ArticlePattern recognition letters2021
Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics.
Article in Pattern recognition letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled 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.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-driven strategies for model-informed decision-making during the COVID-19 pandemic: a systematic review.BMJ open · 2026Pooled it
- Global Spatiotemporal Dynamics of African Swine Fever: An Integrated Multi-Scale Spatial and Time-Series Analysis.Viruses · 2026Article
- Article
- Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet.Scientific reports · 2024Article
- Article
- A glimpse into the future: modelling global prevalence of hypertension.BMC public health · 2023Article
- A new time-varying coefficient regression approach for analyzing infectious disease data.Scientific reports · 2023Article
- The Impact of Telemedicine on Patients with Hidradenitis Suppurativa in the COVID-19 Era.Healthcare (Basel, Switzerland) · 2023Article
- Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic.Decision support systems · 2023Article
- Digital Eye Strain among Peruvian Nursing Students: Prevalence and Associated Factors.International journal of environmental research and public health · 2023Article
- A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia.Open life sciences · 2023Article
- Vaccine hesitancy in the post-vaccination COVID-19 era: a machine learning and statistical analysis driven study.Evolutionary intelligence · 2023Article
- Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.Archives of disease in childhood · 2022Article
- Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia.Healthcare (Basel, Switzerland) · 2022Article
- Prediction and forecasting of worldwide corona virus (COVID-19) outbreak using time series and machine learning.Concurrency and computation : practice & experience · 2022Article
- ANN-Based traffic volume prediction models in response to COVID-19 imposed measures.Sustainable cities and society · 2022Article
- A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy.SN computer science · 2022Article
- The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China.Frontiers in public health · 2022Article
- A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan.Computational intelligence and neuroscience · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure.
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.