Inference in epidemiological agent-based models using ensemble-based data assimilation.
To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilatio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0264892 |
| _version_ | 1818288421018271744 |
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| author | Tadeo Javier Cocucci Manuel Pulido Juan Pablo Aparicio Juan Ruíz Mario Ignacio Simoy Santiago Rosa |
| author_facet | Tadeo Javier Cocucci Manuel Pulido Juan Pablo Aparicio Juan Ruíz Mario Ignacio Simoy Santiago Rosa |
| author_sort | Tadeo Javier Cocucci |
| collection | DOAJ |
| description | To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiological interest. However, there are many important features which are based on the individual interactions that cannot be represented in the mean field equations, such as social network and bubbles, contact tracing, isolating individuals in risk, and social network-based distancing strategies. Agent-based models can describe contact networks at an individual level, including demographic attributes such as age, neighborhood, household, workplaces, schools, entertainment places, among others. Nevertheless, these models have several unknown parameters which are thus difficult to prescribe. In this work, we propose the use of ensemble-based data assimilation techniques to calibrate an agent-based model using daily epidemiological data. This raises the challenge of having to adapt the agent populations to incorporate the information provided by the coarse-grained data. To do this, two stochastic strategies to correct the model predictions are developed. The ensemble Kalman filter with perturbed observations is used for the joint estimation of the state and some key epidemiological parameters. We conduct experiments with an agent based-model designed for COVID-19 and assess the proposed methodology on synthetic data and on COVID-19 daily reports from Ciudad Autónoma de Buenos Aires, Argentina. |
| first_indexed | 2024-12-13T01:56:06Z |
| format | Article |
| id | doaj.art-4192be8804f0404283a2f476c32edbcc |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T01:56:06Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4192be8804f0404283a2f476c32edbcc2022-12-22T00:03:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01173e026489210.1371/journal.pone.0264892Inference in epidemiological agent-based models using ensemble-based data assimilation.Tadeo Javier CocucciManuel PulidoJuan Pablo AparicioJuan RuízMario Ignacio SimoySantiago RosaTo represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiological interest. However, there are many important features which are based on the individual interactions that cannot be represented in the mean field equations, such as social network and bubbles, contact tracing, isolating individuals in risk, and social network-based distancing strategies. Agent-based models can describe contact networks at an individual level, including demographic attributes such as age, neighborhood, household, workplaces, schools, entertainment places, among others. Nevertheless, these models have several unknown parameters which are thus difficult to prescribe. In this work, we propose the use of ensemble-based data assimilation techniques to calibrate an agent-based model using daily epidemiological data. This raises the challenge of having to adapt the agent populations to incorporate the information provided by the coarse-grained data. To do this, two stochastic strategies to correct the model predictions are developed. The ensemble Kalman filter with perturbed observations is used for the joint estimation of the state and some key epidemiological parameters. We conduct experiments with an agent based-model designed for COVID-19 and assess the proposed methodology on synthetic data and on COVID-19 daily reports from Ciudad Autónoma de Buenos Aires, Argentina.https://doi.org/10.1371/journal.pone.0264892 |
| spellingShingle | Tadeo Javier Cocucci Manuel Pulido Juan Pablo Aparicio Juan Ruíz Mario Ignacio Simoy Santiago Rosa Inference in epidemiological agent-based models using ensemble-based data assimilation. PLoS ONE |
| title | Inference in epidemiological agent-based models using ensemble-based data assimilation. |
| title_full | Inference in epidemiological agent-based models using ensemble-based data assimilation. |
| title_fullStr | Inference in epidemiological agent-based models using ensemble-based data assimilation. |
| title_full_unstemmed | Inference in epidemiological agent-based models using ensemble-based data assimilation. |
| title_short | Inference in epidemiological agent-based models using ensemble-based data assimilation. |
| title_sort | inference in epidemiological agent based models using ensemble based data assimilation |
| url | https://doi.org/10.1371/journal.pone.0264892 |
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