Evidence map›Paper›PMID 37193062›Full record

ArticleInformation sciences2023

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Xinqi Du, Hechang Chen, Bo Yang, Cheng Long, Songwei Zhao

Open access · greenAbstract read
In one paragraph

Article in Information sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Xinqi DuSchool of Artificial Intelligence, Jilin University, Changchun 130012, China.
Hechang ChenSchool of Artificial Intelligence, Jilin University, Changchun 130012, China.
Bo YangCollege of Computer Science and Technology, Jilin University, Changchun 130012, China.
Cheng LongSchool of Computer Science and Engineering, Nanyang Technological University, Singapore.
Songwei ZhaoSchool of Artificial Intelligence, Jilin University, Changchun 130012, China.
Ministry of Education of the People's Republic of China · CNNanyang Technological University · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a

Indexed as

Deep reinforcement learningEpidemic controlHierarchical reinforcement learningMulti-mode intervention

Identifiers

PMID37193062
PMCPMC10167779
OpenAlexW4375948715

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.