A minimal model for multigroup adaptive SIS epidemics
Massimo A Achterberg1, Mattia Sensi2,3, Sara Sottile4
1Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands.
Abstract:
We propose a generalization of the adaptive N-Intertwined Mean-Field Approximation (aNIMFA) model studied in Achterberg and Sensi [Nonlinear Dyn. 111, 12657-12670 (2023)] to a heterogeneous network of communities. In particular, the multigroup aNIMFA model describes the impact of both local and global disease awareness on the spread of a disease in a network. We obtain results on the existence and stability of the equilibria of the system, in terms of the basic reproduction number R0. Assuming individuals have no reason to decrease their contacts in the absence of disease, we show that the basic reproduction number R0 is equivalent to the basic reproduction number of the NIMFA model on static networks. Based on numerical simulations, we demonstrate that with just two communities periodic behavior can occur, which contrasts the case with only a single community, in which periodicity was ruled out analytically. We also find that breaking connections between communities is more fruitful compared to breaking connections within communities to reduce the disease outbreak on dense networks, but both strategies are viable in networks with fewer links. Finally, we emphasize that our method of modeling adaptivity is not limited to Susceptible-Infected-Susceptible models, but has huge potential to be applied in other compartmental models in epidemiology.
Insights
This study introduces a multigroup adaptive N-Intertwined Mean-Field Approximation (aNIMFA) model to analyze disease spread in networks. The model reveals that community connections impact disease dynamics, offering insights for public health interventions.
Area of Science:
- Epidemiology
- Mathematical Biology
- Network Science
Background:
- The adaptive N-Intertwined Mean-Field Approximation (aNIMFA) model analyzes disease spread in networks.
- Understanding disease dynamics in complex, heterogeneous networks is crucial for effective public health strategies.
Purpose of the Study:
- To generalize the aNIMFA model to heterogeneous networks of communities.
- To investigate the influence of local and global disease awareness on disease transmission.
- To analyze the existence and stability of system equilibria using the basic reproduction number (R0).
Main Methods:
- Developed a multigroup aNIMFA model for heterogeneous networks.
- Analyzed the existence and stability of equilibria based on R0.
- Conducted numerical simulations to explore disease dynamics and intervention strategies.
Main Results:
- The basic reproduction number (R0) in this model aligns with static network models when no disease-induced contact reduction occurs.
- Periodic disease behavior emerged in simulations with just two communities, unlike single-community models.
- Disrupting inter-community links proved more effective than intra-community link disruption for reducing outbreaks in dense networks.
Conclusions:
- The multigroup aNIMFA model provides a framework for understanding disease spread in complex networks with varying awareness levels.
- Network structure and community interactions significantly influence epidemic trajectories.
- The adaptive modeling approach has broad applicability to various epidemiological compartmental models beyond Susceptible-Infected-Susceptible (SIS).
More Related Videos
Related Concept Videos
01:18Steps in Outbreak Investigation
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
01:15Assumptions of Survival Analysis
01:09Censoring Survival Data
01:12Statistical Software for Data Analysis and Clinical Trials