sub> at the endemic equilibrium, and obtain the condition that the system has a Hopf bifurcation at the endemic equilibrium. Finally, with the suitable choices of the parameters, some numerical simulations are presented in order to verify the effectiveness of the obtained theoretical results.
文摘The outbreak and rapid spread of COVID-19 has become a public health emergency of international concern.A number of studies have used modeling techniques and developed dynamic models to estimate the epidemiological parameters,explore and project the trends of the COVID-19,and assess the effects of intervention or control measures.We identified 63 studies and summarized the three aspects of these studies:epidemiological parameters estimation,trend prediction,and control measure evaluation.Despite the discrepancy between the predictions and the actuals,the dynamic model has made great contributions in the above three aspects.The most important role of dynamic models is exploring possibilities rather than making strong predictions about longer-term disease dynamics.
基金supported by the Department of Statistics,Yazd University,Iran.
文摘The current epidemic outbreak COVID-19 first took place in the Wuhan city of China and then spread worldwide.This deadly disease affected millions of people and compelled the governments and other concerned institutions to take serious actions.Around 0.28 million people have died from the COVID-19 outbreak as of May 11,2020,05:41 GMT,and the number is still increasing exponentially.The results of any scientific investigation of this phenomenon are still to come.However,now it is urgently needed to evaluate and compare the disease dynamics to improve the quarantine activities and the level of individual protection,to at least speed up the rate of isolation of infected persons.In the domain of big data science and other related areas,it is always of interest to provide the best description of the data under consideration.Therefore,in this article,we compare the COVID-19 pandemic dynamics between two neighboring Asian countries,Iran and Pakistan,to provide a framework to arrange the appropriate quarantine activities.Simple tools for comparing this deadly pandemic dynamic have been presented that can be adopted to produce the bases for inferences.Most importantly,a new statistical model is developed to provide the best description of COVID-19 daily deaths data in Iran and Pakistan.
文摘Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.
文摘The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.
文摘The rational and effective implementation of epidemic prevention and control measures is crucial to controlling the spread of COVID-19, and vaccination is a key part to be considered in the development of epidemic prevention and control strategies. In order to give full play to the greater role of vaccination strategies in epidemic prevention and control, more accurate and efficient vaccination strategies should be explored. Based on the classical SEIR dynamic model, this paper established a COVID-19 dynamic model of population age structure in the form of population grouping and combined with the transmission characteristics of the COVID-19 epidemic. An optimization model with the goal of minimizing daily infection was established to analyze the optimization studies on infection-related specificity of vaccination for different age groups under the condition of limited daily vaccine supply at the early stage of the epidemic, and to obtain the priority of vaccination strategies for Chinese age groups. And the effect of the heterogeneity of infection rate and hospitalization rate on the priority of vaccine allocation.
文摘The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 transmission have been reported. Among them, Bayesian probabilistic models of COVID-19 transmission dynamics have been very efficient in the interpretation of early data from the beginning of the pandemic, helping to estimate the impact of non-pharmacological measures in each country, and forecasting the evolution of the pandemic in different potential scenarios. These models use probability distribution curves to describe key dynamic aspects of the transmission, like the probability for every infected person of infecting other individuals, dying or recovering, with parameters obtained from experimental epidemiological data. However, the impact of vaccine-induced immunity, which has been key for controlling the public health emergency caused by the pandemic, has been more challenging to describe in these models, due to the complexity of experimental data. Here we report different probability distribution curves to model the acquisition and decay of immunity after vaccination. We discuss the mathematical background and how these models can be integrated in existing Bayesian probabilistic models to provide a good estimation of the dynamics of COVID-19 transmission during the entire pandemic period.
基金This work was supported by the National Key Research and Development Program of China(2020YFC0845600)the Hubei Provincial Natural Science Foundation of China(2019CFA014)+1 种基金the Starting Research Grant for High-level Talents from Guangxi University,Nanning,ChinaPostdoctoral Research Platform Grant of Guangxi University,Nanning,China.
文摘The recent pandemic of coronavirus disease 2019(COVID-19)caused by SARS-CoV-2 has raised global health concerns.The viral 3-chymotrypsin-like cysteine protease(3CL^pro)enzyme controls coronavirus replication and is essential for its life cycle.3CL^pro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus(SARS-CoV)and Middle East respiratory syndrome coronavirus(MERS-CoV).Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV.Therefore,herein,we analysed the 3CL^pro sequence,constructed its 3D homology model,and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds.Our analyses revealed that the top nine hits might serve as potential anti-SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19.
基金supported by the following fundings:Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(2020-I2M-1-001,2020-I2M-2-015,and 2016-I2M-1-014)National Social Science Fund of China(20&ZD201).
文摘The coronavirus disease 2019(COVID-19)pandemic is a global crisis,and medical systems in many countries are overwhelmed with supply shortages and increasing demands to treat patients due to the surge in cases and severe illnesses.This study aimed to assess COVID-19-related essential clinical resource demands in China,based on different scenarios involving COVID-19 spreads and interventions.We used a susceptible–exposed–infectious–hospitalized/isolated–removed(SEIHR)transmission dynamics model to estimate the number of COVID-19 infections and hospitalizations with corresponding essential healthcare resources needed.We found that,under strict non-pharmaceutical interventions(NPIs)or mass vaccination of the population,China would be able to contain community transmission and local outbreaks rapidly.However,under scenarios involving a low intensity of implemented NPIs and a small proportion of the population vaccinated,the use of a peacetime–wartime transition model would be needed for medical source stockpiles and preparations to ensure a normal functioning healthcare system.The implementation of COVID-19 vaccines and NPIs in different periods can influence the transmission of COVID-19 and subsequently affect the demand for clinical diagnosis and treatment.An increased proportion of asymptomatic infections in simulations will not reduce the demand for medical resources;however,attention must be paid to the increasing difficulty in containing COVID-19 transmission due to asymptomatic cases.This study provides evidence for emergency preparations and the adjustment of prevention and control strategies during the COVID-19 pandemic.It also provides guidance for essential healthcare investment and resource allocation.
文摘This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.
基金ABG acknowledges the support,in part,of the National Science Foundation(Grant Number:DMS-2052363,transferred to DMS-2330801)AO acknowledges the support of the University of Maryland School of Graduate Studies Dean's Fellowship.
文摘The COVID-19 pandemic,caused by SARS-CoV-2,highlighted heterogeneities in human behavior and attitudes of individuals with respect to adherence or lack thereof to public health-mandated intervention and mitigation measures.This study is based on using mathematical modeling approaches,backed by data analytics and computation,to theoretically assess the impact of human behavioral changes on the trajectory,burden,and control of the COVID-19 pandemic during the first two waves in New York City.A novel behavior-epidemiology model,which considers n heterogeneous behavioral groups based on level of risk tolerance and distinguishes behavioral changes by social and disease-related motivations(such as peer-influence and fear of disease-related hospitalizations),is developed.In addition to rigorously analyzing the basic qualitative features of this model,a special case is considered where the total population is stratified into two groups:risk-averse(Group 1)and risk-tolerant(Group 2).The 2-group model was calibrated and validated using daily hospitalization data for New York City during the first wave,and the calibrated model was used to predict the data for the second wave.The 2-group model predicts the daily hospitalizations during the second wave almost perfectly,compared to the version without behavioral considerations,which fails to accurately predict the second wave.This suggests that epidemic models of the COVID-19 pandemic that do not explicitly account for heterogeneities in human behavior may fail to accurately predict the trajectory and burden of the pandemic in a population.Numerical simulations of the calibrated 2-group behavior model showed that while the dynamics of the COVID-19 pandemic during the first wave was largely influenced by the behavior of the risk-tolerant(Group 2)individuals,the dynamics during the second wave was influenced by the behavior of individuals in both groups.It was also shown that disease-motivated behavioral changes(i.e.,behavior changes due to the level of COVID-19 hospitalizations in the community)had greater influence in significantly reducing COVID-19 morbidity and mortality than behavior changes due to the level of peer or social influence or pressure.Finally,it is shown that the initial proportion of members in the community that are risk-averse(i.e.,the proportion of individuals in Group 1 at the beginning of the pandemic)and the early and effective implementation of non-pharmaceutical interventions have major impacts in reducing the size and burden of the pandemic(particularly the total COVID-19 mortality in New York City during the second wave).
基金supported by Natural Science Found of Jiangsu province(BK20200805)Natural Science Fund Project of Colleges in Jiangsu Province(20KJB110009)+2 种基金National Natural Science Foundation of China(12101318,12101393)National Social Science Foundation of China(22BTJ018)the National Statistical Science Research Project(2021LZ41).
文摘In this paper,we present a mathematical model that incorporates seasonal variations in COVID-19 transmission within South Africa.By fitting the model to real-world data,we estimate its parameters and demonstrate its enhanced accuracy in describing the local infection dynamics.We analyze the basic reproduction number and establish threshold dynamics through theoretical analysis,alongside investigating its numerical relationship with specific parameters.Furthermore,we conduct an optimal control analysis to evaluate the impact of intervention strategies,including quarantine,vaccination and medical treatment,on COVID-19 spread.Our findings emphasize the effectiveness of combining all three interventions in reducing the number of exposed and infected individuals.We identify that implementing these interventions when the infected population is at its lowest yields optimal results.
基金supported by Consultancy Project by the Chinese Academy of Engineering(2022-JB-06)supported by Special Projects of the Central Government Guiding Local Science and Technology Development(2021L3018)+2 种基金supported by the Natural Science Foundation of Fujian ProvinceProvince of China(2021J01621)supported by Fujian Science and Technology Innovation Platform Construction Project(2019Y2001)Health Science and Technology Project of Fujian Province(2020GGB019).
文摘The epidemiological characteristics and distributions of two epidemics in Fujian Province of Southeast China were attributed to the complex interactions among variant,host,and non-pharmaceutical interventions(NPIs).All reported cases in the Putian epidemic(September 8–October 2,2021,Delta variant B.1.617.2)and Fuzhou epidemic(October 22–November 18,2022,Omicron variant BA.5.2)were classified by sex,age group,occupation,and location in this study.Using surveillance data from the Fujian Provincial Center for Disease Control and Prevention,we established a virus-oriented SVEIR(Susceptible–Vaccinated–Exposed–Infected–Recovered)model to investigate the dynamic evolution features of these two variants and the effects of NPIs.The optimal simulations were carried out with variants and scenario investigations.The scenario investigations showed that NPIs significantly reduced the transmission risk and infection scales of COVID-19,and that the Omicron variant was more infectious than the Delta variant.Moreover,the dynamic investigations revealed the increasing tendencies from Delta to Omicron,such as the basic reproduction number,infection rate,percentage of high-risk cases,and the growth rate.Decreasing tendencies were also identified,such as the average recovery period,the awareness delay,and the percentage of symptomatic cases.This study highlighted that NPIs played critical roles in successfully containing the two epidemics.Such interventions are strongly recommended to public health policymakers.
文摘Using the classical Susceptible-Infected-Recovered epidemiological model,an analytical formula is derived for the number of beds occupied by Covid-19 patients.The analytical curve is fitted to data in Belgium,France,New York City and Switzerland,with a correlation coefficient exceeding 98.8%,suggesting that finer models are unnecessary with such macroscopic data.The fitting is used to extract estimates of the doubling time in the ascending phase of the epidemic,the mean recovery time and,for those who require medical intervention,the mean hospitalization time.Large variations can be observed among different outbreaks.
基金This study was supported by the National Key R&D Program of China(Nos.2018YFC0910400,2017YFC0907500,and 2018ZX10302205)the National Natural Science Foundation of China(Nos.61702406,3161372,31701739 and 8191101420)+1 种基金the"World-Class Universities and the Characteristic Development Guidance Funds for the Central Universities",Xi'an Jiaotong University Basic Research and Profession Grant(No.xtr022019003)Shanghai Municipal Science and Technology Major Project(No.2017SHZDZX01).
文摘Backgrounds Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19.Methods:In this study,we designed a dynamic graph model,not for precisely predicting the number of infected cases,but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors・Results^We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city.We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system,and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality.Conclusions Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions・We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS・CoV・2 from spreading globally.
文摘In this study, we developed a SEIR model, including social interactions and individualhuman mobility in everyday activities. For this purpose, daily mobility of people wasconsidered by using the molecular dynamic method and the virus spreading was modeledemploying the ordinary SEIR scheme. Utilizing this model, the variation of populationsize, density, and health strategy as well as the effect of busy places such as malls,were considered. The results show that our flexible model is able to consider the effectsof different parameters such as distance between peoples, local population density andhealth strategy in the outbreak.
文摘The dynamics of COVID-19 contagion investigated in S˜ao Lu′ıs-MA,Brazil,is based on the SIR model.S˜ao Lu′ıs was the first city in Brazil to implement the lockdown to control the disease.The SIR model’s definition and properties are presented in addition to the ways of estimating its parameters.The estimation of their values is due to a database released on the Maranh˜ao State Health Department website during the period March 20 to July 2,2020.The analysis of the proliferation of the disease shows the evolution of the COVID-19 in the city.The graphs illustrate that result.
基金supported by FCT,I.P,the Portuguese national funding agency for science,research and technology under the Project UIDB/04521/2020.
文摘This study investigates the static and dynamic return and volatility spillovers between non-fungible tokens(NFTs)and conventional currencies using the time-varying parameter vector autoregressions approach.We reveal that the total connectedness between these markets is weak,implying that investors may increase the diversification benefits of their multicurrency portfolios by adding NFTs.We also find that NFTs are net transmitters of both return and volatility spillovers;however,in the case of return spillovers,the influence of NFTs on conventional currencies is more pronounced than that of volatility shock transmissions.The dynamic exercise reveals that the returns and volatility spillovers vary over time,largely increasing during the onset of the Covid-19 crisis,which deeply affected the relationship between NFTs and the conventional currencies markets.Our findings are useful for currency traders and NFT investors seeking to build effective cross-currency and cross-asset hedge strategies during systemic crises.
文摘Background:The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019(COVID-19)pandemic complicated to predict and posed a severe challenge to the Beijing 2022Winter Olympics and Winter Paralympics held in February and March 2022.Methods:During the preparations for the Beijing 2022 Winter Olympics,we established a dynamic model with pulsedetection and isolation efect to evaluate the efect of epidemic prevention and control measures such as entry policies,contact reduction,nucleic acid testing,tracking,isolation,and health monitoring in a closed-loop managementenvironment,by simulating the transmission dynamics in assumed scenarios.We also compared the importance ofeach parameter in the combination of intervention measures through sensitivity analysis.Results:At the assumed baseline levels,the peak of the epidemic reached on the 57th day.During the simulationperiod(100 days),13,382 people infected COVID-19.The mean and peak values of hospitalized cases were 2650and 6746,respectively.The simulation and sensitivity analysis showed that:(1)the most important measures to stopCOVID-19 transmission during the event were daily nucleic acid testing,reducing contact among people,and dailyhealth monitoring,with cumulative infections at 0.04%,0.14%,and 14.92%of baseline levels,respectively(2)strictlyimplementing the entry policy and reducing the number of cases entering the closed-loop system could delay thepeak of the epidemic by 9 days and provide time for medical resources to be mobilized;(3)the risk of environmentaltransmission was low.Conclusions:Comprehensive measures under certain scenarios such as reducing contact,nucleic acid testing,health monitoring,and timely tracking and isolation could efectively prevent virus transmission.Our research resultsprovided an important reference for formulating prevention and control measures during the Winter Olympics,andno epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results.
基金This study was supported by the National Key Research and Development Program of China(2021YFC2301603).
文摘It’s urgently needed to assess the COVID-19 epidemic under the“dynamic zero-COVID policy”in China,which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control.Here,we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-remov ed(SEAIQR)model with stage-specific interventions based on recent Shanghai epidemic data,considering a large number of asymptomatic infectious,the changing parameters,and control procedures.The data collected from March 1st,2022 to April 15th,2022 were used to fit the model,and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting.We then calculated the effective regeneration number(Rt)and analyzed the sensitivity of different measures scenarios.Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai,and Pudong is the district with the most positive cases.The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th,2022,with 1963 and 28,502 cases,respectively,and zero community transmission may be achieved in early to mid-May.The prediction errors for newly confirmed cases were considered to be reasonable,and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th.The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477~47,749 and 402,254~730,176,respectively.At the beginning of the outbreak,Rt was 6.69.Since the implementation of comprehensive control,Rt showed a gradual downward trend,dropping to below 1.0 on April 15th,2022.With the early implementation of control measures and the improvement of quarantine rate,recovery rate,and immunity threshold,the peak number of infections will continue to decrease,whereas the earlier the control is implemented,the earlier the turning point of the epidemic will arrive.The proposed time-dependent SEAIQR dynamic model fits and forecasts the epidemic well,which can provide a reference for decision making of the“dynamic zero-COVID policy”.
