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

Pie Chart01:04

Pie Chart

15.5K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
15.5K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

770
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:
770
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.1K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.1K
Pareto Chart00:52

Pareto Chart

7.5K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
7.5K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

436
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
436
Prevalence and Incidence01:08

Prevalence and Incidence

1.3K
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
1.3K

You might also read

Related Articles

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

Sort by
Same author

A New Ridge-Type Estimator for the Gamma Regression Model.

Scientifica·2021
See all related articles

Related Experiment Video

Updated: Dec 7, 2025

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.8K

Modeling Palestinian COVID-19 Cumulative Confirmed Cases: A Comparative Study.

Issam Dawoud1

  • 1Department of Mathematics, Al-Aqsa University, Gaza, Palestine.

Infectious Disease Modelling
|September 28, 2020
PubMed
Summary

This study models COVID-19 cases in Palestine using statistical approaches. The 5th Exponential Weighted Moving Average-ARIMA model is recommended for forecasting alarming future case numbers.

Keywords:
ARIMA modelsCOVID-19ForecastingMoving average modelsPandemic

More Related Videos

Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic
09:03

Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic

Published on: November 7, 2020

5.3K
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

1.6K

Related Experiment Videos

Last Updated: Dec 7, 2025

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.8K
Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic
09:03

Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic

Published on: November 7, 2020

5.3K
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

1.6K

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • The COVID-19 pandemic poses a significant global threat.
  • Palestine reported 26,764 cumulative confirmed cases by August 27, 2020.
  • Accurate forecasting is crucial for public health interventions.

Purpose of the Study:

  • To model COVID-19 cumulative confirmed cases in Palestine.
  • To compare the effectiveness of Autoregressive Integrated Moving Average (ARIMA) and k-th Moving Averages-ARIMA models.
  • To identify the optimal model for forecasting future COVID-19 cases in Palestine.

Main Methods:

  • Utilized time-series data from the World Health Organization (WHO) spanning 176 days (March 5 to August 27, 2020).
  • Applied Autoregressive Integrated Moving Average (ARIMA) modeling.
  • Employed k-th Moving Averages-ARIMA models, specifically the 5th Exponential Weighted Moving Average-ARIMA.

Main Results:

  • Identified ARIMA (1,2,4) and 5th Exponential Weighted Moving Average-ARIMA (2,2,3) as the best performing models.
  • The 5th Exponential Weighted Moving Average-ARIMA (2,2,3) model demonstrated superior performance for forecasting.
  • Forecasted values indicate an alarming trend in cumulative confirmed cases.

Conclusions:

  • The 5th Exponential Weighted Moving Average-ARIMA (2,2,3) model is recommended for forecasting COVID-19 cases in Palestine.
  • The alarming forecast necessitates a review of current public health activities and interventions.
  • Robust measures are needed to mitigate future challenges posed by the pandemic.