Assessment of event-triggered policies of nonpharmaceutical interventions based on epidemiological indicators
Author
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Castillo Laborde, Carla
Author
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de Wolff, Taco
Author
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Gajardo, Pedro
Author
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Lecaros, Rodrigo
Author
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Olivar Tost, Gerard
Author
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Ramírez Cabrera, Héctor Ariel
Admission date
dc.date.accessioned
2021-12-21T19:36:27Z
Available date
dc.date.available
2021-12-21T19:36:27Z
Publication date
dc.date.issued
2021
Cita de ítem
dc.identifier.citation
Journal of Mathematical Biology (2021) 83:42
es_ES
Identifier
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10.1007/s00285-021-01669-0
Identifier
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https://repositorio.uchile.cl/handle/2250/183324
Abstract
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Nonpharmaceutical interventions (NPI) such as banning public events or instituting lockdowns have been widely applied around the world to control the current COVID-19 pandemic. Typically, this type of intervention is imposed when an epidemiological indicator in a given population exceeds a certain threshold. Then, the nonpharmaceutical intervention is lifted when the levels of the indicator used have decreased sufficiently. What is the best indicator to use? In this paper, we propose a mathematical framework to try to answer this question. More specifically, the proposed framework permits to assess and compare different event-triggered controls based on epidemiological indicators. Our methodology consists of considering some outcomes that are consequences of the nonpharmaceutical interventions that a decision maker aims to make as low as possible. The peak demand for intensive care units (ICU) and the total number of days in lockdown are examples of such outcomes. If an epidemiological indicator is used to trigger the interventions, there is naturally a trade-off between the outcomes that can be seen as a curve parameterized by the trigger threshold to be used. The computation of these curves for a group of indicators then allows the selection of the best indicator the curve of which dominates the curves of the other indicators. This methodology is illustrated with indicators in the context of COVID-19 using deterministic compartmental models in discrete-time, although the framework can be adapted for a larger class of models.
es_ES
Patrocinador
dc.description.sponsorship
Basal Program from ANID-Chile CMM-AFB 170001
FONDECYT from ANID-Chile 1200355
es_ES
Lenguage
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en
es_ES
Publisher
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Springer
es_ES
Type of license
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Attribution-NonCommercial-NoDerivs 3.0 United States