ArticlePloS one2023
A comparative study of the policy response to COVID-19 in the ASEAN region: A dynamic simulated ARDL approach.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Research on New Methods of Topic Mining and Topic Prediction for Medical Preprints on Emerging Infectious Diseases.Cureus · 2025Article
- Retraction: A comparative study of the policy response to COVID-19 in the ASEAN region: A dynamic simulated ARDL approach.PloS one · 2025Article
- Article
- The impacts of COVID-19 on seafood prices in Japan: A comparison between cheap and luxury products.PloS one · 2023Article
Corrections and comments
- Retraction · 2025-08-25Concerns/Issues about Authorship/Affiliation · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
4 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 epidemic is the most significant global health disaster of this century and the greatest challenge to humanity since World War II. One of the most important research issues is to determine the effectiveness of measures implemented worldwide to control the spread of the corona virus. A dynamic simulated Autoregressive-Distributed Lag (ARDL) approach was adopted to analyze the policy response to COVID-19 in the ASEAN region using data from February 1, 2020, to November 8, 2021. The results of unit root concluded that the dependent variable is integrated of order one while the independent variables are stationarized at the level or first difference, and the use of a dynamic simulated ARDL technique is appropriate for this paper. The outcomes of the dynamic simulated ARDL model explored that government economic support and debt/contract relief for poor families is substantially important in the fight against COVID-19. The study also explored that closing schools and workplaces, restrictions on gatherings, cancellation of public events, stay at home, closing public transport, restrictions on domestic and international travel are necessary to reduce the spread of COVID-19. Finally, this study explored that public awareness campaigns, testing policy and social distancing significantly decrease the spread of COVID-19. Policy implications such as economic support from the government to help poor families, closing schools and public gatherings during the pandemic, public awareness among the masses, and testing policies must be adopted to reduce the spread of COVID-19. Moreover, the reduction in mortality shows that immunization could be a possible new strategy to combat COVID-19, but the factors responsible for the acceptability of the vaccine must be addressed immediately through public health policies.
Indexed as
Identifiers
What OpenQuestion holds
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.