Related Experiment Video
Updated: Aug 26, 2025
03:53
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
1.3K
Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case
Iqra Sardar1, Muhammad Azeem Akbar2, Víctor Leiva3
1Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan.
Stochastic Environmental Research and Risk Assessment : Research Journal
|October 11, 2022
Summary
The Autoregressive Integrated Moving Average (ARIMA) model effectively forecasts COVID-19 confirmed cases in most SAARC nations. Machine learning models were compared, with ARIMA showing superiority for countries like India and Bangladesh.
Area of Science:
- Epidemiology
- Data Science
- Public Health
Background:
- The COVID-19 pandemic has caused global suffering, necessitating accurate forecasting of confirmed cases.
- Machine learning (ML) offers powerful tools for analyzing complex real-world problems like disease spread.
Purpose of the Study:
- To develop and evaluate an autoregressive modeling framework using ML and statistical methods.
- To predict confirmed COVID-19 cases in South Asian Association for Regional Cooperation (SAARC) countries.
Main Methods:
- Applied various forecasting models: Autoregressive Integrated Moving Average (ARIMA), Prophet, Extreme Gradient Boosting, Generalized Linear Model Elastic Net (GLMNet), and Random Forest.
- Utilized COVID-19 data from SAARC countries for model training and validation.
- Compared model performance using selection criteria and evaluation metrics.
Main Results:
- The ARIMA model demonstrated suitability for forecasting confirmed COVID-19 cases across most SAARC countries.
- ARIMA outperformed other models for Afghanistan, Bangladesh, India, Maldives, and Sri Lanka.
- Prophet was suitable for Bhutan, and GLMNet was accurate for Nepal and Pakistan.
- Random Forest showed poor fit and was excluded from forecasting.
Conclusions:
- The ARIMA model is an ideal choice for forecasting confirmed COVID-19 infections in several SAARC nations.
- Specific ML and time-series models show country-specific strengths in COVID-19 case prediction.
- This study provides valuable insights for public health strategies and resource allocation during pandemics.
Related Concept Videos
01:18Steps in Outbreak Investigation
171
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
171
01:25Statistical Methods for Analyzing Epidemiological Data
489
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
489
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:26Principles of Disease Surveillance
171
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
171
01:16What is Climate?
18.8K
Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
18.8K
01:05Single Nucleotide Polymorphisms-SNPs
15.6K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.6K