tetano
Editor, Senior Moderator
Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting.
Methods: A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan.
By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions.
Results: The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates.
The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds.
Conclusions: Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details.
The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance.
Author: Hiroshi Nishiura
Credits/Source: BioMedical Engineering OnLine 2011, 10:15
http://7thspace.com/headlines/37294...se_study_of_pandemic_influenza_h1n1_2009.html
Methods: A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan.
By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions.
Results: The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates.
The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds.
Conclusions: Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details.
The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance.
Author: Hiroshi Nishiura
Credits/Source: BioMedical Engineering OnLine 2011, 10:15
http://7thspace.com/headlines/37294...se_study_of_pandemic_influenza_h1n1_2009.html