ArticleBioMed research international2022
COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets.
Article in BioMed research international, 2022. 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 It has been retracted, and should not be counted.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 2 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
2 citing papers in PubMed.
- Geospatial analysis of contagious infection growth and cross-boundary transmission in non-vaccinated districts of North-East Indian states during the COVID-19 pandemic.The Lancet regional health. Southeast Asia · 2024Article
- Retracted: COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets.BioMed research international · 2023Article
Corrections and comments
- Retraction · 2023-12-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID-19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID-19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID-19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID-19 infection.
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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.