STOCHASTIC MODELLING OF THE IMPACT OF ASYMPTOMATIC INFECTION AND THE USE OF NON-COVID-19 DRUGS ON COVID-19 DYNAMICS

Abstract

We formulate a stochastic epidemic model for COVID-19 dynamics which takes into ac count asymptomatic infection and the use of non-COVID-19 drugs. The stochastic model, a continuous-time Markov chain (CTMC), is based on the assumptions of its correspond ing deterministic model. Our basic reproduction number is in agreement with the basic reproduction numbers for COVID-19 across Africa. The disease dynamics of both models are compared to ascertain the effect of demographic stochasticity on COVID-19 transmis sion dynamics. Analytical and numerical results show differences in model predictions and asymptotic dynamics between stochastic and deterministic models that are crucial for the prevention of disease outbreaks. The probability of disease extinction computed from the branching process is shown to be in good agreement with the probability approximated from numerical simulations. It is found that there is a high probability of disease extinc tion if the disease emerges from symptomatic-infectious individuals and the probability of extinction is smallest if the disease emanates from asymptomatic-infectious individu als. Also, finite-time to disease extinction is estimated using 10,000 sample paths and it is shown that the epidemic duration is shortest if the disease is introduced by symptomatic infectious individuals and is longest if the disease emanates from all the 3 infectious groups at the onset of the epidemic process. Finally, numerical simulations illustrate that the probability of disease extinction is generally increased with an increase in the use of non COVID-19 drugs to treat COVID-19 symptoms in patients.