Upon researching predictive models related toWest Nile virus disease,it is discovered that there are numerous parameters and extensive information in most models,thus contributing to unnecessary complexity.Another cha...Upon researching predictive models related toWest Nile virus disease,it is discovered that there are numerous parameters and extensive information in most models,thus contributing to unnecessary complexity.Another challenge frequently encountered is the lead time,which refers to the period for which predictions are made and often is too short.This paper addresses these issues by introducing a parsimonious method based on ICC curves,offering a logistic distribution model derived from the vector-borne SEIR model.Unlike existing models relying on diverse environmental data,our approach exclusively utilizes historical and present infected human cases(number of new cases).With a yearlong lead time,the predictions extend throughout the 12 months,gaining precision as new data emerge.Theoretical conditions are derived to minimize Bayesian loss,enhancing predictive precision.We construct a Bayesian forecasting probability density function using carefully selected prior distributions.Applying these functions,we predict monthspecific infections nationwide,rigorously evaluating accuracy with probabilistic metrics.Additionally,HPD credible intervals at 90%,95%,and 99%levels is performed.Precision assessment is conducted for HPD intervals,measuring the proportion of intervals that does not include actual reported cases for 2020e2022.展开更多
As an emerging infectious disease,the 2019 coronavirus disease(COVID-19)has developed into a global pandemic.During the initial spreading of the virus in China,we demonstrated the ensemble Kalman filter performed well...As an emerging infectious disease,the 2019 coronavirus disease(COVID-19)has developed into a global pandemic.During the initial spreading of the virus in China,we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City.Second,we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios.Our simulation results show that without continued control measures,the epidemic in Hubei Province could have become persistent.Only by continuing to decrease the infection rate through 1)protective measures and 2)social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation.Finally,we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories,compared to those obtained with Markov processes.Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes,future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories.In addition,shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.展开更多
文摘Upon researching predictive models related toWest Nile virus disease,it is discovered that there are numerous parameters and extensive information in most models,thus contributing to unnecessary complexity.Another challenge frequently encountered is the lead time,which refers to the period for which predictions are made and often is too short.This paper addresses these issues by introducing a parsimonious method based on ICC curves,offering a logistic distribution model derived from the vector-borne SEIR model.Unlike existing models relying on diverse environmental data,our approach exclusively utilizes historical and present infected human cases(number of new cases).With a yearlong lead time,the predictions extend throughout the 12 months,gaining precision as new data emerge.Theoretical conditions are derived to minimize Bayesian loss,enhancing predictive precision.We construct a Bayesian forecasting probability density function using carefully selected prior distributions.Applying these functions,we predict monthspecific infections nationwide,rigorously evaluating accuracy with probabilistic metrics.Additionally,HPD credible intervals at 90%,95%,and 99%levels is performed.Precision assessment is conducted for HPD intervals,measuring the proportion of intervals that does not include actual reported cases for 2020e2022.
基金This work was supported by the Department of the Army,U.S.Army Contracting Command,Aberdeen Proving Ground,Natick Contracting Division,Ft Detrick,MD(DWFP grant W911QY-19-1-0004)the National Science Foundation under Grant Award IIS-2027336Any opinions,findings,and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred.
文摘As an emerging infectious disease,the 2019 coronavirus disease(COVID-19)has developed into a global pandemic.During the initial spreading of the virus in China,we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City.Second,we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios.Our simulation results show that without continued control measures,the epidemic in Hubei Province could have become persistent.Only by continuing to decrease the infection rate through 1)protective measures and 2)social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation.Finally,we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories,compared to those obtained with Markov processes.Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes,future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories.In addition,shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.
