Calibration verification for stochastic agent-based disease spread models.
Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alon...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315429 |
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| _version_ | 1841555363213606912 |
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| author | Maya Horii Aidan Gould Zachary Yun Jaideep Ray Cosmin Safta Tarek Zohdi |
| author_facet | Maya Horii Aidan Gould Zachary Yun Jaideep Ray Cosmin Safta Tarek Zohdi |
| author_sort | Maya Horii |
| collection | DOAJ |
| description | Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques. |
| format | Article |
| id | doaj-art-169e3a1dd7ba4f138cc33e0cb4509be0 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-169e3a1dd7ba4f138cc33e0cb4509be02025-01-08T05:33:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031542910.1371/journal.pone.0315429Calibration verification for stochastic agent-based disease spread models.Maya HoriiAidan GouldZachary YunJaideep RayCosmin SaftaTarek ZohdiAccurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.https://doi.org/10.1371/journal.pone.0315429 |
| spellingShingle | Maya Horii Aidan Gould Zachary Yun Jaideep Ray Cosmin Safta Tarek Zohdi Calibration verification for stochastic agent-based disease spread models. PLoS ONE |
| title | Calibration verification for stochastic agent-based disease spread models. |
| title_full | Calibration verification for stochastic agent-based disease spread models. |
| title_fullStr | Calibration verification for stochastic agent-based disease spread models. |
| title_full_unstemmed | Calibration verification for stochastic agent-based disease spread models. |
| title_short | Calibration verification for stochastic agent-based disease spread models. |
| title_sort | calibration verification for stochastic agent based disease spread models |
| url | https://doi.org/10.1371/journal.pone.0315429 |
| work_keys_str_mv | AT mayahorii calibrationverificationforstochasticagentbaseddiseasespreadmodels AT aidangould calibrationverificationforstochasticagentbaseddiseasespreadmodels AT zacharyyun calibrationverificationforstochasticagentbaseddiseasespreadmodels AT jaideepray calibrationverificationforstochasticagentbaseddiseasespreadmodels AT cosminsafta calibrationverificationforstochasticagentbaseddiseasespreadmodels AT tarekzohdi calibrationverificationforstochasticagentbaseddiseasespreadmodels |