Persistent Correlation in Cellular Noise Determines Longevity of Viral Infections

The slowly decaying viral dynamics, even after 2–3
weeks
from diagnosis, is one of the characteristics of COVID-19 infection
that is still unexplored in theoretical and experimental studies.
This long-lived characteristic of viral infections in the framework
of inherent variations or noise present at the cellular level is often
overlooked. Therefore, in this work, we aim to understand the effect
of these variations by proposing a stochastic non-Markovian model
that not only captures the coupled dynamics between the immune cells
and the virus but also enables the study of the effect of fluctuations.
Numerical simulations of our model reveal that the long-range temporal
correlations in fluctuations dictate the long-lived dynamics of a
viral infection and, in turn, also affect the rates of immune response.
Furthermore, predictions of our model system are in agreement with
the experimental viral load data of COVID-19 patients from various
countries.