Related Experiment Video
Updated: Jul 8, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.8K
Forecasting COVID19 Reliability of the Countries by Using Non-Homogeneous Poisson Process Models.
Nevin Guler Dincer1, Serdar Demir1, Muhammet Oğuzhan Yalçin1
1Faculty of Science, Department of Statistics, University of Muğla Sıtkı Koçman, Muğla, Turkey.
New Generation Computing
|July 11, 2022
Summary
This study models COVID-19 reliability using Non-Homogenous Poisson Process models. S-shaped models best fit most countries, predicting outbreak continuation or emergence and assessing national COVID-19 reliability.
Area of Science:
- Epidemiology
- Statistical Modeling
- Public Health
Background:
- Reliability quantifies a system's ability to function without failure over time.
- Understanding COVID-19's reliability is crucial for predicting outbreak trajectories and informing public health strategies.
- Previous studies have not focused on forecasting country-specific COVID-19 reliability using advanced statistical models.
Purpose of the Study:
- To predict and forecast country-specific COVID-19 reliability.
- To model cumulative COVID-19 cases using various intensity functions and Non-Homogeous Poisson Process (NHPP) models.
- To identify the best-fit model for each country based on established statistical criteria.
Main Methods:
- Utilized eight Non-Homogeous Poisson Process (NHPP) models to analyze cumulative COVID-19 case data.
- Modeled data using intensity functions with geometric, exponential, Weibull, and gamma shapes.
- Selected best-fit (BF) models using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and Theil Statistics (TS).
Main Results:
- S-shaped models demonstrated superior fit for 56 out of 70 countries analyzed.
- Outbreak continuation was predicted for 43 countries, with potential new outbreaks in 27 countries.
- As of August 11, 2021, 50 countries had COVID-19 reliability below 75%, 9 between 75%-90%, and 11 above 90%.
Conclusions:
- S-shaped NHPP models are effective for forecasting COVID-19 reliability and outbreak dynamics.
- The findings highlight significant variations in national COVID-19 reliability, necessitating tailored public health interventions.
- This study provides a novel framework for assessing and predicting infectious disease reliability on a global scale.
Related Concept Videos
01:09Poisson Probability Distribution
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
The...
01:12Analysis of Population Pharmacokinetic Data
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
01:25Statistical Methods for Analyzing Epidemiological Data
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:
01:18Steps in Outbreak Investigation
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:
01:14Parametric Survival Analysis: Weibull and Exponential Methods
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...