ArticleApplied network science2021
Towards Dynamic lockdown strategies controlling pandemic spread under healthcare resource budget.
Article in Applied network science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.Health care management science · 2025Review
- Non-pharmaceutical interventions in the State of Georgia: Economic implications.Ethics, medicine, and public health · 2023Article
- An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020-2021.PLoS neglected tropical diseases · 2022Article
- Spatiotemporal tracing of pandemic spread from infection data.Scientific reports · 2021Article
- A Comparative Study on Distancing, Mask and Vaccine Adoption Rates from Global Twitter Trends.Healthcare (Basel, Switzerland) · 2021Article
- A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research.IEEE access : practical innovations, open solutions · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is one of the deadliest pandemics in modern human history that has killed nearly a million people and rapidly inundated the healthcare resources around the world. Current lockdown measures to curb infection spread are threatening to bring the world economy to a halt, necessitating dynamic lockdown policies that incorporate the healthcare resource budget of people in a zone. We conceive a dynamic pandemic lockdown strategy that employs reinforcement learning to modulate the zone mobility, while restricting the COVID-19 hospitalizations within its healthcare resource budget. We employ queueing theory to model the inflow and outflow of patients and validate the approach through extensive simulation on real demographic and epidemiological data from the boroughs of New York City. Our experiments demonstrate that this approach can not only adapt to the varying trends in contagion in a region by regulating its own lockdown level, but also manages the overheads associated with time-varying dynamic lockdown policies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.