Related Experiment Video
Updated: Dec 21, 2025
03:53
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
1.7K
Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving
Ram Kumar Singh1, Meenu Rani2, Akshaya Srikanth Bhagavathula3
1Department of Natural Resources, TERI School of Advanced Studies, New Delhi, India.
JMIR Public Health and Surveillance
|May 12, 2020
Summary
The COVID-19 pandemic
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- The COVID-19 pandemic has impacted over 200 countries globally.
- The first cases were identified in Wuhan, China, in December 2019.
Purpose of the Study:
- To identify the top 15 countries most affected by COVID-19.
- To spatially map confirmed cases and compare key metrics.
- To predict COVID-19 spread using an advanced ARIMA model.
Main Methods:
- Spatial mapping of confirmed COVID-19 cases.
- Comparative analysis of confirmed cases, deaths, and recoveries in top 15 countries.
- Application of an advanced Autoregressive Integrated Moving Average (ARIMA) model for future trend prediction.
Main Results:
- Comparison of cumulative and predicted COVID-19 data for top 15 countries.
- Identification of countries with fast disease spread (e.g., US, UK, Turkey) and high/low recovery/death rates.
- ARIMA model predicted confirmed cases, deaths, and recoveries from April 24 to July 7, 2020, with acceptable validation results.
Conclusions:
- COVID-19 cases, deaths, and recoveries are predicted to double in most top countries except China, Switzerland, and Germany.
- Death and recovery rates are expected to rise faster than confirmed cases in the next two months.
- Mortality rates are projected to be highest in the US, Spain, and Italy, but recovery numbers show promise by July 7, 2020.
Related Concept Videos
01:18Steps in Outbreak Investigation
418
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:
418
00:52Pareto 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...
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
7.5K
01:11Residuals and Least-Squares Property
8.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.8K
01:03Prediction Intervals
3.0K
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.
3.0K
01:25Statistical Methods for Analyzing Epidemiological Data
814
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:
814
01:09Types of Skewness
16.9K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
16.9K