Projected Spread Models

TL;DR

This paper introduces a projected disease spread model that incorporates both explicit and hidden factors, extending previous models for more accurate infectious disease prediction and control.

Contribution

It extends existing spread models to include hidden factors and provides spread rates for topological and random models, with illustrative examples and numerical results.

Findings

Extended spread model includes hidden and explicit factors

Derived spread rates for different network models

Numerical examples validate the theoretical framework

Abstract

We present a disease transmission model that considers both explicit and non-explicit factors. This approach is crucial for accurate prediction and control of infectious disease spread. In this paper, we extend the spread model from our previous works \cite{ban2021mathematical,ban2023randomspread, ban2023mathematical, ban2023spread} to a projected spread model that considers both hidden and explicit types. Additionally, we provide the spread rate for the projected spread model corresponding to the topological and random models. Furthermore, examples and numerical results are provided to illustrate the theory.