A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates.
During pandemics, countries, regions, and communities develop various epidemic models to evaluate spread and guide mitigation policies. However, model uncertainties caused by complex transmission behaviors, contact-tracing networks, time-varying parameters, human factors, and limited data present si...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012569 |
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| _version_ | 1850105383853490176 |
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| author | Baike She Rebecca Lee Smith Ian Pytlarz Shreyas Sundaram Philip E Paré |
| author_facet | Baike She Rebecca Lee Smith Ian Pytlarz Shreyas Sundaram Philip E Paré |
| author_sort | Baike She |
| collection | DOAJ |
| description | During pandemics, countries, regions, and communities develop various epidemic models to evaluate spread and guide mitigation policies. However, model uncertainties caused by complex transmission behaviors, contact-tracing networks, time-varying parameters, human factors, and limited data present significant challenges to model-based approaches. To address these issues, we propose a novel framework that centers around reproduction number estimates to perform counterfactual analysis, strategy evaluation, and feedback control of epidemics. The framework 1) introduces a mechanism to quantify the impact of the testing-for-isolation intervention strategy on the basic reproduction number. Building on this mechanism, the framework 2) proposes a method to reverse engineer the effective reproduction number under different strengths of the intervention strategy. In addition, based on the method that quantifies the impact of the testing-for-isolation strategy on the basic reproduction number, the framework 3) proposes a closed-loop control algorithm that uses the effective reproduction number both as feedback to indicate the severity of the spread and as the control goal to guide adjustments in the intensity of the intervention. We illustrate the framework, along with its three core methods, by addressing three key questions and validating its effectiveness using data collected during the COVID-19 pandemic at the University of Illinois Urbana-Champaign (UIUC) and Purdue University: 1) How severe would an outbreak have been without the implemented intervention strategies? 2) What impact would varying the intervention strength have had on an outbreak? 3) How can we adjust the intervention intensity based on the current state of an outbreak? |
| format | Article |
| id | doaj-art-3f53da682ef241e08a90ad75d224ee7e |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-3f53da682ef241e08a90ad75d224ee7e2025-08-20T02:39:07ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-11-012011e101256910.1371/journal.pcbi.1012569A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates.Baike SheRebecca Lee SmithIan PytlarzShreyas SundaramPhilip E ParéDuring pandemics, countries, regions, and communities develop various epidemic models to evaluate spread and guide mitigation policies. However, model uncertainties caused by complex transmission behaviors, contact-tracing networks, time-varying parameters, human factors, and limited data present significant challenges to model-based approaches. To address these issues, we propose a novel framework that centers around reproduction number estimates to perform counterfactual analysis, strategy evaluation, and feedback control of epidemics. The framework 1) introduces a mechanism to quantify the impact of the testing-for-isolation intervention strategy on the basic reproduction number. Building on this mechanism, the framework 2) proposes a method to reverse engineer the effective reproduction number under different strengths of the intervention strategy. In addition, based on the method that quantifies the impact of the testing-for-isolation strategy on the basic reproduction number, the framework 3) proposes a closed-loop control algorithm that uses the effective reproduction number both as feedback to indicate the severity of the spread and as the control goal to guide adjustments in the intensity of the intervention. We illustrate the framework, along with its three core methods, by addressing three key questions and validating its effectiveness using data collected during the COVID-19 pandemic at the University of Illinois Urbana-Champaign (UIUC) and Purdue University: 1) How severe would an outbreak have been without the implemented intervention strategies? 2) What impact would varying the intervention strength have had on an outbreak? 3) How can we adjust the intervention intensity based on the current state of an outbreak?https://doi.org/10.1371/journal.pcbi.1012569 |
| spellingShingle | Baike She Rebecca Lee Smith Ian Pytlarz Shreyas Sundaram Philip E Paré A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates. PLoS Computational Biology |
| title | A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates. |
| title_full | A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates. |
| title_fullStr | A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates. |
| title_full_unstemmed | A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates. |
| title_short | A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates. |
| title_sort | framework for counterfactual analysis strategy evaluation and control of epidemics using reproduction number estimates |
| url | https://doi.org/10.1371/journal.pcbi.1012569 |
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