Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework

• Published in: Natural computing 2022-09, Vol.21 (3), p.449-461
• Main Authors: Lima, Isaías, Balbi, Pedro Paulo
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Cellular automata; Complex Systems; Computer Science; Coronaviruses; COVID-19; Estimates; Evolutionary Biology; Immunity; Immunization; Impact analysis; Infectious diseases; Processor Architectures; Severe acute respiratory syndrome coronavirus 2; Theory of Computation; Transition probabilities; Uncertainty; Viral diseases
• ISSN: 1567-7818; 1572-9796
• DOI: 10.1007/s11047-022-09893-3

In the context of the propagation of infectious diseases, when a sufficient degree of immunisation is achieved within a population, the spread of the disease is ended or significantly decreased, leading to collective immunity, meaning the indirect protection given by immune individuals to susceptible individuals. Here we describe the estimates of the collective immunity to COVID-19 from a stochastic cellular automaton based model designed to emulate the spread of SARS-CoV-2 in a population of static individuals interacting only via a Moore neighbourhood of radius one, with a view to analyze the impact of initially immune individuals on the dynamics of COVID-19. This impact was measured by comparing a progression of initial immunity ratio—the percentage of immunised individuals before patient zero starts infecting its neighbourhood—from 0 to 95% of the initial population, with the number of susceptible individuals not contaminated, the peak value of active cases, the total number of deaths and the emulated pandemic duration in days. The influence of this range of immunities over the model was tested with different parameterisations regarding the uncertainties involved in the model such as the durations of the cellular automaton states, the contamination contributions of each state and the state transition probabilities. A collective immunity threshold of 55 % ± 2.5 % on average was obtained from this procedure, under four distinct parameterisations, which is in tune with the estimates of the currently available medical literature, even increasing the uncertainty of the input parameters.