Bayesian modeling of dynamic behavioral change during an epidemic
For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-12-01
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| Series: | Infectious Disease Modelling |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042723000787 |
| _version_ | 1797448060900474880 |
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| author | Caitlin Ward Rob Deardon Alexandra M. Schmidt |
| author_facet | Caitlin Ward Rob Deardon Alexandra M. Schmidt |
| author_sort | Caitlin Ward |
| collection | DOAJ |
| description | For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of “alarm” in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics. |
| first_indexed | 2024-03-09T14:04:55Z |
| format | Article |
| id | doaj.art-7cfea4e5f9b542f9a4d50cf7964858c2 |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-03-09T14:04:55Z |
| publishDate | 2023-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-7cfea4e5f9b542f9a4d50cf7964858c22023-11-30T05:08:10ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272023-12-0184947963Bayesian modeling of dynamic behavioral change during an epidemicCaitlin Ward0Rob Deardon1Alexandra M. Schmidt2Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA; Corresponding author.Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada; Department of Mathematics and Statistics, University of Calgary, Calgary, AB, CanadaDepartment of Epidemiology, Biostatistics, and Occupational Health, Montreal, QC, CanadaFor many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of “alarm” in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.http://www.sciencedirect.com/science/article/pii/S2468042723000787Bayesian inferenceCompartmental modelSIRSEIRTransmission modeling |
| spellingShingle | Caitlin Ward Rob Deardon Alexandra M. Schmidt Bayesian modeling of dynamic behavioral change during an epidemic Infectious Disease Modelling Bayesian inference Compartmental model SIR SEIR Transmission modeling |
| title | Bayesian modeling of dynamic behavioral change during an epidemic |
| title_full | Bayesian modeling of dynamic behavioral change during an epidemic |
| title_fullStr | Bayesian modeling of dynamic behavioral change during an epidemic |
| title_full_unstemmed | Bayesian modeling of dynamic behavioral change during an epidemic |
| title_short | Bayesian modeling of dynamic behavioral change during an epidemic |
| title_sort | bayesian modeling of dynamic behavioral change during an epidemic |
| topic | Bayesian inference Compartmental model SIR SEIR Transmission modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2468042723000787 |
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