A two-level deterministic reasoning pattern to curb the spread of COVID-19 in Africa

Abstract The emergence of the pandemic disease COVID-19, which keeps many nations on their toes to find a the solution for a cure, needs a more predetermined approach by investigating the pattern and speed with which the disease is spread from one individual to another. The predetermining method can also be used to solve the future occurrence of such diseases. The predetermined approach is s good reasoning model for proactive measures. This study presents a two-level deterministic reasoning model to curb the spread of COVID-19 in some populated and economically optimistic African countries. A Petri net was used as a predetermining model to ensure proactive measures for present and future control of the spread of deadly diseases such as COVID-19. Data were collected from a reliable organization, and the result of the use the normal distribution model on these sets of data was fed into the Petri net to determine the severity and rate at which people contract disease before and during lockdown in selected countries. The results from this model proved that the number of cases of COVID-19 is not a function of the death rate in the selected countries; the discharge rate had a stronger effect on the COVID-19 cases. The results of the normal statistical distribution of various instances of COVID-19 were compared with those of the Petri net and proved that the hybrid deterministic model is viable for future use on any pandemic disease.