Integrating community level transmission geographical networks into a dynamical system for better epidemic control

Despite the widespread use of deterministic models in understanding and controlling epidemics, they are often criticized for their inability to provide timely practical solutions during rapid spread. Similarly, conventional stochastic and statistical models also have limitations in providing time-sensitive solutions. These models are useful for implementing policy measures when there is enough time to make changes. In this article, we propose a novel approach to address these limitations by introducing a graphical network model with time-sensitive data blending to enhance deterministic epidemic models like the SIR model. This innovative approach could be valuable for rapidly spreading epidemics, providing timely model-based solutions to control their spread. For the first time, this article introduces higher-dimensional transmission rate functions in the literature and methods to obtain such functions. AMS MSC 2020 classifications: 92D30; 62P10; 65T60.