ArticleEuropean journal of operational research2023
Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations.
Article in European journal of operational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Optimising pandemic response through vaccination strategies using neural networks.Scientific reports · 2026Article
- SEIRV epidemiological model for COVID 19 with Holling type II functional response.Scientific reports · 2026Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Article
- The application of artificial intelligence in health policy: a scoping review.BMC health services research · 2023Article
- HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.Information sciences · 2023Article
- An analytical approach to evaluate the impact of age demographics in a pandemic.Stochastic environmental research and risk assessment : research journal · 2023Article
- Risk factors for COVID-19 outbreaks in livestock slaughtering and processing facilities in Republic of Korea.Osong public health and research perspectives · 2023Article
- Optimal timing of non-pharmaceutical interventions during an epidemic.European journal of operational research · 2023Article
- Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.Neural computing & applications · 2023Review
- Introduction to the special issue on the role of operational research in future epidemics/ pandemics.European journal of operational research · 2023Article
- Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature.Yonsei medical journal · 2023Review
- Did the general practice residents well adapt to real public health prevention --a study from the COVID-19 prevention training in China.BMC medical education · 2022Article
- Product availability and stockpiling in times of pandemic: causes of supply chain disruptions and preventive measures in retailing.Annals of operations research · 2022Article
- Polyelectrolyte Membrane Nanocoatings Aimed at Personal Protective and Medical Equipment Surfaces to Reduce Coronavirus Spreading.Membranes · 2022Article
- COVID-19 and credit risk: A long memory perspective.Insurance, mathematics & economics · 2022Article
- Investigation of Factors Affecting COVID-19 and Sixth Wave Management Using a System Dynamics Approach.Journal of healthcare engineering · 2022Article
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Authors and funding
3 authors.
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Abstract
Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy.
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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.