tetano
Editor, Senior Moderator
J Math Biol
. 2022 Sep 26;85(4):40.
doi: 10.1007/s00285-022-01806-3.
Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics
Romain Narci[SUP] 1 [/SUP], Maud Delattre[SUP] 2 [/SUP], Catherine Larédo[SUP] 2 [/SUP], Elisabeta Vergu[SUP] 2 [/SUP]
Affiliations
Abstract
The estimation from available data of parameters governing epidemics is a major challenge. In addition to usual issues (data often incomplete and noisy), epidemics of the same nature may be observed in several places or over different periods. The resulting possible inter-epidemic variability is rarely explicitly considered. Here, we propose to tackle multiple epidemics through a unique model incorporating a stochastic representation for each epidemic and to jointly estimate its parameters from noisy and partial observations. By building on a previous work for prevalence data, a Gaussian state-space model is extended to a model with mixed effects on the parameters describing simultaneously several epidemics and their observation process. An appropriate inference method is developed, by coupling the SAEM algorithm with Kalman-type filtering. Moreover, we consider here incidence data, which requires to develop a new version of the filtering algorithm. Its performances are investigated on SIR simulated epidemics for prevalence and incidence data. Our method outperforms an inference method separately processing each dataset. An application to SEIR influenza outbreaks in France over several years using incidence data is also carried out. Parameter estimations highlight a non-negligible variability between influenza seasons, both in transmission and case reporting. The main contribution of our study is to rigorously and explicitly account for the inter-epidemic variability between multiple outbreaks, both from the viewpoint of modeling and inference with a parsimonious statistical model.
Keywords: Kalman filter; Latent variables; Parametric inference; Random effects; SAEM algorithm; Stochastic compartmental models.
. 2022 Sep 26;85(4):40.
doi: 10.1007/s00285-022-01806-3.
Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics
Romain Narci[SUP] 1 [/SUP], Maud Delattre[SUP] 2 [/SUP], Catherine Larédo[SUP] 2 [/SUP], Elisabeta Vergu[SUP] 2 [/SUP]
Affiliations
- PMID: 36161526
- DOI: 10.1007/s00285-022-01806-3
Abstract
The estimation from available data of parameters governing epidemics is a major challenge. In addition to usual issues (data often incomplete and noisy), epidemics of the same nature may be observed in several places or over different periods. The resulting possible inter-epidemic variability is rarely explicitly considered. Here, we propose to tackle multiple epidemics through a unique model incorporating a stochastic representation for each epidemic and to jointly estimate its parameters from noisy and partial observations. By building on a previous work for prevalence data, a Gaussian state-space model is extended to a model with mixed effects on the parameters describing simultaneously several epidemics and their observation process. An appropriate inference method is developed, by coupling the SAEM algorithm with Kalman-type filtering. Moreover, we consider here incidence data, which requires to develop a new version of the filtering algorithm. Its performances are investigated on SIR simulated epidemics for prevalence and incidence data. Our method outperforms an inference method separately processing each dataset. An application to SEIR influenza outbreaks in France over several years using incidence data is also carried out. Parameter estimations highlight a non-negligible variability between influenza seasons, both in transmission and case reporting. The main contribution of our study is to rigorously and explicitly account for the inter-epidemic variability between multiple outbreaks, both from the viewpoint of modeling and inference with a parsimonious statistical model.
Keywords: Kalman filter; Latent variables; Parametric inference; Random effects; SAEM algorithm; Stochastic compartmental models.