ArticleJournal of infection and public health2021
COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey.
Article in Journal of infection and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Topological analysis of COVID-19 wave patterns and policy responses in Europe.Frontiers in public health · 2025Article
- Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models.BMC public health · 2023Article
- Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.Infectious Disease Modelling · 2023Article
- Data Mining Based Techniques for Covid-19 Predictions.Procedia computer science · 2023Article
- A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images.Annals of operations research · 2022Article
- Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels.Environmental science and pollution research international · 2022Article
- Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy.The European journal of health economics : HEPAC : health economics in prevention and care · 2022Article
- Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach.International journal of environmental research and public health · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A local outbreak of unknown pneumonia was detected in Wuhan (Hubei, China) in December 2019. It is determined to be caused by a severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) and called COVID-19 by scientists. The outbreak has since spread all over the world with a total of 120,815,512 cases and 2,673,308 deaths as of 16 March 2021. The health systems in the world collapsed in many countries due to the pandemic and many countries were negatively affected in the social life. In such situations, it is very important to predict the load that will occur in the health system of a country. In this study, the COVID-19 prevalence of Turkey is inspected. The infected cases, the number of deaths, and the recovered cases are predicted with Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) in Turkey. The techniques are compared in terms of correlation coefficient and mean square error (MSE). The results showed that the used techniques used are very successful in the estimation of prevalence in Turkey.
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.