State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection
We present an exploration of advanced stochastic simulation techniques for state-space models, with a specific focus on their applications in infectious disease modelling. Utilizing COVID-19 surveillance data from the province of Ontario, Canada, we employ Markov Chain Monte Carlo (MCMC) and Sequent...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042725000375 |
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| author | Christopher D. Prashad |
| author_facet | Christopher D. Prashad |
| author_sort | Christopher D. Prashad |
| collection | DOAJ |
| description | We present an exploration of advanced stochastic simulation techniques for state-space models, with a specific focus on their applications in infectious disease modelling. Utilizing COVID-19 surveillance data from the province of Ontario, Canada, we employ Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods to detect structural changes and pre-dict future trends in case counts. Our approach begins with the application of a Kalman smoothing technique, integrated with MCMC for state sampling within local level and seasonal models, alongside Bayesian inference for non-linear dynamic regression models. We then assess the effectiveness of various priors, including normal, Student's t, Laplace, and horseshoe distributions, in capturing abrupt changes within the data using a Rao-Blackwellized par-ticle filter. Our findings highlight the superior performance of the horseshoe prior in identifying change points and adapting to complex data structures, offering valuable insights for real-time monitoring and forecasting in public health. This study emphasizes the efficacy of state-space models, particu-larly when enhanced with sophisticated prior distributions, in providing a nuanced understanding of infectious disease transmission. |
| format | Article |
| id | doaj-art-b20251ca1326467f876e32c85db02f72 |
| institution | Kabale University |
| issn | 2468-0427 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj-art-b20251ca1326467f876e32c85db02f722025-08-22T04:56:57ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272025-12-011041507153210.1016/j.idm.2025.05.005State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detectionChristopher D. Prashad0Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, CanadaWe present an exploration of advanced stochastic simulation techniques for state-space models, with a specific focus on their applications in infectious disease modelling. Utilizing COVID-19 surveillance data from the province of Ontario, Canada, we employ Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods to detect structural changes and pre-dict future trends in case counts. Our approach begins with the application of a Kalman smoothing technique, integrated with MCMC for state sampling within local level and seasonal models, alongside Bayesian inference for non-linear dynamic regression models. We then assess the effectiveness of various priors, including normal, Student's t, Laplace, and horseshoe distributions, in capturing abrupt changes within the data using a Rao-Blackwellized par-ticle filter. Our findings highlight the superior performance of the horseshoe prior in identifying change points and adapting to complex data structures, offering valuable insights for real-time monitoring and forecasting in public health. This study emphasizes the efficacy of state-space models, particu-larly when enhanced with sophisticated prior distributions, in providing a nuanced understanding of infectious disease transmission.http://www.sciencedirect.com/science/article/pii/S2468042725000375State-space modellingNonlinear dynamic regressionStochastic simulationStructural change detectionRao-blackwellized particle filterInfectious disease surveillance data |
| spellingShingle | Christopher D. Prashad State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection Infectious Disease Modelling State-space modelling Nonlinear dynamic regression Stochastic simulation Structural change detection Rao-blackwellized particle filter Infectious disease surveillance data |
| title | State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection |
| title_full | State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection |
| title_fullStr | State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection |
| title_full_unstemmed | State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection |
| title_short | State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection |
| title_sort | state space modelling for infectious disease surveillance data stochastic simulation techniques and structural change detection |
| topic | State-space modelling Nonlinear dynamic regression Stochastic simulation Structural change detection Rao-blackwellized particle filter Infectious disease surveillance data |
| url | http://www.sciencedirect.com/science/article/pii/S2468042725000375 |
| work_keys_str_mv | AT christopherdprashad statespacemodellingforinfectiousdiseasesurveillancedatastochasticsimulationtechniquesandstructuralchangedetection |