Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response.Differential equations are often used to model an epidemic outbreak's behaviour but are challen...Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response.Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise.Furthermore,these models can suffer from misspecification,which biases predictions and parameter estimates.Stochastic models can help with misspecification but are even more expensive to simulate and perform inference with.Here,we develop an explicitly likelihood-based variation of the generalised profiling method as a tool for prediction and inference under model mis-specification.Our approach allows us to carry out identifiability analysis and uncertainty quantification using profile likelihood-based methods without the need for marginalisation.We provide justification for this approach by introducing a new interpretation of the model approximation component as a stochastic constraint.This preserves the rationale for using profiling rather than integration to remove nuisance parameters while also providing a link back to stochastic models.We applied an initial version of this method during an outbreak of measles in Samoa in 2019e2020 and found that it achieved relatively fast,accurate predictions.Here we present the most recent version of our method and its application to this measles outbreak,along with additional validation.展开更多
The basic reproduction number,R0,is defined as the expected number of secondary cases of a disease produced by a single infection in a completely susceptible population,and can be estimated in several ways.For example...The basic reproduction number,R0,is defined as the expected number of secondary cases of a disease produced by a single infection in a completely susceptible population,and can be estimated in several ways.For example,from the stability analysis of a compartmental model;through the use of the matrix of next generation,or from the final size of an epidemic,etc.In this paper we applied the method for estimating R0 of dengue fever from the initial growth phase of an outbreak,without assuming exponential growth of cases,a common assumption in many studies.We used three different methods of calculating R0 to compare the techniques'details and to evaluate how these techniques estimate the value of R0 of dengue using data from the city of Ribeir^ao Preto(SE of Brazil)in two outbreaks.The results of the three methods are numerically different but,when we compare them using a system of differential equations developed for modeling only the first generation time,we can observe that the methods differ little in the initial growth phase.We conclude that the methods predict that dengue will spread in the city studied and the analysis of the data shows that the estimated values of R0 have an equal pattern overtime.展开更多
文摘Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response.Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise.Furthermore,these models can suffer from misspecification,which biases predictions and parameter estimates.Stochastic models can help with misspecification but are even more expensive to simulate and perform inference with.Here,we develop an explicitly likelihood-based variation of the generalised profiling method as a tool for prediction and inference under model mis-specification.Our approach allows us to carry out identifiability analysis and uncertainty quantification using profile likelihood-based methods without the need for marginalisation.We provide justification for this approach by introducing a new interpretation of the model approximation component as a stochastic constraint.This preserves the rationale for using profiling rather than integration to remove nuisance parameters while also providing a link back to stochastic models.We applied an initial version of this method during an outbreak of measles in Samoa in 2019e2020 and found that it achieved relatively fast,accurate predictions.Here we present the most recent version of our method and its application to this measles outbreak,along with additional validation.
基金This work was partially funded by grants CAPES,CNPq,LIM01-HCFMUSP,DengueTools(Health theme of the Seventh Framework Programme of the European Community,Grant Agreement Number:282589).
文摘The basic reproduction number,R0,is defined as the expected number of secondary cases of a disease produced by a single infection in a completely susceptible population,and can be estimated in several ways.For example,from the stability analysis of a compartmental model;through the use of the matrix of next generation,or from the final size of an epidemic,etc.In this paper we applied the method for estimating R0 of dengue fever from the initial growth phase of an outbreak,without assuming exponential growth of cases,a common assumption in many studies.We used three different methods of calculating R0 to compare the techniques'details and to evaluate how these techniques estimate the value of R0 of dengue using data from the city of Ribeir^ao Preto(SE of Brazil)in two outbreaks.The results of the three methods are numerically different but,when we compare them using a system of differential equations developed for modeling only the first generation time,we can observe that the methods differ little in the initial growth phase.We conclude that the methods predict that dengue will spread in the city studied and the analysis of the data shows that the estimated values of R0 have an equal pattern overtime.
