File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China

TitleOptimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China
Authors
Keywordsartificial intelligence
Covid-19
infectious diseases
machine learning
mathematical modelling
reinforcement learning
Issue Date2023
Citation
Journal of the American Medical Informatics Association : JAMIA, 2023, v. 30, n. 9, p. 1543-1551 How to Cite?
AbstractBACKGROUND: Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources. METHODS: Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context. FINDINGS: Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing. INTERPRETATION: DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.
Persistent Identifierhttp://hdl.handle.net/10722/330489
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYao, Yao-
dc.contributor.authorZhou, Hanchu-
dc.contributor.authorCao, Zhidong-
dc.contributor.authorZeng, Daniel Dajun-
dc.contributor.authorZhang, Qingpeng-
dc.date.accessioned2023-09-05T12:11:09Z-
dc.date.available2023-09-05T12:11:09Z-
dc.date.issued2023-
dc.identifier.citationJournal of the American Medical Informatics Association : JAMIA, 2023, v. 30, n. 9, p. 1543-1551-
dc.identifier.urihttp://hdl.handle.net/10722/330489-
dc.description.abstractBACKGROUND: Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources. METHODS: Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context. FINDINGS: Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing. INTERPRETATION: DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.-
dc.languageeng-
dc.relation.ispartofJournal of the American Medical Informatics Association : JAMIA-
dc.subjectartificial intelligence-
dc.subjectCovid-19-
dc.subjectinfectious diseases-
dc.subjectmachine learning-
dc.subjectmathematical modelling-
dc.subjectreinforcement learning-
dc.titleOptimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/jamia/ocad116-
dc.identifier.pmid37364025-
dc.identifier.scopuseid_2-s2.0-85168239982-
dc.identifier.volume30-
dc.identifier.issue9-
dc.identifier.spage1543-
dc.identifier.epage1551-
dc.identifier.eissn1527-974X-
dc.identifier.isiWOS:001016260200001-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats