Related Experiment Video
Updated: Jun 10, 2025
07:38
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
Published on: April 9, 2017
10.0K
A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo
Touria Jdid1, Mohammed Benbrahim1, Mohammed Nabil Kabbaj1
1Laboratory of Engineering, Modeling and Systems Analysis (LIMAS), Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Heliyon
|October 11, 2024
Summary
This study developed a COVID-19 compartmental model incorporating vaccination. The model demonstrated that vaccination significantly reduces infection rates and epidemic peaks, highlighting its importance in disease control.
Area of Science:
- Epidemiology
- Mathematical Biology
- Infectious Disease Modeling
Background:
- Compartmental models are effective for analyzing infectious disease dynamics, including COVID-19.
- Integrating vaccination strategies into these models is crucial for understanding disease control.
- Previous models have shown success, but a detailed vaccination-focused approach is needed.
Purpose of the Study:
- To develop and analyze a vaccination-based compartmental model for COVID-19 transmission.
- To assess the impact of vaccination rates and efficacy on disease spread in Tennessee, USA.
- To estimate key epidemiological parameters and reproduction numbers.
Main Methods:
- A compartmental model incorporating COVID-19 infection stages and vaccination was developed.
- Bayesian inference using the Hamiltonian Monte Carlo (HMC) algorithm was employed for model fitting.
- The model was fitted to daily COVID-19 case data from Tennessee between June 4 and November 26, 2021.
Main Results:
- The basic reproduction number (R0) was estimated at 1.5 without vaccination.
- Vaccination rates were estimated for Pfizer, Moderna, and Janssen vaccines.
- Simulations showed significant reductions in epidemic peaks with vaccination, with a 95% efficacy vaccine halving infections.
Conclusions:
- Vaccination is a critical factor in reducing COVID-19 transmission and epidemic peaks.
- The developed model provides a robust framework for evaluating vaccination strategies.
- Model accuracy improves with increased observational data for training.
Related Concept Videos
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
98
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
98
01:18Steps in Outbreak Investigation
108
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:
108
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
31
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
31
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
44
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
44
01:25Statistical Methods for Analyzing Epidemiological Data
312
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:
312
01:21Causality in Epidemiology
324
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
324