ArticleComputers & electrical engineering : an international journal2021
An intelligent framework to predict socioeconomic impacts of COVID-19 and public sentiments.
Article in Computers & electrical engineering : an international journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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, 12 citations in OpenAlex.
- Survey on the Traditional Use of Medicinal Herbs in Haiti: A Study on Knowledge, Practices, and Efficacy Prevention.Plants (Basel, Switzerland) · 2024Article
- Opinion analysis and aspect understanding during covid-19 pandemic using BERT-Bi-LSTM ensemble method.Scientific reports · 2022Article
- Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network.Frontiers in public health · 2022Article
- On the development of an information system for monitoring user opinion and its role for the public.Journal of big data · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of novel coronavirus (COVID-19) has extremely shaken the whole world. COVID-19 has increased human distress, damaged the global economy, flipped the lives of many people around the world upside down, and has had a huge effect on the health, economic, environmental, and social sectors. This study aims to determine the social and economic trends in the outbreak of COVID-19 in Pakistan. Machine learning techniques learn patterns from historical data and make predictions on its basis. Furthermore, an online survey has been conducted to collect data and a total of 410 responses are collected. Machine learning techniques have been used to highlight the impact of COVID-19 on daily life. Moreover, sentiment analysis on tweets of Pakistan has also been performed to evaluate the positive and negative sentiments of the people on COVID-19.
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.