Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive
<jats:title>Abstract</jats:title><jats:p>Successful epidemic modeling requires understanding the implicit feedback control strategies used by populations to modulate the spread of contagion. While such strategies can be replicated with intricate modeling assumptions, here we propos...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2023
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| Online Access: | https://hdl.handle.net/1721.1/148594 |
| _version_ | 1811092555580833792 |
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| author | Jadbabaie, Ali Sarker, Arnab Shah, Devavrat |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Jadbabaie, Ali Sarker, Arnab Shah, Devavrat |
| author_sort | Jadbabaie, Ali |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>Successful epidemic modeling requires understanding the implicit feedback control strategies used by populations to modulate the spread of contagion. While such strategies can be replicated with intricate modeling assumptions, here we propose a simple model where infection dynamics are described by a three parameter feedback policy. Rather than model individuals as directly controlling the contact rate which governs the spread of disease, we model them as controlling the contact rate’s derivative, resulting in a dynamic rather than kinematic model. The feedback policy used by populations across the United States which best fits observations is proportional-derivative control, where learned parameters strongly correlate with observed interventions (e.g., vaccination rates and mobility restrictions). However, this results in a non-zero “steady-state” of case counts, implying current mitigation strategies cannot eradicate COVID-19. Hence, we suggest making implicit policies a function of cumulative cases, resulting in proportional-integral-derivative control with higher potential to eliminate COVID-19.</jats:p> |
| first_indexed | 2024-09-23T15:19:45Z |
| format | Article |
| id | mit-1721.1/148594 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:19:45Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1485942023-03-18T03:07:54Z Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive Jadbabaie, Ali Sarker, Arnab Shah, Devavrat Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Institute for Data, Systems, and Society <jats:title>Abstract</jats:title><jats:p>Successful epidemic modeling requires understanding the implicit feedback control strategies used by populations to modulate the spread of contagion. While such strategies can be replicated with intricate modeling assumptions, here we propose a simple model where infection dynamics are described by a three parameter feedback policy. Rather than model individuals as directly controlling the contact rate which governs the spread of disease, we model them as controlling the contact rate’s derivative, resulting in a dynamic rather than kinematic model. The feedback policy used by populations across the United States which best fits observations is proportional-derivative control, where learned parameters strongly correlate with observed interventions (e.g., vaccination rates and mobility restrictions). However, this results in a non-zero “steady-state” of case counts, implying current mitigation strategies cannot eradicate COVID-19. Hence, we suggest making implicit policies a function of cumulative cases, resulting in proportional-integral-derivative control with higher potential to eliminate COVID-19.</jats:p> 2023-03-17T15:52:55Z 2023-03-17T15:52:55Z 2023-02-23 2023-03-17T15:39:29Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/148594 Jadbabaie, Ali, Sarker, Arnab and Shah, Devavrat. 2023. "Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive." Scientific Reports, 13 (1). en 10.1038/s41598-023-29542-8 Scientific Reports Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Scientific Reports |
| spellingShingle | Jadbabaie, Ali Sarker, Arnab Shah, Devavrat Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive |
| title | Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive |
| title_full | Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive |
| title_fullStr | Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive |
| title_full_unstemmed | Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive |
| title_short | Implicit feedback policies for COVID-19: why “zero-COVID” policies remain elusive |
| title_sort | implicit feedback policies for covid 19 why zero covid policies remain elusive |
| url | https://hdl.handle.net/1721.1/148594 |
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