Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Reinforcement Learning for Optimization of COVID-19 Mitigation policies
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2010.10560.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:40:51.973756Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T07:46:46.179634Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5a3250c6-f159-4b36-b989-383caa4044ef · inbound
A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis cites this paper.
A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis Reinforcement Learning for Optimization of COVID-19 Mitigation policies
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T05:33:56.511450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b941cd25-00c6-4f0b-91ae-3711f29e5d27 · inbound
Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data cites this paper.
Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data Reinforcement Learning for Optimization of COVID-19 Mitigation policies
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:40:51.973756Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9761d8a6-01c7-4ce7-a588-94624c8ad982 · inbound
Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning cites this paper.
Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning Reinforcement Learning for Optimization of COVID-19 Mitigation policies
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T07:59:50.759532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 08587e4e-c312-4bc2-95e4-75c702ebc461 · inbound
When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks? cites this paper.
When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks? Reinforcement Learning for Optimization of COVID-19 Mitigation policies
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-28T23:42:49.589091Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d058c3ec-911e-4642-87b0-b395a7b3a335 · inbound
When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks? cites this paper.
When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks? Reinforcement Learning for Optimization of COVID-19 Mitigation policies
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T05:43:07.722514Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2de3ed79-4cf1-4590-ac89-1ad4a84ef8f0 · inbound
Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models cites this paper.
Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models Reinforcement Learning for Optimization of COVID-19 Mitigation policies
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T07:46:46.181133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.