ArticleIranian journal of public health2020
Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models.
Article in Iranian journal of public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Comparison of growth, time series, and hybrid models for HIV infectious disease incidence in adults in Saudi Arabia.BMC infectious diseases · 2026Article
- From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings.PLOS digital health · 2024Article
- Using meta-learning to recommend an appropriate time-series forecasting model.BMC public health · 2024Article
- Article
- Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention.Artificial intelligence in medicine · 2022Article
- Explanation of COVID-19 Mortality Using Artificial Neural Network Based on Underlying and Laboratory Risk Factors in Ilam, Iran.Archives of Razi Institute · 2022Article
- Optimizing human-centered AI for healthcare in the Global South.Patterns (New York, N.Y.) · 2022Review
- Investigation of Factors Affecting COVID-19 and Sixth Wave Management Using a System Dynamics Approach.Journal of healthcare engineering · 2022Article
- A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting.Neural computing & applications · 2022Article
- Developing an artificial neural network for detecting COVID-19 disease.Journal of education and health promotion · 2022Article
- How do the smart travel ban policy and intercity travel pattern affect COVID-19 trends? Lessons learned from Iran.PloS one · 2022Article
- Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.Heliyon · 2021Article
- Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India.Journal of biomedical informatics · 2021Article
- On the accuracy of ARIMA based prediction of COVID-19 spread.Results in physics · 2021Article
- COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey.Journal of infection and public health · 2021Article
- Improving the performance of deep learning models using statistical features: The case study of COVID-19 forecasting.Mathematical methods in the applied sciences · 2021Article
- Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods.Biomedical signal processing and control · 2021Article
- Forecasting COVID-19 daily cases using phone call data.Applied soft computing · 2021Article
- A review on COVID-19 forecasting models.Neural computing & applications · 2021Review
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
backgroundThe outbreak of COVID-19 is rapidly spreading around the world and became a pandemic disease. For help to better planning of interventions, this study was conducted to forecast the number of daily new infected cases with COVID-19 for next thirty days in Iran.
methodsThe information of observed Iranian new cases from 19th Feb to 30th Mar 2020 was used to predict the number of patients until 29
resultsBoth algorithms forecasted an exponential increase in number of newly infected patients. If the spreading pattern continues the same as before, the number of daily new cases would be 7872 and 9558 by 29
conclusionCOVID-19 is contagious disease, and has infected many people in Iran. Our results are an alarm for health policy planners and decision-makers, to make timely decisions, control the disease and provide the equipment needed.
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.