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A reasonable prediction of infectious diseases transmission process underdifferent disease control strategies is an important reference point for policymakers. Here we established a dynamic transmission model via Python andrealized comprehensive regulation of disease control measures. We classifiedgovernment interventions into three categories and introduced three parametersas descriptions for the key points in disease control, these beingintraregional growth rate, interregional communication rate, and detection rateof infectors. Our simulation predicts the infection by COVID-19 in the UK wouldbe out of control in 73 days without any interventions; at the same time, herdimmunity acquisition will begin from the epicentre. After we introducedgovernment interventions, single intervention is effective in disease controlbut at huge expense while combined interventions would be more efficient, amongwhich, enhancing detection number is crucial in control strategy of COVID-19.In addition, we calculated requirements for the most effective vaccinationstrategy based on infection number in real situation. Our model was programmedwith iterative algorithms, and visualized via cellular automata, it can beapplied to similar epidemics in other regions if the basic parameters areinputted, and is able to synthetically mimick the effect of multiple factors ininfectious disease control.

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