A prognostic dynamic model applicable to infectious diseases providing easily visualized guides -- A case study of COVID-19 in the UK

TL;DR

This paper presents a dynamic, visualizable model for infectious disease transmission, specifically COVID-19 in the UK, demonstrating the impact of various control strategies and vaccination requirements through simulation and cellular automata visualization.

Contribution

The study introduces a Python-based, adaptable transmission model with visual tools that evaluates multiple intervention strategies and vaccination planning for infectious diseases.

Findings

Without intervention, COVID-19 would be uncontrolled in 73 days in the UK.

Combined interventions are more effective than single measures.

Enhancing detection rates is crucial for controlling COVID-19.

Abstract

A reasonable prediction of infectious diseases transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. We classified government interventions into three categories and introduced three parameters as descriptions for the key points in disease control, these being intraregional growth rate, interregional communication rate, and detection rate of infectors. Our simulation predicts the infection by COVID-19 in the UK would be out of control in 73 days without any interventions; at the same time, herd immunity acquisition will begin from the epicentre. After we introduced government interventions, single intervention is effective in disease control but at huge expense while combined interventions would be more efficient, among which, enhancing detection number is crucial in control strategy of COVID-19. In addition, we calculated requirements for the most effective vaccination strategy based on infection number in real situation. Our model was programmed with iterative algorithms, and visualized via cellular automata, it can be applied to similar epidemics in other regions if the basic parameters are inputted, and is able to synthetically mimick the effect of multiple factors in infectious disease control.