Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps 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
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical 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
Prediction Intervals01:03

Prediction 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. 
2.3K
Principles of Disease Surveillance01:26

Principles 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
What is Climate?01:16

What 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
Single Nucleotide Polymorphisms-SNPs01:05

Single 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

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A katE katG double-knockout E. coli strain eliminates the risk of catalase contamination in recombinant proteins.

Applied microbiology and biotechnology·2026
Same author

Forecasting tomato production in major Asian producers: a comparative study of ARIMA, exponential smoothing, score-driven models, and XGBoost.

Scientific reports·2026
Same author

Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.

Scientific reports·2026
Same author

Integrating machine learning and time-to-event models to explain and predict risk of hospitalization due to dengue in Colombia.

Scientific reports·2025
Same author

Climate modeling for South Asia: statistical and deep learning for rainfall and temperature prediction.

Scientific reports·2025
Same author

Asthma and severe acute respiratory infections: a stratified analysis of mortality patterns in Brazil.

BMC public health·2025

Related Experiment Video

Updated: Aug 26, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
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
PubMed
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.

Keywords:
Artificial intelligenceFacebook Prophet algorithmGLMR softwareSARS-CoV-2South Asian Association for Regional Cooperation countriesTime-series models

More Related Videos

Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP
05:34

Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP

Published on: September 8, 2023

869
Live Imaging and Quantification of Viral Infection in K18 hACE2 Transgenic Mice Using Reporter-Expressing Recombinant SARS-CoV-2
08:41

Live Imaging and Quantification of Viral Infection in K18 hACE2 Transgenic Mice Using Reporter-Expressing Recombinant SARS-CoV-2

Published on: November 5, 2021

2.9K

Related Experiment Videos

Last Updated: Aug 26, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.3K
Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP
05:34

Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP

Published on: September 8, 2023

869
Live Imaging and Quantification of Viral Infection in K18 hACE2 Transgenic Mice Using Reporter-Expressing Recombinant SARS-CoV-2
08:41

Live Imaging and Quantification of Viral Infection in K18 hACE2 Transgenic Mice Using Reporter-Expressing Recombinant SARS-CoV-2

Published on: November 5, 2021

2.9K

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.