Influenza viruses continue to cause epidemics worldwide every year.However,due to the lack of an effective assessment for the severity of influenza epidemics,it was extremely difficult to take preventative measures.Da...Influenza viruses continue to cause epidemics worldwide every year.However,due to the lack of an effective assessment for the severity of influenza epidemics,it was extremely difficult to take preventative measures.Data were extracted from infectious diseases reports from 2011–2018.Joinpoint regression model and susceptible-exposed-infectious-recovered model were built to understand the characteristics and processes of the epidemic.The reported incidence of influenza was 1,913,698 from January 2011 to February 2018,with an average-yearly-reported-incidence-rate of 19.21 per 100,000.However,there had been a substantial nationwide epidemic of influenza after September 2017,when the average yearly reported incidence rate was 87.29 per 100,000 and an annual percentage change of 48.1%.The hemagglutinin genes of most influenza A(H1N1 and H3N2)viruses from the period of the epidemic had lower homology to those before August 2017.All the hemagglutinin of the recommended A(H3N2,H1N1)and B(Victoria)viruses for vaccines 2017/2018 had low matches with the epidemic viruses.The basic reproduction number was 1.53.The vaccination benefit was linearly related to vaccination coverage,while the quarantine measure had only significantly benefited when over 60%of the quarantined population.The most severe epidemic of influenza in China since 2011 occurred during the period from September 2017 to February 2018.Compared to quarantine,influenza vaccination is more effective way to prevent influenza,and strategies to increase vaccination coverage should be taken for the prevention of severe epidemics of influenza.展开更多
We develop a discrete time compartmental model to describe the spread of seasonal influenza virus.As time and disease state variables are assumed to be discrete,this model is considered to be a discrete time,stochasti...We develop a discrete time compartmental model to describe the spread of seasonal influenza virus.As time and disease state variables are assumed to be discrete,this model is considered to be a discrete time,stochastic,Susceptible-Infectious-RecoveredSusceptible(DT-SIRS)model,where weekly counts of disease are assumed to follow a Poisson distribution.We allow the disease transmission rate to also vary over time,and the disease can only be reintroduced after extinction if there is a contact with infected individuals from other host populations.To capture the variability of influenza activities from one season to the next,we define the seasonality with a 4-week period effect that may change over years.We examine three different transmission rates and compare their performance to that of existing approaches.Even though there is limited information for susceptible and recovered individuals,we demonstrate that the simple models for transmission rates effectively capture the behaviour of the disease dynamics.We use a Bayesian approach for inference.The framework is applied in an analysis of the temporal spread of influenza in the province of Manitoba,Canada,2012e2015.展开更多
Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health ...Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses.However,it is important to correctly classify reported data and understand how this impacts estimation of model parameters.The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions,as well as how we think about classical infectious disease modelling paradigms.Objective:We aim to assess the appropriateness of model parameter estimates and preiction results in classical infectious disease compartmental modelling frameworks given available data types(infected,active,quarantined,and recovered cases)for situations where just one data type is available to fit the model.Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.Methods:We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed(SIR)modelling framework.We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy:a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County,Montana,USA.Results:We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data.We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification.Using a classical example of influenza epidemics in an England boarding school,we argue that the Susceptible-Infected-Quarantined-Recovered(SIQR)model is more appropriate than the commonly employed SIR model given the data collected(number of active cases).Similarly,we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.Conclusions:We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data.For both a classical influenza data set and a COVID-19 case data set,we demonstrate the implications of using the“right”data in the“wrong”model.The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections,as well as minimal vaccination requirements.展开更多
基金supported by grants from the National Natural Science Foundation of China(81672005,81001271)the Key Joint Project for Data Center of the National Natural Science Foundation of China(U1611264)+1 种基金the Mega-Project of National Science and Technology of China(2018ZX10715014002,2014ZX10004008,2013ZX10004901 and 2013ZX10004904)the Fundamental Research Funds for the Central Universities。
文摘Influenza viruses continue to cause epidemics worldwide every year.However,due to the lack of an effective assessment for the severity of influenza epidemics,it was extremely difficult to take preventative measures.Data were extracted from infectious diseases reports from 2011–2018.Joinpoint regression model and susceptible-exposed-infectious-recovered model were built to understand the characteristics and processes of the epidemic.The reported incidence of influenza was 1,913,698 from January 2011 to February 2018,with an average-yearly-reported-incidence-rate of 19.21 per 100,000.However,there had been a substantial nationwide epidemic of influenza after September 2017,when the average yearly reported incidence rate was 87.29 per 100,000 and an annual percentage change of 48.1%.The hemagglutinin genes of most influenza A(H1N1 and H3N2)viruses from the period of the epidemic had lower homology to those before August 2017.All the hemagglutinin of the recommended A(H3N2,H1N1)and B(Victoria)viruses for vaccines 2017/2018 had low matches with the epidemic viruses.The basic reproduction number was 1.53.The vaccination benefit was linearly related to vaccination coverage,while the quarantine measure had only significantly benefited when over 60%of the quarantined population.The most severe epidemic of influenza in China since 2011 occurred during the period from September 2017 to February 2018.Compared to quarantine,influenza vaccination is more effective way to prevent influenza,and strategies to increase vaccination coverage should be taken for the prevention of severe epidemics of influenza.
基金supported by the Natural Sciences and Engineering Research Council of Canada(NSERC)the Canadian Statistical Sciences Institute-Collaborative Research Teams(CANSSI-CRT)grants.
文摘We develop a discrete time compartmental model to describe the spread of seasonal influenza virus.As time and disease state variables are assumed to be discrete,this model is considered to be a discrete time,stochastic,Susceptible-Infectious-RecoveredSusceptible(DT-SIRS)model,where weekly counts of disease are assumed to follow a Poisson distribution.We allow the disease transmission rate to also vary over time,and the disease can only be reintroduced after extinction if there is a contact with infected individuals from other host populations.To capture the variability of influenza activities from one season to the next,we define the seasonality with a 4-week period effect that may change over years.We examine three different transmission rates and compare their performance to that of existing approaches.Even though there is limited information for susceptible and recovered individuals,we demonstrate that the simple models for transmission rates effectively capture the behaviour of the disease dynamics.We use a Bayesian approach for inference.The framework is applied in an analysis of the temporal spread of influenza in the province of Manitoba,Canada,2012e2015.
基金Supported by the National Natural Science Foundation of China(11201368)the Fundamental Research Funds for the Central Universities(Xi’an Jiaotong University)the Science and Technology Research Project of the Department of Education of Heilongjiang Province(12531495)
基金supported by National Institute of General Medical Sciences of the National Institutes of Health,United States(Award Numbers P20GM130418,U54GM104944).
文摘Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses.However,it is important to correctly classify reported data and understand how this impacts estimation of model parameters.The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions,as well as how we think about classical infectious disease modelling paradigms.Objective:We aim to assess the appropriateness of model parameter estimates and preiction results in classical infectious disease compartmental modelling frameworks given available data types(infected,active,quarantined,and recovered cases)for situations where just one data type is available to fit the model.Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.Methods:We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed(SIR)modelling framework.We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy:a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County,Montana,USA.Results:We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data.We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification.Using a classical example of influenza epidemics in an England boarding school,we argue that the Susceptible-Infected-Quarantined-Recovered(SIQR)model is more appropriate than the commonly employed SIR model given the data collected(number of active cases).Similarly,we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.Conclusions:We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data.For both a classical influenza data set and a COVID-19 case data set,we demonstrate the implications of using the“right”data in the“wrong”model.The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections,as well as minimal vaccination requirements.
