Related Experiment Video
Updated: Nov 9, 2025
08:23
Finite Element Modelling of a Cellular Electric Microenvironment
Published on: May 18, 2021
3.7K
Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19
Raiha Browning1,2, Deborah Sulem3, Kerrie Mengersen1,2
1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Plos One
|April 9, 2021
Summary
Discrete-time Hawkes processes model complex phenomena like COVID-19 dynamics. This self-exciting model offers insights into epidemic phases and mortality counts across diverse countries.
Area of Science:
- Epidemiology and statistical modeling
- Complex systems analysis
Background:
- Hawkes processes are self-exciting models used in various fields.
- Traditional Hawkes processes operate in continuous time.
- Complex phenomena can be modeled using simple self-exciting processes.
Purpose of the Study:
- To introduce and evaluate a discrete-time variant of Hawkes processes.
- To apply discrete-time Hawkes processes to model COVID-19 mortality data.
- To gain alternative insights into epidemic dynamics compared to existing models.
Main Methods:
- Development of a discrete-time Hawkes process model.
- Application to daily COVID-19 mortality counts across multiple countries.
- Analysis of different epidemic phases, including exponential growth and decline.
Main Results:
- The discrete-time Hawkes process accurately captures COVID-19 mortality dynamics.
- The model provides insights into distinct phases of the epidemic.
- The model's utility is demonstrated across diverse countries with unique epidemic characteristics.
Conclusions:
- Discrete-time Hawkes processes offer a parsimonious yet effective approach to modeling complex temporal data.
- This modeling approach can be applied beyond the COVID-19 pandemic to other complex phenomena.
- The model's simplicity aids in understanding processes with unknown underlying dynamics.
Related Concept Videos
01:18Steps in Outbreak Investigation
296
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:
296
02:33Exponential Equations for Modeling Growth
48
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
48
01:29Introduction to Exponential Functions
82
Exponential functions are fundamental in modeling dynamic processes where the rate of change is proportional to the current value. Defined by f(x) = bx, where b is a positive constant not equal to one, they form the basis for describing processes of growth and decay depending on whether the base b is greater than or less than one.Exponential models describe situations where change occurs at a rate proportional to the current amount. These include phenomena such as bacterial proliferation,...
82
01:21Causality in Epidemiology
1.2K
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...
1.2K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
137
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...
137
01:21Mechanistic Models: Overview of Compartment Models
224
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
224