Pandemic modeling with the game of life

Abstract

The generalized semi-classical game of life (gSCGOL) is a cellular automaton in which qubits evolve through repeated applications of birth, death, and survival operators. gSCGOL is used to model human–virus interactions during the COVID-19 pandemic by using cell “liveness” to be equivalent to the human population that is vulnerable to the disease. A match to the United Kingdom (UK) daily 7-day average COVID-19 deaths was achieved by daily changes to a parameter V representing the vulnerability of the population to the virus. The model predicts that the first COVID-19 virus entered the UK on December 22, 2019 with an uncertainty of ±1 days. The changes in the vulnerability V elucidate the human response to the unfolding pandemic during 2020, quantifying the impact of the lockdown, the relaxation of restrictions, and the opening and closing of schools. The model predicts that starting the first UK national lockdown one week earlier would have led to 40% fewer COVID-19-related deaths, equivalent to 17 000 lives.