Related Experiment Video
Updated: Oct 2, 2025
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.8K
Forecasting the daily deaths caused by COVID-19 using ARIMA model
Amber Asghar1, Hina Khan2, Nida Razzak1
1Department of Statistics, Virtual University of Pakistan, Lahore, Pakistan.
Pakistan Journal of Pharmaceutical Sciences
|February 28, 2022
Summary
This study analyzed COVID-19 daily deaths and cases in ten countries using linear regression and ARIMA models. Results show varying correlations between cases and deaths, with some nations indicating controlled spread and others an upward trend.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- The Coronavirus disease (COVID-19) pandemic presents a significant global health crisis.
- Understanding the dynamics of daily deaths and new cases is crucial for pandemic management.
Purpose of the Study:
- To analyze the relationship between daily new cases and daily deaths caused by COVID-19 across ten countries.
- To predict potential daily COVID-19 deaths for the next three months using time-series forecasting.
Main Methods:
- Linear regression models were fitted to establish correlations between daily cases and deaths.
- Autoregressive Integrated Moving Average (ARIMA) models were employed for short-term death predictions.
Main Results:
- Correlation coefficients (R-squared) varied, with Pakistan (0.662) and Turkey (0.702) showing substantial explanation of deaths by cases. Iran showed a lower correlation (0.24).
- India reported the highest number of deaths, while the UAE reported the lowest.
- China, UAE, and Australia demonstrated signs of controlled pandemic spread. Pakistan, Iran, Germany, and Italy indicated an upward trend in disease spread, potentially correlating with increased mortality.
Conclusions:
- The relationship between COVID-19 cases and deaths varies significantly by country.
- Predictive models suggest differing pandemic trajectories, with some countries requiring intensified control measures.
More Related Videos
Related Concept Videos
01:18Steps in Outbreak Investigation
243
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:
243
01:11Residuals and Least-Squares Property
8.0K
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...
8.0K
01:25Statistical Methods for Analyzing Epidemiological Data
588
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:
588
01:14Parametric Survival Analysis: Weibull and Exponential Methods
687
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
687
01:20Actuarial Approach
143
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
143
01:03Prediction Intervals
2.4K
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.
2.4K