Behavioural Change Piecewise Constant Spatial Epidemic Models
Human behaviour significantly affects the dynamics of infectious disease transmission as people adjust their behavior in response to outbreak intensity, thereby impacting disease spread and control efforts. In recent years, there have been efforts to incorporate behavioural change into spatio-tempor...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042724001210 |
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| author | Chinmoy Roy Rahul Rob Deardon |
| author_facet | Chinmoy Roy Rahul Rob Deardon |
| author_sort | Chinmoy Roy Rahul |
| collection | DOAJ |
| description | Human behaviour significantly affects the dynamics of infectious disease transmission as people adjust their behavior in response to outbreak intensity, thereby impacting disease spread and control efforts. In recent years, there have been efforts to incorporate behavioural change into spatio-temporal individual-level models within a Bayesian MCMC framework. In this past work, parametric spatial risk functions were employed, depending on strong underlying assumptions regarding disease transmission mechanisms within the population. However, selecting appropriate parametric functions can be challenging in real-world scenarios, and incorrect assumptions may lead to erroneous conclusions. As an alternative, non-parametric approaches offer greater flexibility. The goal of this study is to investigate the utilization of semi-parametric spatial models for infectious disease transmission, integrating an “alarm function” to account for behavioural change based on infection prevalence over time within a Bayesian MCMC framework. In this paper, we discuss findings from both simulated and real-life epidemics, focusing on constant piecewise distance functions with fixed change points. We also demonstrate the selection of the change points using the Deviance Information Criteria (DIC). |
| format | Article |
| id | doaj-art-b4e8a80f5a4647a889fc4ad2f32d59c8 |
| institution | Kabale University |
| issn | 2468-0427 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj-art-b4e8a80f5a4647a889fc4ad2f32d59c82024-12-21T04:28:49ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272025-03-01101302324Behavioural Change Piecewise Constant Spatial Epidemic ModelsChinmoy Roy Rahul0Rob Deardon1Department of Mathematics and Statistics, Mathematical Sciences Building, University of Calgary, Calgary, T2N 1N4, AB, CanadaDepartment of Mathematics and Statistics, Mathematical Sciences Building, University of Calgary, Calgary, T2N 1N4, AB, Canada; Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, T2N 4Z6, AB, Canada; Corresponding AuthorHuman behaviour significantly affects the dynamics of infectious disease transmission as people adjust their behavior in response to outbreak intensity, thereby impacting disease spread and control efforts. In recent years, there have been efforts to incorporate behavioural change into spatio-temporal individual-level models within a Bayesian MCMC framework. In this past work, parametric spatial risk functions were employed, depending on strong underlying assumptions regarding disease transmission mechanisms within the population. However, selecting appropriate parametric functions can be challenging in real-world scenarios, and incorrect assumptions may lead to erroneous conclusions. As an alternative, non-parametric approaches offer greater flexibility. The goal of this study is to investigate the utilization of semi-parametric spatial models for infectious disease transmission, integrating an “alarm function” to account for behavioural change based on infection prevalence over time within a Bayesian MCMC framework. In this paper, we discuss findings from both simulated and real-life epidemics, focusing on constant piecewise distance functions with fixed change points. We also demonstrate the selection of the change points using the Deviance Information Criteria (DIC).http://www.sciencedirect.com/science/article/pii/S2468042724001210epidemic modelspiecewise spatial riskbehavioural changeBayesian Markov chain Monte Carlofoot-and-mouth disease |
| spellingShingle | Chinmoy Roy Rahul Rob Deardon Behavioural Change Piecewise Constant Spatial Epidemic Models Infectious Disease Modelling epidemic models piecewise spatial risk behavioural change Bayesian Markov chain Monte Carlo foot-and-mouth disease |
| title | Behavioural Change Piecewise Constant Spatial Epidemic Models |
| title_full | Behavioural Change Piecewise Constant Spatial Epidemic Models |
| title_fullStr | Behavioural Change Piecewise Constant Spatial Epidemic Models |
| title_full_unstemmed | Behavioural Change Piecewise Constant Spatial Epidemic Models |
| title_short | Behavioural Change Piecewise Constant Spatial Epidemic Models |
| title_sort | behavioural change piecewise constant spatial epidemic models |
| topic | epidemic models piecewise spatial risk behavioural change Bayesian Markov chain Monte Carlo foot-and-mouth disease |
| url | http://www.sciencedirect.com/science/article/pii/S2468042724001210 |
| work_keys_str_mv | AT chinmoyroyrahul behaviouralchangepiecewiseconstantspatialepidemicmodels AT robdeardon behaviouralchangepiecewiseconstantspatialepidemicmodels |