Estimating Unobservable States in Stochastic Epidemic Models with Partial Information
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arXiv
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2025
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| author | Kamkumo, Florent Ouabo Njiasse, Ibrahim Mbouandi Wunderlich, Ralf |
| author_facet | Kamkumo, Florent Ouabo Njiasse, Ibrahim Mbouandi Wunderlich, Ralf |
| contents | This article investigates stochastic epidemic models with partial information and addresses the estimation of current values of not directly observable states. The latter is also called nowcasting and related to the so-called "dark figure" problem, which concerns, for example, the estimation of unknown numbers of asymptomatic and undetected infections. The study is based on Ouabo Kamkumo et al. (2025), which provides detailed information about stochastic multi-compartment epidemic models with partial information and various examples. Starting point is a description of the state dynamics by a system of nonlinear stochastic recursions resulting from a time-discretization of a diffusion approximation of the underlying counting processes. The state vector is decomposed into an observable and an unobservable component. The latter is estimated from the observations using the extended Kalman filter approach in order to take into account the nonlinearity of the state dynamics. Numerical simulations for a Covid-19 model with partial information are presented to verify the performance and accuracy of the estimation method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_00906 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating Unobservable States in Stochastic Epidemic Models with Partial Information Kamkumo, Florent Ouabo Njiasse, Ibrahim Mbouandi Wunderlich, Ralf Populations and Evolution Probability Statistics Theory 92D30, 92-10, 60J60, 60G35, 62M20 This article investigates stochastic epidemic models with partial information and addresses the estimation of current values of not directly observable states. The latter is also called nowcasting and related to the so-called "dark figure" problem, which concerns, for example, the estimation of unknown numbers of asymptomatic and undetected infections. The study is based on Ouabo Kamkumo et al. (2025), which provides detailed information about stochastic multi-compartment epidemic models with partial information and various examples. Starting point is a description of the state dynamics by a system of nonlinear stochastic recursions resulting from a time-discretization of a diffusion approximation of the underlying counting processes. The state vector is decomposed into an observable and an unobservable component. The latter is estimated from the observations using the extended Kalman filter approach in order to take into account the nonlinearity of the state dynamics. Numerical simulations for a Covid-19 model with partial information are presented to verify the performance and accuracy of the estimation method. |
| title | Estimating Unobservable States in Stochastic Epidemic Models with Partial Information |
| topic | Populations and Evolution Probability Statistics Theory 92D30, 92-10, 60J60, 60G35, 62M20 |
| url | https://arxiv.org/abs/2506.00906 |